In intelligent education, personalized mathematics question generation aims to produce mathematics questions that satisfy educational requirements while supporting adaptive assessment and learning. Existing LLM-based single-agent and multi-agent methods improve generation flexibility, but they still tend to rely on aggregated feedback or model randomness, making it difficult to jointly ensure dimension-wise objective alignment and controllable diversity. To address these challenges, we propose EduAgentQG, a multi-agent collaborative framework for personalized mathematics question generation with explicit diversity and objective-aware evaluation. EduAgentQG organizes question generation as a closed-loop process of planning, writing, evaluation, refinement, and checking: structured generation plans and multiple generation directions guide candidate generation, while fine-grained evaluation verifies logical correctness, solvability, and objective alignment in knowledge concepts, difficulty, grade level, and core competencies. We first construct a mathematics question generation benchmark containing 10,273 questions across Grades 1–9, covering 634 knowledge concepts, 16 core competencies, and three difficulty levels; for evaluation, it is organized into two subsets: MathChoice, with 489 educational objectives for multiple-choice question generation, and MathBlank, with 500 educational objectives for fill-in-the-blank question generation. Experiments using Gemini-2.5-Flash, GPT-4o-mini, and Qwen2.5-72B-Instruct show that EduAgentQG consistently outperforms COT, COTN, ReAct, and EQPR in diversity, Objective Consistency, and Win Rate. On Gemini-2.5-Flash, EduAgentQG improves Win Rate by 3.90 and 4.75 percentage points on MathChoice and MathBlank, respectively, over the strongest baseline, demonstrating its ability to generate diverse, solvable, and educationally aligned personalized mathematics questions with stronger controllability.
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.
Mycobacterium tuberculosis remains the leading cause of death from a single infectious pathogen globally despite decades of effective chemotherapy. In 2024, an estimated 10·7 million people developed tuberculosis, including approximately 620 000 people living with HIV (PLHIV), and tuberculosis caused an estimated 1·23 million deaths overall, including approximately 150 000 deaths among PLHIV. Men accounted for more than half of the cases, and children represented a substantial burden, reflecting ongoing transmission and diagnostic gaps. Approximately a quarter of the world's population has been infected with M tuberculosis, with immunological evidence of previous or current infection. This population includes groups at increased risk of progression to tuberculosis disease, particularly those with recent infection, HIV, undernutrition, young age, or other clinical and social vulnerabilities. Following 3 years of COVID-19-related setbacks, global tuberculosis incidence declined modestly (1%) from 2023 to 2024 but remains higher than in 2020 and far off-track to meet the 2025 WHO End TB Strategy milestones. Case detection improved to 8·3 million notifications (78% of estimated incident cases), supported by expanded molecular diagnostics; however, prevalence surveys continue to reveal substantial proportions of bacteriologically confirmed but asymptomatic tuberculosis, highlighting persistent transmission and missed diagnoses. 30 high-burden countries accounted for 87% of cases, led by India, Indonesia, the Philippines, China, Pakistan, Nigeria, the Democratic Republic of the Congo, and Bangladesh. M tuberculosis-HIV co-infection remains a major driver of mortality in sub-Saharan Africa. Drug-resistant tuberculosis threatens progress: of 390 000 estimated multidrug-resistant or rifampicin-resistant tuberculosis cases in 2024, only 42% initiated treatment, although treatment success improved to 71%. Tuberculosis-preventive treatment reached 5·3 million people, including 58% of PLHIV and 25% of eligible household contacts, well below global targets. Persistent undernutrition, poverty, HIV, diabetes, smoking, alcohol use, air pollution, migration, and conflict continue to shape tuberculosis epidemiology. With financing at only 27% of global targets, accelerated prevention, proactive case finding, social protection, and sustained political commitment are essential to eliminate tuberculosis.
The Poisson or negative binomial item count technique was developed to avoid the ceiling effect by replacing the list of non-sensitive items with a single non-sensitive question whose responses follow a Poisson or negative binomial distribution. However, identifying an appropriate "non-sensitive" question is often non-trivial. This motivates us to consider Benford's law as the basis for constructing the non-sensitive question. Building on this idea, we propose a new survey methodology that leverages Benford's law for estimating the prevalence of a sensitive attribute. We present the survey design, parameter estimation procedures, hypothesis testing strategies, and confidence interval construction for the prevalence parameter. Simulation studies are conducted to evaluate the performance of the proposed method. Finally, we illustrate the methodology using a real dataset on premarital sexual behaviour in a nationwide population survey.
The extracellular matrix (ECM) undergoes extensive architectural remodeling during tumor progression, generating nanoscale alignment cues that profoundly influence behaviors of immune cells. However, whether ECM nanoarchitecture directly programs their immune secretory functions remains poorly defined. Here, we develop a magnetically switchable hydrogel that enables reversible, on-demand modulation of nanoscale ligand alignment without altering bulk stiffness or composition. Using this platform, we uncover a mechanotransduction pathway in tumor-associated macrophages (TAMs) whereby matrix nanoalignment enhances integrin β1-dependent mechanosensing and transient receptor potential cation channel subfamily V member 2 (TRPV2)-dependent Ca2+ signaling, thereby promoting phosphoinositide 3-kinase (PI3K)–Ras-related protein 11a (Rab11a)-associated small extracellular vesicle (sEV) release by approximately tenfold. Beyond increasing vesicle output, PI3K–mechanistic target of rapamycin complex 2-associated signaling altered the particle-normalized cargo profile of sEVs, with preferential enrichment of immunoregulatory molecules, including M2-promoting cytokines and miR-181a-5p. These sEVs reprogram macrophages toward an immunosuppressive M2-like phenotype, suppress cytotoxic CD8+ T-cell infiltration, and accelerate melanoma progression in vivo. Importantly, selective disruption of the TRPV2–Rab11a axis or pharmacological attenuation of ECM remodeling restores antitumor immunity and restricts tumor growth. Together, our findings demonstrate that ECM nanoarchitecture is sufficient to regulate macrophage sEV programming in a defined matrix environment and identify TRPV2-associated mechanotransduction as a potential therapeutic vulnerability in matrix-remodeled tumors.
