Search Results (618)

The relationship between economic development and residents’ perceived urban well-being remains an important question in urban research. This study examines whether the relationship between GDP and city-level satisfaction exhibits non-linear patterns or plateau effects. Using the 2024 nationwide Urban Physical Examination (UPE) resident survey in China, this study assesses how city economic level relates to perceived urban well-being, proxied by city-level overall satisfaction. The survey was conducted in April–June 2024 in the main urban districts of 47 cities, using 499,500 valid questionnaires. We aggregate satisfaction to the city level, match it with GDP and key city characteristics, and estimate the GDP–satisfaction association using restricted cubic splines (RCS) to test for potential non-linearity. Across unadjusted and covariate-adjusted models (accounting for population scale and density, industrial structure, fiscal capacity, and regional effects), results show a robust positive association between economic level and satisfaction, while nested-model tests provide no evidence that spline terms improve fit over a linear specification within the observed GDP range. Substantial dispersion around the fitted curve indicates that GDP is an enabling capacity rather than a sufficient condition, pointing to cross-city differences in how effectively resources are converted into lived urban quality. We propose using GDP-adjusted satisfaction benchmarking within the UPE cycle to identify underperforming cities and prioritize targeted governance and renewal actions. Full article

Generative AI enables rapid visualization of sustainable urban design scenarios, yet the question of whether these outputs encode sustainability as operable spatial logic, rather than merely depicting it as a visual impression, remains underexplored. This study proposes a two-level assessment framework that scores the same sustainability dimensions at both the visual-representation level and the spatial-logic level, treating the systematic decoupling between the two as a form of visual greenwashing: system-induced representational distortion rather than deliberate misrepresentation. Using AI-workflow reports from two site-based urban design studios (47 students, 12 teams, 36 coded scenes), the framework integrates rubric-based scoring with qualitative process tracing of breakdown–repair logs. Results show that image-level scores consistently outperform logic-level scores across all five dimensions, with the gap most severe in mobility hierarchy and walkability and smallest in green/blue infrastructure. Case analysis reveals that breakdowns arise from failures in program encoding, urban-scale coherence, functional-boundary demarcation, and relational-condition matching, and that students deploy multi-stage repair pipelines, including prompt restructuring, tool switching, reference injection, and external-source compositing, to re-inject collapsed spatial logic. These findings reframe AI-assisted urban design as repair-centered workmanship rather than automated production. The study proposes three guardrails to prevent visual sustainability from substituting for spatial-logic sustainability: image–logic paired submission, design audit trail formalization, and gap-based red-flag review. Full article

Wildfires cause major damage, and their accurate detection is crucial. A common approach to near-real-time detection uses Geostationary (GEO) satellite algorithms. A standard scheme for evaluating the accuracy of a GEO-based algorithm is to compare its detections with higher-resolution Low Earth Orbit (LEO) images, considering the latter as ground truth. The primary objective of this study is to quantify the prevalence of GOES ABI/VIIRS fire detection misalignments and assess their impact on the accuracy evaluation of the GOES Fire Detection and Characterization (FDC) product. Thus, the key question is how this evaluation should be performed. To this end, a large dataset of matching FDC/VIIRS fire detections across Western U.S., Amazonas, and Patagonia was constructed. Our finding is that for nearly 12% of fire events, there are spatial misalignments between FDC and VIIRS detections. Next, we show that using VIIRS as ground truth without considering these misalignments yields highly biased estimates. This affects the evaluation of the FDC product detection capabilities. Finally, we demonstrate that using a GOES FDC/VIIRS buffer window substantially mitigates the effect of misalignments. For example, the estimated false alarm rate ranges between 26% and 36% without a window, whereas using a $3\times 3$ window yields values between 7% and 15%. Full article

Oxygen therapy is one of the most widely used interventions in critical care, yet it remains poorly individualized. Recent trials and meta-analysis suggest no mortality difference between conservative and liberal oxygen strategies, reinforcing the perception that dose does not matter within usual ranges. From this perspective, we argue that this apparent neutrality may largely reflect methodological and conceptual limitations, although true clinical equivalence in some patient populations remains plausible and cannot be excluded based on current evidence. Heterogeneous populations, overlapping oxygenation targets, and the absence of exposure metrics (time in hyperoxia, time in hypoxemia, and cumulative partial pressure of arterial oxygen/peripheral oxygen saturation curves) dilute phenotype-specific signals and force distinct physiological responses into a single pooled estimate. We propose a conceptual model in which oxygen behaves as a dose-dependent, time-dependent drug with phenotype-specific therapeutic windows, particularly in chronic hypercapnia, traumatic brain injury, sepsis, and early versus late acute respiratory distress syndrome. Building on this model, we outline a methodological agenda for precision oxygen trials: defining interventions by actual exposure, pre-specifying pathophysiological subgroups, adopting patient-centered core outcome sets, and using adaptive, target-range designs and individual patient data meta-analyses. For contemporary guidelines and research, the key question is no longer whether conservative or liberal oxygen therapy is superior on average, but how to match the right oxygenation range to the right intensive care unit phenotype at the right time. Moving from population-averaged comparisons to exposure-aware, phenotype-oriented strategies is essential if oxygen therapy is to become a truly precision intervention in critical care. Full article

This paper explores how semantic-space reasoning, traditionally used in computational linguistics, can be extended to tactical decision-making in team sports. Building on the analogy between texts and teams—where players act as words and collective play conveys meaning—the proposed methodology models tactical configurations as compositional semantic structures. Each player is represented as a multidimensional vector integrating technical, physical, and psychological attributes; team profiles are aggregated through contextual weighting into a higher-level semantic representation. Within this shared vector space, tactical templates such as high press, counterattack, or possession build-up are encoded analogously to linguistic concepts. Their alignment with team profiles is evaluated using vector-distance metrics, enabling the computation of tactical “fit” and opponent-exploitation potential. A Python-based prototype demonstrates how these methods can generate interpretable, dynamically adaptive strategy recommendations, accompanied by fine-grained diagnostic insights at the attribute level. Evaluation through synthetic scenarios and a pilot study with real match data establishes internal consistency and feasibility of the approach; operational validity in live coaching contexts remains an open question for future prospective validation. Beyond football, the framework offers a potentially generalizable approach for collective decision-making in team-based domains—ranging from basketball and hockey to cooperative robotics and human–AI coordination systems. The paper concludes by outlining future directions toward real-world data integration, predictive simulation, and the validation work required before operational deployment. Full article

Knowledge graphs represent real-world facts as structured triplets and underpin a wide range of applications, including question answering, recommendation, and retrieval-augmented generation. Automatically extracting such triplets from unstructured text is essential for scalable knowledge base construction. Traditional extraction methods require task-specific training data and struggle to generalize across domains. Large language models (LLMs) offer an alternative through in-context learning, enabling flexible extraction without fine-tuning. However, LLMs frequently hallucinate—generating plausible triplets unsupported by the source text. The root cause is the lack of provenance: existing methods produce triplets without explicit links to their textual origins, making faithfulness unverifiable. This paper presents Anchor-Extraction-Verification-Supplement (AEVS), a framework that grounds every triplet element to the source text. AEVS operates in three stages: (1) anchor discovery identifies entities, relation phrases, and attribute values with precise positions, forming a constrained extraction vocabulary; (2) grounded extraction generates triplets linked to discovered anchors; and (3) restoration-based verification validates triplets through hierarchical matching, with a coverage-aware supplement ensuring comprehensive extraction. Experiments on WebNLG, REBEL, and Wiki-NRE demonstrate consistent improvements over both trained models and LLM-based baselines. Ablation studies confirm that anchor-based constraints are the primary mechanism for hallucination reduction. Dedicated analyses of anchor discovery quality, computational cost (2.83–4.28 LLM calls per sample), and hallucination rates (0.23–20.23% across model–dataset configurations) provide insights into the framework’s practical applicability and limitations. Full article

Sex chromosome imbalance is a genetic challenge in species with unequal X-chromosome numbers. Organisms have developed distinct strategies to control this imbalance through a process called dosage compensation. These strategies include X-chromosome inactivation in mammals mediated by the XIST long noncoding RNA and proteins recruited by XIST, and X-linked hypertranscription in male Drosophila driven by the Male-Specific Lethal (MSL) complex. In Caenorhabditis elegans, gene expression is downregulated from each of the two X chromosomes of hermaphrodites by half, thereby matching the levels in XO males. This is mediated by a specialized condensin-containing protein complex, the Dosage Compensation Complex (DCC). In all cases, the chromatin states on the sex chromosomes must be first established and then maintained for the entire lifetime of the organism. Although mammals and nematodes both use repression to achieve dosage compensation, the mechanisms are very different. Here, we summarize recent advances on how repressive chromatin states are established and maintained, with a focus on contrasting C. elegans dosage compensation to XIST-mediated X-chromosome inactivation. We review how specialized chromosome topology, repressive chromatin modifications, and higher-order nuclear architecture are established and maintained to achieve sex-specific regulation of the X chromosomes and highlight key outstanding questions and future research directions. Full article

Quantum machine learning (QML) is an emerging field combining quantum computing and artificial intelligence, with promising applications in medicine and healthcare. This survey reviews more than 60 studies published between 2018 and 2025, highlighting a sharp increase in research activity, especially in the last three years. We address seven core research questions related to publication trends, the use of real quantum hardware versus simulators, quantum architectures overview, dataset types, medical domains, algorithmic frameworks, and reported results. Our analysis shows that most QML research in healthcare is conducted on simulators due to limited hardware access, and it relies on small datasets. Quantum convolutional neural network (QCNN) architectures dominate image-based medical tasks such as tumor detection, pneumonia diagnosis, and ECG interpretation, while feature-based datasets are mainly analyzed with variational quantum classifiers and quantum support vector machines. Despite hardware constraints, QML models often match or surpass classical machine learning approaches in accuracy, frequently reaching 95–99%. However, these performance statements should be qualified to recognize experimental limitations and avoid excessive optimism and should not be interpreted as definitive proof of quantum superiority at this stage. Additionally, issues with reproducibility and reporting of hardware details persist, which is a significant research gap. This review emphasizes the need for standardized benchmarks, more real hardware testing, and architecture-aware algorithm design. With the potential for accelerated diagnostics and personalized healthcare, QML represents a strategic direction for future medical research. Full article

Benchmarking Spanish reading comprehension remains challenging due to the scarcity of proficiency-calibrated resources grounded in authentic human assessments. We introduce IC-UNED-RC-ES, a benchmark comprising more than 6000 items derived from Instituto Cervantes examinations, converted to a machine-readable format while preserving exam structure, proficiency levels, and scoring criteria. Unlike many existing resources, IC-UNED-RC-ES includes a diverse set of exercise formats, combining common multiple-choice questions with new formats such as matching and fill-in-the-gap, which support a broader assessment of reading skills. The benchmark supports evaluation at both the item and exam levels and includes an exercise taxonomy with category-specific metrics. Baseline results with current AI systems reveal a strong difficulty effect (a 15-point drop from lower to advanced levels) and substantial variation across exercise types, with inference- and discourse-heavy categories reaching only 41%. IC-UNED-RC-ES provides a human-aligned, interpretable testbed for diagnosing strengths and weaknesses in Spanish reading comprehension and for tracking progress across model generations. Full article

General-purpose vision–language models (VLMs) are increasingly applied to imaging tasks, yet their reliability on medical visual question answering (Med-VQA) remains unclear. We investigate how three state-of-the-art VLMs—ViLT, BLIP, and MiniCPM-V-2—perform on radiology-focused Med-VQA when evaluated in a modality-aware manner. Using SLAKE and OmniMedVQA-Mini, we construct harmonised subsets for computed tomography (CT), magnetic resonance imaging (MRI), and X-ray, standardising schema and answer processing. We first benchmark all models in a strict zero-shot setting, then perform supervised fine-tuning on modality-specific data splits, and finally add a post-hoc semantic option-selection layer that maps free-text predictions to multiple-choice answers. Zero-shot performance is modest (exact match ≈20% for ViLT/BLIP and 0% for MiniCPM-V-2), confirming that off-the-shelf deployment is inadequate. Fine-tuning substantially improves all models, with ViLT reaching ≈80% exact match and BLIP ≈50%, while MiniCPM-V-2 lags behind. When coupled with option selection, ViLT and BLIP achieve 90–93% exact match and F1 across all modalities, corresponding to 95–97% BERTScore-F1. Our novel results show that (i) modality-specific supervision is essential for Med-VQA, and (ii) post-hoc option selection can transform strong but imperfect generative predictions into highly reliable discrete decisions on harmonised radiology benchmarks. The latter is useful for medical VLMs that combine generative responses with option or sentence selection. Full article

Background/Objectives: Circular RNAs (circRNAs) are emerging players in human diseases, with functions as part of competing endogenous networks. Given the importance of messenger RNA (mRNA) regulation in human diseases and the potential of circRNAs in this regulation, we studied the circRNA–mRNA couple in blood within a cohort of 712 patients suspected of having hereditary colorectal cancer (CRC) and 249 matched controls. Methods: The circRNA–mRNA couple was studied by SEALigHTS (Splice and Expression Analyses by exon Ligation and High-Throughput Sequencing) with a panel of 23 genes involved in CRC predisposition, comprising 788 probes designed at exon ends, enabling the exploration of all exon–exon junctions. Following reverse transcription and probe hybridization on cDNA, nearby probes were ligated, and the number of ligations was quantified using unique molecular identifiers and sequencing. Results: We identified 220 circular junctions, including 47 novel ones. The circRNA/mRNA ratio was 2.42-fold higher in patients compared to controls (p < 10⁻¹⁶), irrespective of age at cancer onset. This increase was mainly driven by POLD1 (fold change 3.84) and a single circPOLD1(3,2) with oncogenic potential Conclusions: This study supports the existence of a physiological balance between circRNA and mRNA that can be disrupted under pathological conditions. It rules out a competitive mechanism between circular and linear transcripts in CRC predisposition and raises questions about the role of specific circRNAs in the development of CRC, either as a cause or a consequence. Full article

Recent evidence indicates that both epithelial-to-mesenchymal transition (EMT) and its reverse process, mesenchymal-to-epithelial transition (MET), are key mechanisms driving breast cancer (BC) metastasis. During EMT, epithelial BC cells acquire mesenchymal traits that enhance motility, invasiveness, and resistance to therapy. A deeper understanding of EMT regulation may therefore unveil novel therapeutic targets to limit disease progression. In this study, we analyzed the expression of key EMT-associated proteins, namely Vimentin, E-cadherin, Cytokeratin-18, and alpha-smooth muscle actin, in a cohort of 95 BC tissue samples and observed marked intra- and inter-tumoral heterogeneity. Notably, we found positive correlations between epithelial and mesenchymal markers, supporting the presence of hybrid epithelial/mesenchymal phenotypes and substantial cellular plasticity, which may contribute to BC heterogeneity. High heterogeneity in marker expression was also detected between tumor tissues and matched adjacent normal tissues. The unexpected complexity uncovered at the protein level prompted us to question whether single markers or limited proteomic panels are sufficient to capture the EMT landscape in BC. Through integrative bioinformatics, we defined a novel EMT gene signature significantly associated with prognosis. Functional enrichment revealed pathways related to extracellular matrix organization, proteoglycans, and intercellular communication, emphasizing the dynamic bidirectional crosstalk between BC cells and the tumor microenvironment. Moreover, we identified a gene cluster linked to cancer stem cell-like features, which may be clinically relevant for patient risk stratification. Overall, our findings underscore the complexity of EMT regulation in BC and introduce a new EMT signature with potential prognostic and therapeutic relevance. Full article

The revision and implementation of the Environmental Protection Law signaled a major transformation in China’s environmental regulatory paradigm—from a traditional command-and-control model to a more diversified and market-oriented approach. This shift has raised critical questions regarding the actual impact of regulation on green technological innovation. Using panel data from A-share listed firms in China between 2011 and 2022, this study employs a propensity score matching–difference-in-differences (PSM-DID) model to identify the causal effect of environmental regulation on green innovation. Results reveal that the enactment of the law significantly enhances firms’ green innovation capacity. Robustness tests confirm the stability of these findings. Further analysis identifies several potential transmission mechanisms. Specifically, we find robust empirical evidence that environmental regulation exerts its effects through elevated R&D investment levels and strengthened executives’ environmental awareness, while the financing constraint and environmental information disclosure channels yield suggestive yet less statistically robust results in indirect effect tests. Moreover, heterogeneous effects are more evident among non-state-owned enterprises, firms in the eastern region, and those in highly market-oriented provinces. This study contributes empirical evidence to the literature on environmental regulation and green innovation, and offers policy insights for improving environmental governance in emerging economies. Full article

The rapid expansion of artificial intelligence (AI) systems has intensified concerns regarding their energy consumption and carbon footprint, raising questions about whether efficiency-focused strategies under the Green AI paradigm are sufficient to ensure system-level environmental sustainability. This study systematically synthesizes empirical evidence on the energy use and carbon emissions of AI systems across their life cycle and develops a conceptual framework to integrate sustainability constraints into AI deployment. A systematic review was conducted in accordance with PRISMA 2020 guidelines and AMSTAR-2 standards, with searches performed in Web of Science, Pubmed and Scopus up to 19 December 2025. Eligible studies quantitatively assessed energy consumption, carbon footprint, greenhouse-gas emissions, or life-cycle impacts associated with AI systems, including training, inference, hardware, and deployment infrastructures. Ten studies met the inclusion criteria. The results show that AI-related environmental impacts are substantial and highly context-dependent, with inference-phase energy demand often matching or exceeding training-related consumption in large-scale deployments. Life-cycle assessments indicate that hardware-related emissions and electricity mix strongly influence total carbon footprints, while efficiency gains are frequently constrained by system-level feedback. These findings suggest that isolated efficiency improvements are insufficient and that sustainable AI requires coordinated, system-level governance embedding energy and carbon constraints into design and operational decision-making. Full article

The automation of precise discipline inspection consultation requires question-answering (QA) systems that are both semantically nuanced and factually grounded. To address the limitations of keyword-based retrieval and the hallucination tendencies of generative language models in high-stakes discipline inspection domains, we propose a two-stage Retrieval-Augmented Generation (RAG) framework designed for Chinese discipline inspection text. Our approach synergizes token-level late interaction and cross-encoder reranking to achieve high-precision evidence retrieval. First, we employ ColBERTv2 to perform efficient, fine-grained semantic matching between queries and lengthy discipline inspection documents. Subsequently, we refine the initial candidate set using a computationally focused cross-encoder, which performs deep pairwise relevance scoring on a shortlist of passages. This retrieved evidence strictly conditions the answer generation process of a large language model (DeepSeek-chat). Through rigorous evaluation on a curated corpus of real Chinese discipline inspection documents and expert-annotated queries, we demonstrate that our pipeline significantly outperforms strong baselines—including BM25, single-stage dense retrieval (BGE), and a simplified ColBERT variant—in both retrieval metrics (Recall@k, Precision@k) and answer faithfulness. Our work provides a robust, reproducible blueprint for building reliable, evidence-based discipline inspection AI systems, highlighting the critical role of hierarchical retrieval in mitigating hallucinations for domain-specific QA. Full article