Search Results (694)

Artificial intelligence is typically formulated as an information-processing system composed of artificial neurons, where computation is understood as recursive operations connecting inputs and outputs. However, real neural systems are materially embodied and continuously reconfigured by metabolic and physical processes, suggesting that computation cannot be reduced to fixed causal structures. In this paper, we propose a theoretical framework that captures the interplay between informational and material processes as the interaction between two computational schemes: a vertical scheme, representing fixed cause–effect relations, and a horizontal scheme, representing transformations between such relations. We show that the vertical scheme corresponds to Bayesian inference, which updates probability distributions over a fixed hypothesis space, and is consistent with the free-energy minimization principle. In contrast, the horizontal scheme is formalized as inverse Bayesian inference, which modifies the hypothesis space itself by updating likelihood structures based on experienced data. We further demonstrate that the interplay between these schemes can be expressed algebraically as a process of continuously gluing Boolean algebras. This construction yields a non-distributive orthomodular lattice, i.e., quantum logic, without invoking Hilbert space formalism. In this view, quantum logic emerges not as a static logical system but as a structural consequence of dynamically reconfiguring causal contexts. This framework provides a unified perspective in which inference is understood not only as optimization within a fixed model but also as a process that generates and transforms the model itself. It offers a formal basis for describing open-ended computation and suggests a connection to approaches such as unconventional computing and Natural Born Intelligence, where computational structures evolve through interaction with material processes. Unlike existing approaches, this framework derives quantum-logic-like structure from the continual reconfiguration of causal contexts rather than from Hilbert-space assumptions or optimization within a fixed hypothesis space. Full article

Objective: To examine the associations of metabolic health and obesity phenotypes with liver fat accumulation and hepatic steatosis in adults. Methods: We analyzed 676 non-Hispanic white adults (18–95 years; 55.8% female) from the Fels Longitudinal Study using a cross-sectional design. Participants were classified into metabolically healthy normal weight (MHNW), metabolically healthy obesity (MHO), metabolically unhealthy normal weight (MUNW), and metabolically unhealthy obesity (MUO) phenotypes. Metabolically unhealthy status was defined as the presence of ≥1 metabolic dysfunction, consistent with prior epidemiological definitions; secondary analyses using ≥2 were also performed. Obesity was defined using DXA-derived body fat percentage. Liver fat (%) was quantified using magnetic resonance imaging, and hepatic steatosis was defined as liver fat > 5.56%. Multivariable linear and probit regression models were used to evaluate associations, adjusting for demographic and lifestyle covariates; secondary models additionally examined dietary intake. Results: Mean liver fat was 5.95% (SE = 0.23), and steatosis was present in 29.8% of participants. Compared to MHNW individuals, liver fat was significantly higher in MHO (mean 3.77% vs. 2.67%), MUNW (4.63%), and MUO (8.47%) phenotypes. After covariate adjustment, liver fat was 33.8% (95% CI: 13.7–57.5%) higher in MHO, 28.1% (10.1–49.0%) higher in MUNW, and 113.0% (85.3–144.7%) higher in MUO relative to MHNW. Corresponding increases in steatosis probabilities were observed across phenotypes. No individual dietary component or dietary pattern was significantly associated with liver fat after adjustment. Conclusions: Metabolically healthy obesity was associated with higher liver fat and steatosis probability compared with metabolically healthy normal weight, with levels comparable to metabolically unhealthy normal weight individuals. These findings suggest that the absence of overt metabolic abnormalities does not necessarily indicate a metabolically benign state with respect to liver fat accumulation. Given the cross-sectional design, these results should be interpreted as associations rather than causal relationships. Full article

Traditional inspection and supervision in power grid operations suffer from heterogeneous multi-source data (text, tables, and images), low policy retrieval efficiency, difficult issue characterization, non-standardized reporting, and weak closed-loop rectification. To address these challenges in Guangdong Power Grid scenarios, this paper proposes CSA-DR, a Cross-modal Semantic Alignment and Dynamic Routing enhancement method. CSA-DR retrieves relevant policy documents, structured tables, and inspection-related images from an external regulatory knowledge base and encodes them via a tri-modal into a unified semantic space, achieving precise cross-modal alignment between inspection descriptions and supporting evidence. A dynamic routing mechanism is introduced to adaptively allocate modality importance according to task requirements, significantly improving key information extraction, violation detection, and causal analysis. Additionally, the framework integrates an external regulatory knowledge base. For each inspection task, relevant policy documents, structured tables, and evidence images are retrieved from this knowledge base and used as the tri-modal input to the model. This knowledge-grounded design enables cross-modal semantic alignment, evidence traceability, and standardized inspection report generation. Experiments on a real multi-source inspection dataset from Guangdong Power Grid show that CSA-DR consistently outperforms the compared baseline methods and ablation variant across all applicable metrics, with notable improvements in cross-modal MRR and image-to-image Recall@5. Full article

Bayesian network-based causal structure learning provides an effective framework for uncovering causal relationships among continuous variables. However, many existing methods for continuous data still rely on strong parametric distribution assumptions, which may introduce information loss and reduce Bayesian network modeling accuracy. Kernel density estimation (KDE), a non-parametric statistical method that is more flexible in density estimation form, offers a versatile framework for conducting conditional independence (CI) tests. This approach enables the estimation of mutual information and conditional mutual information, thereby facilitating the identification of underlying structural relationships. Nevertheless, the high computational cost of KDE-based CI testing restricts its practical application in continuous-variable causal learning. To address this issue, this study introduces a radial symmetric kernel-based acceleration scheme within a Fast Fourier Transform (FFT) framework to improve the efficiency of density estimation. On this basis, an enhanced Bayesian network structure learning method is developed for continuous variables, enabling more efficient estimation of mutual information and conditional mutual information while improving the computational efficiency and empirical stability of variable dependency discovery. With proper bandwidth and grid resolution, the proposed MMHC-FFTKDE framework achieves a reduction in computational runtime and improves efficiency compared to MMHC-KDE in the ablation setting, while maintaining competitive F1-scores and SHD for causal structure discovery. Full article

Ototoxicity is traditionally viewed as a local cochlear adverse effect of indispensable therapies such as cisplatin and aminoglycosides. However, emerging evidence suggests that cochlear vulnerability is shaped by systemic physiology, including inflammatory tone, vascular barrier integrity, and metabolic state. In this Review, we propose a Gut–Mito–Ear axis in which gut ecosystem function influences circulating mediator modules that converge on two cochlear mediator nodes: blood–labyrinth barrier (BLB) gating and mitochondrial stress tolerance. We synthesize evidence showing that gut perturbation can alter cochlear outcomes in vivo, that at least one microbiota-derived metabolite signal can directly protect hearing in experimental settings, and that BLB dysfunction and inflammatory trafficking are mechanistically relevant to cisplatin- and aminoglycoside-induced injury. We further organize the literature using an evidence-weighted framework that distinguishes direct cochlear causality from mechanistic plausibility and explicitly retains negative studies as boundary-setting evidence. Finally, we outline a translational roadmap in which microbiome-targeted prevention is pursued through mediator-anchored, non-interference-aware strategies and evaluated across linked state variables spanning exposure context, gut function, defined mediator modules, BLB gating, mitochondrial stress tolerance, and auditory phenotype. The Gut–Mito–Ear axis is not considered an established mechanism. We present it as a falsifiable systems-biology model that organizes the current evidence. Within this model, we define the minimum and ideal standards for A-tier causal evidence, explicit criteria for interpreting boundary-setting negative (A−) studies, and a set of testable predictions for causal validation. Full article

This study investigates the influence of blockchain-enabled supply chain traceability (BESCT) on sustainable supply chain practices (SSCP) in the context of small and medium-sized enterprises (SMEs) in the Turkish manufacturing sector. Grounded in the Resource-Based View (RBV), the research further examines the mediating roles of perceived information transparency (PIT) and supply chain collaboration (SCC) and the moderating effect of environmental orientation (EO). The study employs a quantitative research design using data collected from 652 managers representing various manufacturing SMEs. Structural equation modeling via SmartPLS 4.0 is applied to test a moderated mediation model and assess the relationships among the constructs. The results indicate that BESCT is positively associated with SSCP both directly and through PIT and SCC as mediating mechanisms. PIT is linked to improved visibility and information integrity, while SCC is associated with joint sustainability efforts across supply chain partners. Moreover, EO strengthens the positive associations between BESCT and PIT with SSCP, while its effect on collaboration is more nuanced. Given the cross-sectional design, these findings should be interpreted as associative rather than causal. In addition, the use of a non-probability convenience sampling approach may limit generalizability, and the results should be interpreted with caution. This study contributes to the RBV literature by conceptualizing blockchain as a traceability-enabled dynamic capability that supports sustainability-oriented practices in SMEs. Full article

Financial time series forecasting remains a persistent challenge due to the non-stationary nature, inherent noise, and multi-scale temporal dependencies present in market data. This paper presents MSTFNet, a multi-scale temporal fusion network that combines dilated causal convolutions with a frequency-enhanced sparse attention mechanism for improved financial prediction. The proposed architecture consists of three core components: a multi-scale dilated causal convolution module that extracts temporal patterns across different time horizons through parallel convolutional branches with varying dilation rates, a frequency-enhanced sparse attention mechanism that leverages Fast Fourier Transform to identify dominant periodic components and modulate attention weights accordingly, and an adaptive scale fusion gate that learns to dynamically combine representations from multiple temporal scales. Extensive experiments conducted on three public financial datasets (S&P 500, CSI 300, and NASDAQ Composite) spanning the period from January 2015 to December 2024 show two key results. First, consistent with near-efficient markets, the random-walk benchmark (${\widehat{y}}_{t+1}=y_t$) outperforms all the data-driven models on level-error metrics (MAE, RMSE, MAPE, and $R^2$), establishing the martingale as the binding lower bound on point-prediction error. Second, MSTFNet achieves the highest directional accuracy (DA) across all three indices—56.3% on the S&P 500 versus 50.0% for the martingale—representing a 6.3 percentage-point improvement that generates positive pre-cost returns in a trading strategy backtest. Among the eight data-driven baselines (LSTM, GRU, TCN, Transformer, Autoformer, FEDformer, PatchTST, and iTransformer), MSTFNet also achieves the lowest MAE, reducing it by 13.6% relative to the strongest data-driven baseline (iTransformer) on the S&P 500. These results confirm that integrating multi-scale temporal modeling with frequency-domain guidance extracts a real, if modest, directional signal from financial time series. Full article

While the quantum emergence of spacetime is becoming a major research topic in physics, the quantum emergence of intelligence has not been widely researched in quantum information science (QIS). Following causal-logical quantum gravity theory, bipolar entropy vs. entropy and negative entropy (or negentropy) are reviewed and distinguished for quantum emergence/submergence of quantum agent (QA) and quantum intelligence (QI) in algebraic terms. This work refers to QA as an entangled bipolar string/superstring in bipolar dynamic equilibrium (BDE) and QI being centered on logically definable causality in regularity, mind-light-matter unity, and brain-universe similarity. ER = EPR is extended to ER ≥≥ EPR for the mathematical scalability of bipolar strings and their collective entanglement. The extension leads to a number of conjectures, testable predictions, and theorems. The term “equilibraton” is proposed as a type of EPR or bipolar generic string to serve as an entropic stitch to collectively hold the universe together as a quantum entanglement in BDE with ubiquitous, regulated local emergence and submergence of QA&QI. Equilibraton leads to the concept of bipolar entropy square—a complete entropic solution to the background issue in quantum gravity. With complete background independence, energy/information conservational bipolar entropy, energy/information invariance, bipolar entropy non-additivity, and equilibrium-based plateau concavity are introduced. The nature of the one-dimensional arrow of time is conjectured. As a unification of order and disorder for equilibrium-based regulation, bipolar entropy bridges QA&QI to agentic AI, where quantum-bio-economics can be viewed as a topological intervention of a natural dynamic equilibrium in a social or natural world. Use cases are reviewed to illustrate the practical and theoretical aspects of bipolar entropy in business management, quantum-bio-economics, quantum cryptography, physics, and biology. Eddington–Einstein’s comments on entropy are revisited. It is expected that bipolar entropy will bring quantum emergence/submergence to agentic AI&QI for entangled machine thinking and imagination as a naturally scalable and testable foundation of real-world quantum gravity, quantum information science (QIS), quantum cognition, and quantum biology (QCQB) to enhance Large Language AI Models (LLMs) and machine intelligence. Full article

The sponge city pilot policy (SCP) is a green infrastructure initiative that integrates ecological stormwater management, land-use planning, and urban sustainability goals. This study employs the super-efficiency slack-based measure (SBM) model to evaluate the green total factor productivity (GFP) of 278 prefecture-level and above cities in China from 2010 to 2022. It then applies a difference-in-differences (DID) model to identify the causal effect of the SCP on urban GFP while further examining transmission mechanisms and heterogeneous policy effects. The empirical findings show that: (1) the SCP significantly enhances urban GFP, with pilot cities exhibiting an average increase of approximately 6.08% relative to non-pilot cities, indicating broader medium- to long-term ecological–economic co-benefits beyond the policy’s immediate hydrological objectives; (2) the policy effect is more pronounced in cities with stronger economic foundations, larger urban scales, greater environmental governance pressure, weaker resource dependence, and more favorable locational conditions; and (3) the SCP promotes industrial structure transformation (IST) and green technological innovation (GTI), which jointly mediate the relationship between ecological infrastructure and green productivity. Drawing on ecological modernization theory and structural change theory, this study explains how ecological infrastructure, as a techno-structural reform mechanism, can internalize environmental externalities, stimulate innovation, and facilitate sustainable urban transformation. These findings provide evidence that green infrastructure policies can generate both ecological and economic co-benefits, offering useful insights for climate-resilient and sustainable urban planning. Full article

Background: Splenic marginal zone lymphoma (SMZL) is a rare indolent lymphoma with extremely limited population-level evidence on social and treatment correlates of survival. Methods: We conducted a retrospective cohort study using SEER (2000 to 2022) to evaluate OS in primary SMZL (ICD O 3 9689; spleen C42.2). We summarized baseline features and treatments and used Kaplan–Meier and Cox regression. Results: The cohort included 3548 patients (mean age: 68.2 years; 53.6% female). Most were White (89.8%) and non-Hispanic (92.1%). The Ann Arbor stage was missing in 39.4%. Treatment included systemic antineoplastic therapy in 26.4%, beam radiation in 0.7%, and primary site surgery in 21.4%. At last follow-up, 56.8% were alive; non-Hodgkin lymphoma accounted for 15.8% deaths in the cohort, with substantial competing causes including heart disease (6.1%). In multivariable Cox analysis, OS was independently associated with age (HR 1.082 per year, 95% CI 1.072–1.091), male sex (HR 1.34, 95% CI 1.14–1.57), Hispanic ethnicity (HR 1.42, 95% CI 1.08–1.88), systemic antineoplastic therapy (HR 1.42, 95% CI 1.18–1.70), divorced/separated marital status vs. married (HR 1.35, 95% CI 1.03–1.77), and stage IV disease (HR 1.70, 95% CI 1.16–2.50). Race and year of diagnosis were not independently associated with OS in the adjusted model. Conclusions: In our large population-based analysis, OS in SMZL tracks with demographic and social variables and competing risks. Stage missingness and treatment selection limit causal inference for management effects. Full article

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 10⁸ CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance. Full article

Background/Objectives: Several recent human studies have associated the use of certain medicines, such as antibiotics and antacids, with allergic conditions, potentially through microbiome disruption. In contrast, probiotics which may prevent dysbiosis, could have protective effects. Our meta-analysis aimed to evaluate the impact of these drugs (consumed during pregnancy or early life) on the risk of childhood food allergy, based on the available literature. Methods: Literature searches were conducted in the EMBASE, PubMed, Cochrane, and Web of Science databases using predefined PICO criteria. Overall, our meta-analysis included 25 studies involving 1,662,861 mothers and 5,164,280 children. Results: Using the random-effects model, we found that prenatal and early life antibiotic use (up to 2 years of age) was associated with higher odds of food allergy in childhood (OR: 1.34; 95% CI [1.10, 1.63], OR: 1.53; 95% CI [1.18, 1.98], respectively). Proton pump inhibitors were also associated with a risk of food allergies (OR: 2.65; 95% CI [1.22–5.77]), whereas the impact of H2-receptor antagonists was non-significant (OR: 2.07; 95% CI [0.96–4.45]). Probiotic use during the first two years of life was not associated with decreased risk for food allergy in children (OR: 1.25; 95% CI [0.46, 3.38]). Conclusions: These findings suggest an association between microbiome-disrupting medications during pregnancy and early childhood and an increased risk of childhood food allergy, especially those with a family history of food allergy. However, due to the predominantly observational design of the included studies, causality cannot be established. These results highlight the need for cautious and judicious use of such medications in these populations. Full article

Electroencephalogram (EEG) signals serve as the core information carrier for brain–computer interfaces (BCIs); however, their highly non-stationary nature, extremely low signal-to-noise ratio, and significant inter-individual variability pose considerable challenges for signal decoding. Existing deep learning methods struggle to strike a balance between multi-scale, fine-grained feature extraction and efficient long-range temporal modeling. To overcome this limitation, this study proposes a novel deep cascaded architecture, MSResBiMamba, which deeply integrates multi-scale spatiotemporal feature learning with cutting-edge long-sequence modeling techniques. The model first utilizes an enhanced multi-scale spatiotemporal convolutional network (MS-CNN) combined with a SE-channel attention mechanism to adaptively extract local multi-band features and dynamically suppress redundant artefacts. Subsequently, it innovatively introduces an enhanced bidirectional Mamba (Bi-Mamba) module to efficiently capture non-causal long-range temporal dependencies with linear computational complexity, whilst cascading multi-head self-attention mechanisms to establish global higher-order feature interactions. Extensive experiments on the BCI Competition IV-2a dataset demonstrate that MSResBiMamba achieves outstanding classification performance in multi-class motor imagery tasks, significantly outperforming traditional methods and existing state-of-the-art neural networks. Ablation studies and t-SNE visualisations further confirm the model’s robustness in feature decoupling and cross-subject applications, providing a high-precision, high-efficiency decoding solution for BCI systems. Full article

Background: Depression is highly prevalent among individuals with chronic pain and strongly impacts pain intensity, psychological functioning, and health-related quality of life. Self-efficacy has emerged as a potentially modifiable resilience factor within this interplay, yet large-scale real-world evidence integrating self-efficacy into multidimensional pain–depression models remains limited. Methods: This cross-sectional registry-based analysis evaluated standardized patient-reported measures from chronic pain patients enrolled in the German Pain e-Registry. All variables were directionally harmonized and transformed into standardized deviation scores (hSDSs) relative to patients without depression. Group-level hSDS profiles for five DASS-21 depression severity strata (none, mild, moderate, severe, extreme) were compared across pain intensity, disability, psychological well-being, affective pain processing, quality of life, neuropathic pain features, and pain-related self-efficacy (PSEQ). Correlations and exploratory principal component analysis (PCA) were used to assess multivariate structure. PCA-informed path models were estimated to evaluate directional relationships between pain, function, depression, and self-efficacy. All directional and mediation models represent exploratory, theory-informed statistical frameworks and do not imply causal or mechanistic relationships. Results: Across all domains, hSDS values increased monotonically with depression severity, while self-efficacy showed the strongest inverse gradient. Exploratory PCA revealed a dominant severity component explaining most variance and a secondary affective–self-efficacy axis, supporting the conceptual separation between functional–physical and psychological–affective symptom clusters. In the bottom-up path model (pain → function → self-efficacy → depression), self-efficacy showed the largest indirect statistical contribution within the proposed path models, and the model explained 55% of depression variance (R² = 0.55). In the top-down model (depression → affective pain → self-efficacy → pain), 45% of pain intensity variance was explained (R² = 0.45), again with self-efficacy as a central mediating construct. Associations remained robust after adjustment for age, sex, and BMI, as well as during sensitivity analyses. Conclusions: This large real-world cohort demonstrates a highly coherent pattern of associations across biopsychosocial domains and highlights pain-related self-efficacy as a central statistical construct linking pain, functional impairment, and depressive symptom burden within the applied exploratory models. The findings suggest that self-efficacy occupies a key position in the interplay between pain and mood, and that pharmacological and non-pharmacological treatments traditionally used in chronic pain management may be associated with changes in this construct. Importantly, all directional and mediation analyses are exploratory and do not imply causal or mechanistic relationships. Therapeutic strategies aimed at enhancing self-efficacy may therefore represent promising targets for future research within multimodal pain management frameworks. Full article

Video-based action recognition for neural rehabilitation—spanning stroke recovery, Parkinsonian gait assessment, and cerebral palsy monitoring—faces critical challenges, including temporal ambiguity, non-causal motion correlations, and the absence of causally grounded dynamics modeling. While transformer-based architectures achieve strong performance, they often exploit spurious temporal and environmental cues, limiting reliability in safety-critical clinical settings. We propose NeuroPrisma, a neuro-prismatic video framework that integrates frequency-domain spectral decomposition with causal intervention under Structural Causal Models (SCMs) via the backdoor criterion. NeuroPrisma introduces (i) a Prismatic Spectral Attention (PSA) module, which applies discrete Fourier transforms to decompose temporal features into multi-scale frequency bands, disentangling slow postural dynamics from rapid corrective movements, and (ii) a Causal Intervention Layer (CIL), which performs do-calculus-based backdoor adjustment to remove confounding influences and produce causally invariant representations. PSA preconditions representations prior to intervention, improving confounder estimation and causal robustness. Extensive evaluation against seven state-of-the-art models (I3D, SlowFast, TimeSformer, ViViT, Video Swin Transformer, UniFormerV2, and VideoMAE) demonstrates that NeuroPrisma achieves 98.7% Top-1 accuracy on UCF101, 82.4% on HMDB51, 71.2% on Something-Something V2, and 91.5%/95.8% on NTU RGB+D (Cross-Subject/Cross-View), consistently outperforming prior methods. It further reduces the Causal Confusion Score (CCS) by 42.3%, indicating substantially lower reliance on spurious correlations, while maintaining real-time performance with 23.4 ms latency per 16-frame clip on an NVIDIA A100 GPU. All improvements are statistically significant (p < 0.001, Cohen’s d = 0.72–1.24). Evaluation was conducted exclusively on benchmark datasets (UCF101, HMDB51, Something-Something V2, and NTU RGB+D) under controlled conditions, without direct clinical validation on neurological patient cohorts. Overfitting was mitigated using three random seeds (42, 123, 456), RandAugment, Mixup (α = 0.8), weight decay (0.05), and early stopping. Cross-dataset generalization from UCF101 to HMDB51 without fine-tuning achieved 76.2% Top-1 accuracy. Future work will focus on prospective clinical validation across stroke, Parkinson’s disease, and cerebral palsy populations, including correlation with standardized clinical assessment scales such as Fugl–Meyer, UPDRS, and GMFCS. These results establish NeuroPrisma as a causally grounded and computationally efficient framework for reliable, real-time movement assessment in clinical rehabilitation systems. Full article