Search Results (1,016)

Background/Objectives: Patients admitted to intensive care units (ICUs) are confronted with complex clinical situations that impact their physical condition and psychological well-being. Psycho-emotional disorders such as pain, anxiety and post-traumatic stress are highly prevalent in this context, significantly affecting both the patient’s experience and the quality of care provided. Effective communication can help manage patients’ psycho-emotional states and prevent post-ICU disorders. To evaluate the effectiveness of the CONECTEM communicative intervention in improving the psycho-emotional well-being of critically ill patients admitted to the intensive care unit, regarding pain, anxiety, and post-traumatic stress symptoms. Methods: A quasi-experimental study employed a pre–post-test design with both a control group and an intervention group. The study was conducted in two ICUs in a tertiary Hospital in Spain. A total of 111 critically ill patients and 180 nurse–patient interactions were included according to the inclusion/exclusion criteria. Interactions were classified according to the level of the patient’s consciousness into three groups: G1 (Glasgow 15), G2 (Glasgow 14–9), and G3 (Glasgow < 9). Depending on the patient’s communication difficulties, nurses selected one of three communication strategies of the CONECTEM intervention (AAC low teach, pictograms, magnetic board, and musicotherapy). Pain was assessed using the VAS or BPS scale, anxiety using the STAI, and symptoms of PTSD using the IES-R. The RASS scale was utilized to evaluate the degree of sedation and agitation in critically ill patients receiving mechanical ventilation. Data analysis was performed using repeated ANOVA measures for the pre–post-test, as well as Pearson’s correlation test and Mann–Whitney U or Kruskal–Wallis statistical tests. Results: The results showed pre–post differences consistent with pain after the intervention in patients with Glasgow scores of 15 (p < 0.001) and 14–9 (p < 0.001) and in anxiety (p = 0.010), reducing this symptom by 50% pre-test vs. 26.7% post-test. Patients in the intervention group with levels of consciousness (Glasgow 15–9) tended to decrease their post-traumatic stress symptoms, with reductions in the mean IES scale patients with a Glasgow score of 15 [24.7 (±15.20) vs. 22.5 (±14.11)] and for patients with a Glasgow score of 14–9 [(Glasgow 14–9) [30.2 (±13.56) 27.9 (±11.14)], though this was not significant. Given that patients with a Glasgow score below 9 were deeply sedated (RASS-4), no pre–post-test differences were observed in relation to agitation levels. Conclusions: The CONECTEM communication intervention outcomes differed between pre- and post-intervention assessments in patients with a Glasgow Coma Scale score of 15–9 regarding pain. These findings are consistent with a potential benefit of the CONECTEM communication intervention, although further studies using designs that allow for stronger causal inference are needed to assess its impact on the psycho-emotional well-being of critically ill patients. Full article

Building extraction from remote sensing imagery plays a pivotal role in applications such as smart cities, urban planning, and disaster assessment. Although deep learning has significantly advanced this task, existing methods still struggle to strike an effective balance among global semantic understanding, local detail recovery, and multi-scale contextual awareness—particularly when confronted with challenges including extreme scale variations, complex spatial distributions, occlusions, and ambiguous boundaries. To address these issues, we propose TriadFlow-Net, an efficient end-to-end network architecture. First, we introduce the Multi-scale Attention Feature Enhancement Module (MAFEM), which employs parallel attention branches with varying neighborhood radii to adaptively capture multi-scale contextual information, thereby alleviating the problem of imbalanced receptive field coverage. Second, to enhance robustness under severe occlusion scenarios, we innovatively integrate a Non-Causal State Space Model (NC-SSD) with a Densely Connected Dynamic Fusion (DCDF) mechanism, enabling linear-complexity modeling of global long-range dependencies. Finally, we incorporate a Multi-scale High-Frequency Detail Extractor (MHFE) along with a channel–spatial attention mechanism to precisely refine boundary details while suppressing noise. Extensive experiments conducted on three publicly available building segmentation benchmarks demonstrate that the proposed TriadFlow-Net achieves state-of-the-art performance across multiple evaluation metrics, while maintaining computational efficiency—offering a novel and effective solution for high-resolution remote sensing building extraction. Full article

Picea schrenkiana is a keystone species in Central Asian ecosystems currently threatened by climate-driven disease outbreaks. Here, we investigated the causal agent of needle blight and characterized the associated microbial dynamics. By integrating tissue isolation, Koch’s postulates, and high-throughput amplicon sequencing across a disease severity level, we confirmed Lirula macrospora as the etiological agent. Community analysis revealed that disease severity is the primary driver of succession, with alpha diversity peaks at the moderate infection stage. Notably, the abundance of Lirula surged from 2.56% in healthy needles to 65.10% in severe cases, displacing the core endophyte Phaeococcomyces, while potentially beneficial bacteria like Sphingomonas showed only transient enrichment. Furthermore, cross-kingdom co-occurrence network analysis revealed marked topological restructuring whereby the system reached a complex ecological “tipping point” during moderate stage before undergoing significant simplification. As the disease progressed, L. macrospora shifted from a peripheral node to a central hub, effectively dismantling the native microbial network. We conclude that L. macrospora infection triggers a cascading collapse of the needle microbiome, driving a phase shift from a healthy homeostasis to a pathogen-dominated state. These findings elucidate the critical mechanisms of pathogen-microbiome interactions and provide a theoretical basis for the ecological management of P. schrenkiana forests. 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

High Mobility Group ^{Box 1} (HMGB1) and S100 proteins are major ligands of Receptor for Advanced Glycation End-products (RAGE) and have causal roles in endometriosis lesions. Yet the AGE–RAGE pathway that unifies Advanced Glycation End-products (AGEs) with these ligands has not been assessed in endometriosis. In diabetes, atherosclerosis, and chronic kidney disease, AGE–RAGE links insulin resistance and oxidative stress to inflammation, fibrosis, and organ harm. Endometriosis shares key drivers of AGE accumulation, including insulin resistance, oxidative stress, and chronic inflammation. Endometriosis is also linked to higher vascular risk and arterial stiffness. We asked whether AGE–RAGE could bridge metabolic stress to pelvic lesions and systemic risk. We did a focused review of mechanisms and an evidence map of studies on AGEs, RAGE, or known RAGE ligands in endometriosis. We grouped findings as most consistent with a driver, amplifier, consequence, or parallel role. We included 29 studies across human samples, cell systems, and animal models. Few studies measured AGE adducts directly. Most work tracked RAGE ligands (mainly HMGB1 and S100 proteins) and downstream immune and angiogenic programs. Across models, this pattern fits best with a self-reinforcing loop after lesions form. RAGE expression often aligned with lesion remodeling, especially fibrosis. Blood and skin readouts of AGE burden were mixed and varied by cohort and sample type. A central gap is receptor proof. Many models point to shared Toll-like receptor 4 (TLR4)/ nuclear factor kappa B (NF-κB) signaling, but few test RAGE dependence. Overall, current evidence supports AGE–RAGE as a disease-amplifying loop involved in chronic inflammation and fibrosis rather than an initiating trigger. Its effects likely vary by stage and site. Priorities now include direct lesion AGE measurement, paired systemic–pelvic sampling over time, receptor-level studies, and trials testing diet or drug interventions against clear endpoints. Outcomes could include fibrosis, angiogenesis, immune state, pain, and oocyte and follicle function. Full article

We present a unified description of dense matter and neutron star structure based on simple but physically motivated models. Starting from the thermodynamics of degenerate Fermi gases, we construct an equation of state for cold, catalyzed matter by combining relativistic fermion statistics with the liquid drop model of nuclear binding. The internal stratification of matter in the outer crust is described by the $\beta$-equilibrium, neutron drip and a gradual transition to supranuclear matter. Short-range repulsive interactions inspired by Quantum Hadrodynamics are incorporated at high densities in order to ensure stability and causality. The resulting equation of state is used as input in the Tolman–Oppenheimer–Volkoff equations, yielding self-consistent neutron star models. We compute macroscopic stellar properties including the mass–radius relation, compactness and surface redshift that can be compared with recent observational data. Despite the simplicity of the underlying microphysics, the model produces neutron star masses and radii compatible with current observational constraints from X-ray timing and gravitational-wave measurements. This work demonstrates that physically transparent models can capture the essential features of neutron star structure and provide valuable insight into the connection between dense-matter physics and astrophysical observables; they can also be used as easy-to-handle models to test the impact of more complicated phenomena and variations in neutron stars. Full article

Control measures for pine wilt disease (PWD) are widely implemented, yet multi-year field comparisons that track biodiversity trajectories across contrasting management regimes remain limited. We conducted a 3-year (2023–2025) replicated study across nine pine-forest sites in Gyeongsangnam-do, Republic of Korea, comparing three management regimes (Clear-cut, Fumigation/Aerial, Unmanaged) to evaluate regime-associated patterns in ground-active beetle diversity, activity density, and community composition while considering understory vegetation cover. Regime-associated differences were consistent but dynamic: Unmanaged stands generally supported higher richness and Shannon diversity (H′), Clear-cut stands showed the lowest diversity immediately after harvest, and Fumigation/Aerial stands maintained the highest activity density. Assemblage composition separated strongly among regimes within each year, and indicator taxa highlighted regime-associated assemblage states, notably Pheropsophus jessoensis (Fumigation/Aerial), Carabus tuberculosus (Clear-cut), and Blindus strigosus (Unmanaged). Because regimes were assigned at the site level and were partially confounded by geographic region, we interpreted these outcomes as region-structured, regime-associated patterns rather than strictly causal effects. We recommend integrating PWD management with retention forestry (e.g., partial canopy and deadwood retention) and routine biodiversity monitoring to reconcile effective disease suppression with the long-term conservation of forest biodiversity. Full article

We introduce Recognition Geometry (RG), an axiomatic framework in which geometric structure is not assumed a priori but derived. The starting point of the theory is a configuration space together with recognizers that map configurations to observable events. Observational indistinguishability induces an equivalence relation, and the observable space is obtained as a recognition quotient. Locality is introduced through a neighborhood system, without assuming any metric or topological structure. A finite local resolution axiom formalizes the fact that any observer can distinguish only finitely many outcomes within a local region. We prove that the induced observable map $\overline{R}:{\mathcal{C}}_R\to \mathcal{E}$ is injective, establishing that observable states are uniquely determined by measurement outcomes with no hidden structure. The framework connects deeply with existing approaches: C^*-algebraic quantum theory, information geometry, categorical physics, causal set theory, noncommutative geometry, and topos-theoretic foundations all share the measurement-first philosophy, yet RG provides a unified axiomatic foundation synthesizing these perspectives. Comparative recognizers allow us to define order-type relations based on operational comparison. Under additional assumptions, quantitative notions of distinguishability can be introduced in the form of recognition distances, defined as pseudometrics. Several examples are provided, including threshold recognizers on ${\mathbb{R}}^n$, discrete lattice models, quantum spin measurements, and an example motivated by Recognition Science. In the last part, we develop the composition of recognizers, proving that composite recognizers refine quotient structures and increase distinguishing power. We introduce symmetries and gauge equivalence, showing that gauge-equivalent configurations are necessarily observationally indistinguishable, though the converse does not hold in general. A significant part of the axiomatic framework and the main constructions are formalized in the Lean 4 proof assistant, providing an independent verification of logical consistency. Full article

In view of the depletion of high-grade magnesite resources in China, this study presents a comparative analysis of two industrial fused magnesia products produced via a flotation–fusion route. A low-grade magnesite (DSQLM-3, MgO 41.48 wt.%) was upgraded by reverse flotation to a concentrate (FDSQLM-3, MgO 47.55 wt.%) with >97% SiO₂ removal. Two fused magnesia samples (FM-1 from natural high-grade ore DSQLM-1; FFM-3 from concentrate FDSQLM-3) were produced under identical arc-furnace melting (2800 °C, 4 h), followed by natural cooling. Although FFM-3 showed higher MgO (97.61 vs. 97.25 wt.%), its bulk density was comparable to FM-1 (3.45 vs. 3.46 g/cm³). XRD/Rietveld refinement and SEM-EDS indicated that CMS dominated the Ca–silicate assemblage in FM-1, whereas β/γ-C₂S was observed in FFM-3, coinciding with a higher CaO/SiO₂ (C/S) ratio (2.85 vs. 0.68). Image analysis further showed higher grain boundary microcrack metrics in FFM-3. These observations are consistent with reports in the literature stating that the β → γ transformation of C₂S during cooling involves ~12% volume expansion that can contribute to cracking; however, cooling history and composition were not independently controlled in this industrial comparison, so the relationships are interpreted as data-supported associations rather than isolated causality. The results suggest that beneficiation strategies may benefit from managing residual oxide balance (especially C/S ratio) in addition to reducing total impurities. Mechanical and thermomechanical properties were not measured and should be evaluated in future work. Full article

This paper constructs a two-way fixed effects model using data from 4623 Chinese A-share listed enterprises from 2011 to 2022, confirming that firm digital transformation can enhance access to sustainable trade credit. Specifically, for every 1% increase in the standard deviation of digital transformation, the trade credit obtained by enterprises increases by 2.14% in relation to their average value. We employed instrumental variable (IV) and propensity score matching (PSM) methods, utilizing the Broadband China pilot policy as a quasi-natural experiment to conduct a multi-period propensity score matching-difference in differences (PSM-DID) analysis to address potential issues of reverse causality and sample selection bias. Mechanism analysis indicates that the diversification of supplier structures, R&D innovation, and market share facilitated by digitalization are three main channels. This effect is particularly significant in state-owned enterprises, mature enterprises, and those with higher social trust. Finally, the study also found that the spillover effects of digital transformation encourage client enterprises to allocate credit resources to downstream firms, thereby promoting the sustainable development of supply chain finance. Furthermore, the digital transformation primarily alleviates short-term credit challenges for enterprises and reduces their reliance on bank credit. Full article

The human gut microbiome and its relationship with both physiological and pathological functions have long intrigued researchers. One of the most fascinating and important areas within this domain is cognitive function. Given that a substantial number of studies, especially interventional ones, have been conducted on animal models, the findings of which are not fully generalizable to humans and may therefore be misinterpreted, the purpose of this study is to synthesize evidence from the most recent human research. Current evidence indicates that the gut microbiota is linked to cognitive function in both healthy and diseased states, with numerous studies suggesting a potential causal relationship between the two. Although the majority of these studies associate changes in cognitive function with differences in the composition of the gut microbiota, some findings also indicate an inverse relationship. Full article

Background/Objectives: Recent public and scientific discussions have raised concerns about a possible link between prenatal paracetamol exposure and autism spectrum disorder (ASD). However, pharmacovigilance-based evidence remains scarce, and the role of reporting bias has not been systematically assessed. This study aimed to characterize ASD-related adverse event reports involving paracetamol in the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS) and to evaluate potential disproportionality signals, considering demographic, temporal, and geographic patterns. Methods: FAERS data from January 2010 to September 2025 were screened for reports listing paracetamol as the suspect drug and ASD-related Preferred Terms. After excluding duplicates and concomitant drugs, 1776 unique cases were analyzed. Patient demographics, reporter type, and country of origin were summarized descriptively. Disproportionality was calculated using four algorithms: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Information Component (IC), and Empirical Bayes Geometric Mean (EBGM). Results: Among 172,129 paracetamol-associated reports, 1776 (1.03%) included ASD-related terms. All were classified as serious and mostly submitted by consumers (98.6%). Gender was available in 47.7% of cases, showing male predominance (68.8%). Most reports referred to fetal exposure during pregnancy. Nearly all originated from the United States (98.4%). A marked rise was observed after 2022, with 562 reports in 2023 and 1051 in 2025. Disproportionality analyses revealed consistently elevated signals (ROR = 69.8, PRR = 69.2, IC025 = 5.60, EB05 = 48.3). Conclusions: The strong disproportionality signal likely reflects increased public attention and reporting dynamics rather than a causal association. Further integration of pharmacovigilance and epidemiologic data is warranted to clarify the clinical significance of these findings. Full article

Multi-radar systems can significantly improve tracking robustness and accuracy, but practical deployments are challenged by asynchronous sensing timestamps across distributed platforms and by limited communication bandwidth. This paper proposes a communication-efficient asynchronous track fusion framework based on state and covariance projection. Each radar performs local Kalman filtering and transmits only a compact track message consisting of the posterior state estimate, the associated error covariance, and a timestamp. At the fusion center, a causal reference time is chosen as the latest received timestamp, and all tracks are projected to this common time using a hybrid constant-acceleration (CA)/constant-velocity (CV) motion model with appropriately discretized process noise, followed by information-form (inverse-covariance) fusion. Under standard linear-Gaussian assumptions, the fusion rule is minimum mean square error (MMSE)-optimal when the projected estimation errors are approximately independent. We also analyze the computational complexity and the communication payload of the proposed procedure. Monte Carlo simulations with five heterogeneous radars and random inter-radar time offsets up to 37.5 ms over 100 runs show that the proposed fusion reduces the steady-state range root mean square error (RMSE) by about 66% and the radial-velocity RMSE by about 31% relative to the average single-radar tracker, while maintaining statistical consistency as verified by the normalized estimation error squared (NEES). These results indicate that projection-based track fusion provides an effective accuracy–communication trade-off for asynchronous multi-radar tracking. Full article

Hyperspectral image (HSI) classification is pivotal in remote sensing, yet deep learning models, particularly Transformers, remain susceptible to spurious spectral–spatial correlations and suffer from limited interpretability. These issues stem from their inability to model the underlying causal structure in high-dimensional data. This paper introduces the Causal Attention Transformer (CAT), a novel architecture that integrates causal inference with a hierarchical CNN-Transformer backbone to address these limitations. CAT incorporates three key modules: (1) a Causal Attention Mechanism that enforces temporal and spatial causality via triangular masking and axial decomposition to eliminate spurious dependencies; (2) a Dual-Path Hierarchical Fusion module that adaptively integrates spectral and spatial causal features using learnable gating; and (3) a Linearized Causal Attention module that reduces the computational complexity from $O\left(N^2\right)$ to $O\left(N\right)$ via kernelized cumulative summation, enabling scalable high-resolution HSI processing. Extensive experiments on three benchmark datasets (Indian Pines, Pavia University, Houston2013) demonstrate that CAT achieves state-of-the-art performance, outperforming leading CNN and Transformer models in both accuracy and robustness. Furthermore, CAT provides inherently interpretable spectral–spatial causal maps, offering valuable insights for reliable remote sensing analysis. Full article

In GPS-denied environments, the spatio-temporal coupling of errors caused by dynamic network topologies poses a fundamental challenge to cooperative localization, presenting existing methods with a dilemma: approaches pursuing global optimization lack dynamic adaptability, while those focusing on local adaptation struggle to guarantee global convergence. To address this challenge, this paper proposes ST-DCL, a cooperative localization framework based on a novel principle of closed-loop spatio-temporal decoupling. The core of ST-DCL comprises two modules: a Dynamic Weighted Multidimensional Scaling (DW-MDS) optimizer, responsible for providing a globally consistent coarse estimate with provable convergence, and a specially designed Spatio-Temporal Graph Neural Network (ST-GNN) corrector, tasked with compensating for local nonlinear errors. The DW-MDS effectively suppresses interference from historical errors via an adaptive sliding window and confidence weights derived from our error propagation model. The key innovation of the ST-GNN lies in its two newly designed components: a Dynamic Topological Attention Module for actively modulating neighbor aggregation to inhibit spatial error diffusion, and a Dilated Causal Convolution Module for modeling long-term temporal dependencies to curb error accumulation. These two modules form a closed loop via a confidence feedback mechanism, working in synergy to achieve continuous error suppression. Theoretical analysis indicates that the framework exhibits bounded-error convergence under dynamic topologies. In simulations involving 200 nodes, velocities up to 50 m/s, and 15% NLOS links, the ST-DCL achieves a normalized root mean square error (NRMSE) of 0.0068, representing a 21% performance improvement over state-of-the-art methods. The practical efficacy and real-time capability are further validated through real-world flight experiments with a 10-UAV swarm in complex, GPS-denied scenarios. Full article