Search Results (16,767)

Objective: To analyze the perceived facilitators and barriers promoting and hindering the clinical application of the best evidence of clean intermittent catheterization (CIC) in patients after radical hysterectomy (RH). Methods: This study employed a convergent parallel mixed-methods design. Participants included patients undergoing CIC after RH, medical and nursing practitioners and managers in the gynecological department and outpatient clinics at a tertiary-level women’s and children’s hospital in Chengdu. They were included in both components separately. Interview data were managed using Nvivo 11.0 software and analyzed through directed content analysis. Quantitative data were analyzed using SPSS 29.0 statistical software. Results: A questionnaire survey was conducted among 156 healthcare providers and 300 patients. Qualitative interviews were conducted with 11 healthcare workers and 12 patients. At the evidence itself level, evidence meeting clinical needs and evidence lacking practical applicability, respectively, promoted and hindered clinical implementation of the best evidence. At the potential adopters’ level, healthcare professionals’ insufficient professional competence, low willingness to promote implementation, numerous concerns, and lack of autonomy and awareness regarding the importance of the task were significant barriers, but they maintained an overall positive attitude toward the application. At the practical environment level, patient-related perceived barriers predominantly hindered evidence implementation. Additionally, a supportive practice atmosphere, economic feasibility, and talent development opportunities served as key facilitators. However, existing nursing practice content and workflows directly impacted evidence adoption. Conclusions: The promotion and barriers to the clinical application of the best evidence for CIC in RH postoperative patients are multifaceted. Targeted intervention strategies must be developed to facilitate the effective translation of evidence into clinical practice. Full article

Accurate multiphase flow measurement in horizontal wells is fundamentally challenged by the antagonistic electrical responses of water and gas: Electrical Resistance Tomography (ERT) loses sensitivity to thin liquid films, while Electrical Capacitance Tomography (ECT) suffers signal saturation in conductive water, preventing either modality from covering the full operating envelope alone. This study proposes a physics-guided hybrid modeling framework that integrates multi-modal ERT/ECT sensing to achieve high-precision flowrate inversion. The framework utilizes a corrected multi-modal fusion algorithm, achieving a liquid holdup MAPE of 2.5 ± 0.5% representing a nearly two-fold improvement over the best single-modality system (Direct ERT, 4.5%). For velocity estimation, an optimized cross-correlation method yields results with ± 3.0% error, incorporating multi-sensor and multi-sequence fusion. A key finding is that deep neural networks exhibit Architectural Phase Specialization: multi-branch architectures (MB-DNN) perform strongly on localized, heterogeneous liquid structures (2.0% liquid error), whereas fully-connected architectures (FC-DNN) excel at capturing the global patterns of the continuous gas core (1.2% gas error). By hybridizing a calibrated drift-flux physical model with these phase-specialized DNNs, the framework achieves overall averaged errors of 1.8% for gas and 1.5% for liquid across the full experimental envelope. The proposed framework was evaluated on 444,313 experimental samples and subsequently validated in a three-month industrial trial at the Puguang gas field under extreme conditions (26 MPa, 80 °C), where it maintained a prediction error of ± 2.3%. This work establishes a scalable, physically consistent paradigm for intelligent hydrocarbon production monitoring. Full article

Changes in human attention, workload, or alertness over time can affect task performance and may even increase the risk of injury. Detecting these changes in real time can be beneficial in improving system performance and safety. We reviewed 27 studies that developed models to sense physiological signals, classify one’s cognitive state, and deliver automated intervention. Interventions included providing real-time feedback, adjusting the task’s difficulty, or modifying automation levels across driving, education, rehabilitation, and human–robot collaboration applications. The findings showed that electroencephalography (EEG) sensors were used in 70% of studies, with attention (56%) and mental workload (26%) considered as the most targeted cognitive states. Within-subject classification reached 81.85–95.81% for multi-class tasks in laboratory settings. The most common interventions included neurofeedback display (30%) and task difficulty adjustment (19%), while automation adjustment was less frequent (11%). Only 33% of studies mentioned a latency of 15 milliseconds to 2.5 s, and all systems operated reactively by detecting cognitive states after their onset rather than anticipating them. The provided recommendations focus on the detection of multiple interacting cognitive states and predictive cognitive state trajectories. This review presents key directions for future research and provides a foundation for designing more effective cognitive state adaptive systems. Full article

Ultra-high-voltage direct-current (UHVDC) transmission systems impose stringent requirements on the reliability of insulation materials used in converter transformer bushings. Epoxy resin systems are key insulating materials in resin-impregnated paper (RIP) capacitor bushings, and their processing characteristics, curing behavior, and electrical properties directly affect bushing performance. In this study, two epoxy insulation systems used for resin-impregnated paper (RIP) bushings, namely the imported Araldite LY1564/Aradur 3486 system and the domestic EP-2020/CA-3015 system, were systematically investigated through viscosity, curing, and electrical property tests. The results show that the viscosities of both resins decreased significantly with increasing temperature. At 60 °C, the viscosities of Resin A and Resin B were 151.6 mPa·s and 156.3 mPa·s, respectively. The mixed resin–hardener systems exhibited similar viscosity evolution and comparable pot life characteristics. DSC measurements revealed two-stage curing reactions for both materials, with first exothermic peak temperatures of 65.4 °C and 96.3 °C and second peak temperatures of 269.3 °C and 269.8 °C for Materials A and B, respectively. Electrical testing demonstrated that both materials exhibited similar temperature-dependent dielectric and resistivity behavior, with dielectric loss increasing at elevated temperatures and resistivity decreasing as temperature increased. The volume resistivity trends and dielectric characteristics of the two materials remained highly consistent throughout the investigated temperature range. The results indicate that Material B exhibits processing performance, curing characteristics, and electrical insulation properties comparable to those of Material A. Therefore, Material B demonstrates strong potential for application in UHVDC RIP bushing insulation systems and provides a promising alternative for the localization of key insulating materials. Full article

Lithium has become one of the key raw materials for the energy transition due to the central role of lithium-ion batteries in electromobility, energy storage, and the integration of renewable energy sources. However, the rapid increase in demand reveals growing environmental, social, geopolitical, and market tensions. The aim of the paper is a critical synthesis of global lithium utilization from the perspective of challenges, technological innovations, and integrative strategies supporting a more sustainable material–energy system. A broad, systematic literature review covering the entire value chain was applied: resources, extraction, processing, end-use applications, second life of batteries, recycling, and governance. The analysis shows that the strategic importance of lithium arises from the increasing demand pressure from electric vehicles and stationary storage, while the sustainability of the current model is constrained by supply concentration, uneven control over downstream stages, the water–carbon footprint of extraction and processing, social conflicts, and incomplete integration of secondary loops. At the same time, innovations such as direct lithium extraction (DLE), recovery from geothermal brines, design for recycling, second life, and battery passports can partially alleviate these tensions, but they do not eliminate the need for primary supply in the short term. The conclusion of the work is that sustainable global lithium utilization requires simultaneous diversification of sources, development of circular value chains, and multi-level governance integrating resource security, environmental efficiency, and social legitimacy. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

Chronic wounds represent a substantial and growing clinical burden, yet durable healing remains difficult to achieve in a large proportion of patients. The skin microbiome plays a central role in this challenge: in healthy tissue, resident microorganisms support barrier integrity and calibrate immune responses, whereas in chronic wounds, community disruption—often combined with persistent biofilm formation—drives non-resolving inflammation, impairs re-epithelialisation, and increases antimicrobial tolerance. As antibiotic resistance escalates, these features strengthen the rationale for microbiome-directed strategies that target wound ecology while reducing reliance on conventional antimicrobials. Current evidence is still dominated by mechanistic and preclinical studies, with only early clinical signals for selected approaches; therefore, next-generation probiotics, including Lactiplantibacillus/Lactobacillus spp., as well as defined prebiotic and postbiotic formulations, should be interpreted as promising adjuncts rather than clinically established therapies. Causal mechanisms, optimal formulations, reproducibility, and patient-level determinants of response remain insufficiently defined, representing a critical knowledge gap that limits translation. Here, we synthesise current evidence linking microbial ecology to key wound-healing pathways and propose a precision framework that integrates metagenomics, transcriptomics, metabolomics, and spatial profiling to map host–microbe interactions, identify predictive biomarkers, and guide stratified therapy. We further highlight combinatorial approaches pairing ecological engineering with biofilm-disruptive materials and immune-modulatory molecules. Realising the potential of these interventions will require mechanism-resolved clinical trials, standardised outcome frameworks, and patient stratification tools—advances that could improve chronic wound management while reducing selective pressure for antimicrobial resistance. Full article

Hookah smoking has become a persistent social phenomenon among young adults in the United States, despite decades of tobacco control efforts. Moreover, hookah smoking presents unique public health challenges due to its strong social appeal, cultural relevance, and widespread use in urban centers and college communities. This paper provides a broad review of recent trends, health implications, regulatory responses, and sociocultural factors influencing hookah smoking. A narrative literature review was conducted using peer-reviewed articles and grey literature sources published between 2019–2025. Electronic databases included PubMed, CINAHL, and Google Scholar, along with reports from professional organizations and government agencies including CDC, FDA, WHO, and health department agencies, to review current public health recommendations and practice guidelines. Review materials were selected that focus on prevalence, health implications, and regulations of hookah smoking. Findings were synthesized to identify key applications, challenges and concerns, and future directions. This review provided a rapid and broad review of the current trends, health implications, regulatory responses, and sociocultural factors of hookah smoking among young adults in the United States. Interdisciplinary research and policy innovation are necessary to address the ongoing public health burden of hookah smoking among young adults in the United States. Full article

The rapid digitalization of energy systems and the increasing integration of distributed energy resources, renewable energy technologies, and prosumer-oriented infrastructures have accelerated the development of IoT-enabled Smart Grids as a foundation for intelligent and adaptive energy management. Modern Smart Grids increasingly depend on the coordinated interaction of IoT architectures, artificial intelligence, distributed analytics, and decentralized control mechanisms to ensure reliability, scalability, and real-time operational flexibility. Despite extensive research activity, existing studies remain predominantly technology-centric, focusing on isolated architectural layers or individual intelligent methods without providing a unified system-level perspective on their coordinated operation and interoperability. This article presents a system-level integrative review and challenge-oriented comparative synthesis of intelligent operational and architectural foundations of IoT-enabled Smart Grids. The study analyzes data-driven, model-driven, knowledge-driven, agent-based, and hybrid-oriented intelligent paradigms within multi-layer IoT energy infrastructures. In addition, the research establishes a cross-layer mapping between Smart Grid operational challenges, enabling technologies, and corresponding analytical approaches while identifying interoperability constraints, scalability limitations, and coordination challenges associated with decentralized energy ecosystems. The conducted synthesis demonstrates that hybrid-oriented intelligent approaches represent the most promising direction for future Smart Grid evolution due to their ability to integrate AI, ML, digital twins, semantic reasoning, and decentralized multi-agent coordination within unified IoT architectures. The conducted comparative synthesis identifies the ongoing transition from isolated intelligent solutions toward integrated hybrid cyber–physical energy ecosystems and highlights key characteristics of future adaptive, interoperable, scalable, and explainable Smart Grid architectures. Full article

Risk perception and value assessment are key drivers of purchase intention. However, the literature lacks a consensus on how and when risk perception and perceived value impact consumers’ purchase intention, and their relationship remains unclear. To solve this gap, mechanisms of consumer purchase intentions must be elucidated. We conducted a systematic meta-analysis to examine the relationships and factors moderating it, and used meta-analytic structural equation modeling (MASEM) to reveal the mechanism and boundaries. Forty-four studies (N = 21,370) were included to examine how risk perception and perceived value impact consumer purchase intention, showing that consumer purchase intention is affected positively by perceived value and negatively by risk perception. Risk perception and perceived value exhibit mutual interaction effects. Perceived value has a stronger relationship with consumer purchase intention than risk perception. In the moderator’s analysis, the effects of perceived value on consumers’ risk perception and of perceived risk on perceived value and purchase intention are stronger when consumers come from developing (vs. developed) countries. Impacts of perceived value on consumer purchase intention and risk perception and of risk perception on perceived value and purchase intention are stronger when consumers are non-students (vs. students). When analyzing the three models’ mechanisms of action, Model 1 better explained consumer intention’s boundaries and impact mechanisms. To our knowledge, this is the first meta-analytic study summarizing how risk perception and perceived value impact consumers’ purchase intention, revealing the mechanism and boundaries of consumer behavior and illuminating a forward-looking new perspective outlining research directions. Full article

Romans 8 contains Paul’s most concentrated reflection on the human person transformed by the Spirit, and at its center stands the rare term φρόνημα. Appearing only four times in the New Testament, all in Romans 8 (6 [x2], 7, 27), φρόνημα is commonly translated as “mindset”, “disposition”, or “attitude”. These translations are useful but insufficient. They risk reducing Paul’s term to a merely psychological state, whereas in Romans 8 φρόνημα names the deep orientation of the human person before God. This study argues that φρόνημα functions as an ethical and anthropological key: it expresses the inner direction of a life either determined by σάρξ or shaped by πνεῦμα. Through linguistic analysis, rhetorical exegesis of Rom 8:5–8 and 8:27, and dialogue with Jewish and Greco-Roman moral discourse, this article proposes that φρόνημα denotes the deep structure of Christian orientation: the Spirit-formed disposition from which perception, desire, obedience, prayer, suffering, and hope are reordered toward God. Full article

3D magnetotelluric (MT) inversion based on the Gauss–Newton (GN) framework plays an important role in deep mineral exploration by imaging subsurface electrical conductivity structures. However, large-scale 3D MT inversion remains computationally expensive due to the high cost of sensitivity-matrix construction. To address this challenge, we develop a stochastic Gauss–Newton (SGN) framework that reduces computational cost through random data subsampling while preserving the practical convergence behavior of GN inversion. In the proposed framework, only a randomly selected subset of data is used to approximate the GN search direction. By exploiting a key property of MT forward modelling, namely that responses at all receivers are obtained simultaneously for each frequency, the line search is performed using the full dataset, ensuring stable convergence of the inversion process. The SGN framework is validated using both a synthetic multiblock model and a field dataset from the Akebasitao area in Xinjiang, China. The recovered models remain highly consistent with those obtained using conventional full-data Gauss–Newton inversion across a wide range of sampling ratios. For the synthetic example, reducing the sampling ratio from 100% to 10% decreases peak memory consumption from approximately 433 GB to 242 GB and reduces runtime from 86.8 h to 23.9 h while maintaining comparable inversion quality. Similar computational savings are achieved for the field-data inversion. The field application successfully recovers the major conductive structures along the margins of the intrusion that are associated with hydrothermal alteration and fluid activity, highlighting the capability of SGN to delineate geologically meaningful targets relevant to deep mineral exploration. These results demonstrate that SGN provides an efficient and scalable approach for large-scale 3D MT inversion. Full article

Inflammatory bowel disease (IBD) poses a significant global health burden with rising incidence, particularly in Asia. This study employed an integrative network pharmacology approach combined with molecular docking to elucidate the therapeutic mechanism of ganoderic acid A (GAA) against IBD. Potential GAA targets were retrieved from pharmacogenomic databases, while IBD-related genes were curated from OMIM and GeneCards databases. Weighted gene co-expression network analysis of IBD transcriptomic datasets (GSE38713, GSE126124) identified disease-associated modules, with the yellow module exhibiting the strongest positive correlation. Functional enrichment analyses demonstrated significant involvement of overlapping targets in lipid metabolism, the inflammatory response, and the mitogen-activated protein kinase (MAPK) signaling cascade pathway. We identified 14 IBD-GAA-ferroptosis-related genes and 54 key module genes. Intersection analysis revealed 5 overlapping targets, including tumor necrosis factor-α(TNF-α), peroxisome proliferators-activated receptor γ (PPARγ), MAPK14, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic α (PIK3CA), and Caspase 3 (CASP3). Molecular docking confirmed high-affinity binding of GAA to these targets, with binding energies ranging from −7.3 to −10 kcal/mol. Crucially, experimental evaluation demonstrated the pivotal role of GAA in alleviating disease pathology. GAA treatment suppressed the significantly elevated levels of TNF-α and p-MAPK14 in the organoids using a cytokine/LPS-induced IBD model. These findings collectively suggest a potential involvement of GAA in pathways associated with ferroptosis regulation, although direct experimental evidence for ferroptosis markers remains to be established. The observed multi-target effects on immune regulation and cellular proliferation/differentiation provide a foundation for further mechanistic investigation. Full article

The fractional Whitham–Broer–Kaup model governs nonlinear wave propagation in memory-dependent media, including porous structures, viscoelastic fluids, and irregular seabeds, yet the full dynamical spectrum from quasi-periodicity to deterministic chaos, the role of stochastic forcing, and reliable identification from noisy data remains insufficiently explored, particularly how the fractional order $\beta$ influences these regimes. This study addresses these gaps through a comprehensive, multi-method dynamical analysis of a representative nonlinear oscillator embodying key FWBK features. Three-dimensional attractor visualizations, return maps, and surrogate data tests demonstrate a transition from quasi-periodic toroidal attractors to fully developed chaos via torus breakdown, confirming that observed complexity originates from deterministic nonlinearity. Poincaré sections reveal multistability and KAM-type structures, where coexisting attractors depend on initial conditions, while increasing noise progressively disrupts coherent dynamics. The OGY control method effectively stabilizes unstable periodic orbits across chaotic regimes with minimal perturbation, and Lyapunov analysis indicates that stochastic forcing attenuates chaos while enhancing dissipation. The Fokker–Planck framework shows that noise reshapes probability landscapes, driving transitions from unimodal to bimodal distributions. Comparative analysis of SINDy, JMAP and VBA highlights trade-offs in interpretability, computational efficiency, and uncertainty quantification, while an integrated Bayesian–PCE–Sobol approach quantifies parametric uncertainty and reveals time-dependent sensitivity variations. Additionally, the overlapping of soliton solutions extracted via the enhanced modified Sardar sub-equation method reveals structural relationships among soliton families and their stability under interaction. Soliton branches that maintain high overlap under noise correspond to stable regimes, while those losing coherence indicate the onset of chaos. Furthermore, while the reduced dynamics in $\eta$-space are independent of $\beta$, the fractional order controls spatial compression and temporal scaling in physical coordinates, directly influencing observable wave localization. These results imply that fractional effects can modify chaos transitions, support controllability through OGY, and influence noise–instability interactions depending on $\beta$. This framework provides a robust, transferable methodology for analyzing and controlling nonlinear oscillatory systems under deterministic and stochastic conditions, with direct applications to FWBK-based models in coastal engineering, fiber optics, and quantum interference systems. Full article

Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with poor survival rates of late-stage disease. While immune checkpoint blockade (ICB) therapy has transformed treatment for mismatch repair-deficient (MMRd)/microsatellite instability-high (MSI-H) tumors, most CRC cases are mismatch repair-proficient (MMRp)/microsatellite-stable (MSS) and derive little to no benefit from current immunotherapy regimens. Tumor-associated macrophages (TAMs) constitute a significant component of the tumor microenvironment (TME) and exhibit a phenotypic gradient between pro-inflammatory (M1-like) and anti-inflammatory, immunosuppressive (M2-like) states. Although their polarization status is increasingly recognized as a key modulator of immunotherapy efficacy in CRC, a comprehensive synthesis of the literature regarding macrophage polarization and its relevance to improving CRC immunotherapy remains lacking. Methods: A systematic literature search was conducted across PubMed, EMBASE, and ScienceDirect from inception to December 2025 using terms encompassing macrophages, immunotherapy, immune checkpoint expression, colorectal cancer, and microsatellite stability status. Title, abstract, and full-text screening were performed independently by multiple authors. Sixty-five studies were included following PRISMA guidelines. The protocol was prospectively registered on PROSPERO (ID: CRD420251244320). Results: Three key themes were identified: (1) macrophage-mediated mechanisms of resistance to ICB, including M2 polarization driven by the PI3Kγ, STAT3, mTOR, and SIRT-1 axes, immunosuppressive cytokine production (IL-10, TGF-β), and altered immune checkpoint ligand expression; (2) macrophage polarization status and associated biomarkers as prognostic indicators of therapeutic response; (3) emerging macrophage-targeted therapeutic strategies in ongoing clinical trials, including CSF1R inhibitors, CD40 agonists, CD47/SIRPα blockade, and STING agonists. Conclusions: TAM polarization is a critical determinant of immunotherapy resistance and patient prognosis in CRC. Macrophage-targeted strategies, particularly M2-to-M1 repolarization approaches used in combination with existing ICB regimens, represent a promising avenue for expanding immunotherapy efficacy beyond MSI-H disease. Further translational research and randomized controlled trials are needed to validate these targets clinically. Full article