Search Results (9,458)

Aquaporin 1 (AQP1) is a transmembrane protein that acts as a highly selective channel for the rapid passage of water across cell membranes, driven by osmotic gradients. The narrowest part of the water channel pore—the selectivity filter (SF)—plays a key role in ensuring selective and efficient water transport. In this study, density functional theory (DFT) at the M062X/6-311+G(d,p) level was used to identify the preferred position of the water molecule(s) inside the SF and to elucidate the forces that lead to its displacement during permeation. A systematic scan along the pore axis identified a well-defined energy minimum where a single water molecule was optimally stabilized by hydrogen bonds with SF residues. A second water molecule was introduced to study how the incoming water affects the translocation of the first water molecule. The resulting energy and force profiles reveal that the approaching water molecule gradually pushes the bound water forward, ultimately occupying its favorable binding site. These results provide an atomistic description of the positioning and displacement of water molecules in SF and offer a quantitative view of the fundamental interactions that govern water transport in AQPs. Full article

This research paper focuses on the role of average working hours (AVHs) of the labor force in explaining the variation in QOL across countries, which is an important but unexplored area in the empirical literature. Using data from 62 countries and employing several econometric techniques, we show that long AVHs are detrimental for improved QOL. The sub-sample results demonstrate that AVHs have a significant detrimental impact on the QOL of the population only in the case of developing countries. However, in the case of developed countries, the influence of AVHs is insignificant as these countries are enjoying relatively reduced AVHs as compared to developing countries. Moreover, our results indicate that the labor force participation rate, human capital, government expenditures, internet use, and electricity consumption are the main driving forces behind a better QOL both in developed and developing countries. Finally, we found evidence that trade openness is an irrelevant factor in explaining the variation in QOL as it is insignificant in most of the specifications despite possessing a positive coefficient. Full article

This study examines the relationships among firm innovativeness, digital enablement, business model innovation, and firm resilience within Saudi family firms, addressing the limited understanding of how these interrelated capabilities contribute to family firm resilience amid rapid digital transformation. Grounded in the Dynamic Capabilities View, the study proposes a sequential mediation model linking these constructs. Data were gathered from 167 family firms in Saudi Arabia and were analyzed using exploratory factor analysis and structural equation modeling (SEM). The findings show positive links between innovativeness, digital enablement, and business model innovation. Digital enablement is also related to business model innovation and firm resilience, and business model innovation positively relates to firm resilience. However, innovativeness does not directly relate to resilience, emphasizing the mediating roles of digital enablement and business model innovation. The results reveal that generational involvement significantly moderates role by weakening the direct effect on business model innovation while strengthening the role of digital enablement, suggesting nuanced family governance in digital transformation. This study extends the dynamic capabilities framework to family firm resilience in emerging economies and offers guidance for managers on fostering innovation culture and digital enablement. Limitations include the cross-sectional design and the focus on the Saudi-specific context. Full article

Urban vitality is a crucial indicator of a city’s sustainable development and the quality of life of its residents. Investigating the spatiotemporal patterns and influencing mechanisms of urban vitality is essential for optimizing the built-environment and improving governance. Using the central urban area of Guiyang, China, as a case study, this research integrates multi-source urban sensing data to investigate the spatiotemporal patterns of urban vitality and their driving factors. Geographically weighted regression (GWR) and machine learning combined with SHapley Additive exPlanations (SHAP) are applied to capture spatial heterogeneity, nonlinear relationships, and threshold effects among influencing variables. Results show that urban vitality exhibits a Y-shaped, single-core, multi-center, and clustered spatial configuration, with slightly higher intensity on weekdays and similar diurnal rhythms across weekdays and weekends. The effects of influencing factors display strong spatial non-stationarity, characterized by a concentric gradient radiating outward from the historic Laocheng core. Building density (BD), residential point density (RED), normalized difference vegetation index (NDVI), and road density (RD) emerge as the dominant contributors to urban vitality, while topographic conditions play a relatively minor role. The relationships between key landscape and built-environment variables and urban vitality are highly nonlinear, with distinct threshold effects. By integrating spatial econometric modeling and explainable machine learning, this study advances methodological approaches for urban vitality research and provides practical insights for landscape-oriented urban planning and human-centered spatial design. Full article

A deep understanding of hydrogen diffusion in metals under stress is crucial for revealing the mechanism of hydrogen embrittlement. While the effects of isotropic and uniaxial stress have been studied, the atomic-scale mechanism under a pure biaxial stress state remains unclear. This work employs molecular dynamics simulations to investigate hydrogen diffusion in α-iron under controlled biaxial stress. The results show that biaxial stress influences diffusion indirectly by altering the lattice geometry and thus the migration energy barrier. It is found that the diffusion path is governed by the direction of the minimum principal strain, while the diffusion rate is controlled by the maximum tensile principal strain, with which it exhibits an approximately exponential relationship. These insights clarify the distinct roles of different strain components, providing a refined framework for understanding hydrogen behavior under complex stress states and guiding the design of hydrogen-resistant materials. Full article

Background: Large language models (LLMs) are becoming progressively integrated into clinical practice; however, their role in cardiovascular (CV) prevention remains unclear. This review synthesizes current evidence on LLM applications in preventive cardiology and proposes a governance framework for their safe translation into practice. Methods: We conducted a comprehensive narrative review of literature published between January 2015 and November 2025. Evidence was synthesized across three functional domains: (1) patient applications for health literacy and behavior change; (2) clinician applications for decision support and workflow efficiency; and (3) system applications for automated data extraction, registry construction, and quality surveillance. Results: Evidence suggests that while LLMs generate empathetic, guideline-concordant patient education, they lack the nuance required for unsupervised, personalized advice. For clinicians, LLMs effectively summarize clinical notes and draft documentation but remain unreliable for deterministic risk calculations and autonomous decision-making. System-facing applications demonstrate potential for automated phenotyping and multimodal risk prediction. However, safe deployment is constrained by hallucinations, temporal obsolescence, automation bias, and data privacy concerns. Conclusions: LLMs could help mitigate structural barriers in CV prevention but should presently be deployed only as supervised “reasoning engines” that augment, rather than replace, clinician judgment. To guide the transition from in silico performance to bedside practice, we propose the C.A.R.D.I.O. framework (Clinical validation, Auditability, Risk stratification, Data privacy, Integration, and Ongoing vigilance) as a roadmap for responsible integration. Full article

The rapid expansion of the digital economy and advances in artificial intelligence have elevated digital governance to a pivotal role in promoting environmental sustainability. Using data from the China Family Panel Studies, this study constructs a household-level indicator of the digital divide and systematically investigates its effects on household carbon emissions through three key mechanisms: consumption hypersensitivity, green technology adoption, and environmental awareness. The empirical findings demonstrate that the digital divide significantly increases household carbon emissions. Specifically, a one-unit increase in the digital divide is associated with an average rise of approximately 38.6% in household carbon emissions. Importantly, this result remains robust across a range of robustness checks and endogeneity controls. Further mechanism analysis reveals that the digital divide amplifies households’ sensitivity to consumption, diminishes their likelihood of adopting green technologies, and weakens their environmental awareness, thereby leading to an increase in household carbon emissions. Heterogeneity analysis indicates that these negative effects are particularly pronounced in regions with underdeveloped digital inclusive finance, among households headed by middle-aged and older individuals, and within populations with lower educational attainment. Based on these findings, policy initiatives should focus on improving the accessibility and inclusiveness of digital infrastructure, developing tiered frameworks to support green behavioral transformation and capacity building, and strengthening green finance initiatives alongside offline support mechanisms for digitally disadvantaged groups. Together, these measures can help bridge the digital divide and foster a more equitable, inclusive, and sustainable transition toward a low-carbon society. Full article

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition. Full article

The environmental and social crises affecting global agri-food systems highlight the need for a profound transformation of production models and their territorial relations. In this context, agroecology, understood as science, practice, and movement, has emerged as a paradigm capable of integrating ecological sustainability, social equity, and community participation. Within this framework, the work carried out by CREA in the AgrEcoMed project (new agroecological approach for soil fertility and biodiversity restoration to improve economic and social resilience of Mediterranean farming systems), funded by the PRIMA programme, investigates agroecology as a social and political process of territorial regeneration. This process is grounded in co-design with local stakeholders, collective learning, and the construction of multi-actor networks for agroecology in the Mediterranean. The Manifesto functions as a tool for participatory governance and value convergence, aiming to consolidate a shared vision for the Mediterranean agroecological transition. The article examines, through an analysis of the existing literature, the role of agroecological networks and empirically examines the function of the collective co-creation of the Manifesto as a tool for social innovation. The methodology is based on a participatory action-research approach that used local focus groups, World Café, and thematic analysis to identify the needs of the companies involved. The results highlight the formation of a multi-actor network currently comprising around 90 members and confirm the effectiveness of the Manifesto as a boundary object for horizontal governance. This demonstrates how sustainability can emerge from dialogue, cooperation, and the co-production of knowledge among local actors. Full article

Dilation matrices are important in multiple subdivision and multiple multiresolution analysis, as they govern the process of data refinement and play a crucial role in capturing directional features. One common limitation in the existing methods is the relatively large determinant of their dilation matrices, leading to high computational and storage costs. To address this issue, this paper proposes a novel family of pairs of directional dilation matrices with determinant 3. Such dilation matrices satisfy the joint expansion property and directional sensitivity. The joint expansion property is verified via the joint spectral radius, while by connecting the action of the matrices to certain elliptic elements of $PSL(2,\mathbb{R})$, their directional adaptability can be established. Compared to most of the existing dilation matrices, the proposed ones achieve a balance between determinant and directional adaptability and provide a new insight into the construction of directional dilation matrices. This makes them suitable for addressing practical anisotropic problems. Full article

The micromechanisms “slip transfer, slip irreversibility, microplasticity, and fatigue cracking” in titanium alloys are reviewed, with a special emphasis on near-α and α + β alloys. As the interplay between slip activity, microplasticity, and fatigue cracking governs both the microscale and macroscale mechanical response, we reveal how the slip irreversibility and localized dislocation activity at the grain boundaries (GBs) and α/β interfaces generate dislocation pile-ups and strain localization, subsequently driving fatigue crack initiation and propagation. The review highlights the favorable crack initiation along basal planes and the roles of α grain orientations, slip transfer barriers, and the β phase in governing fatigue cracking, while addressing unresolved questions about localized interactions and texture effects. It also explores the complex interactions that govern the effects of microstructures, textures, and defects on fatigue cracking. Ultimately, the review provides a unified framework for linking slip events to microplasticity and to fatigue failure, offering actionable insights for alloy design and fatigue prediction. Full article

Glycosaminoglycans (GAGs) are highly negatively charged polysaccharides that play essential roles in numerous physiological and pathological processes through their interactions with proteins. These interactions govern cellular signaling, inflammation, coagulation, and recognition. Surface Plasmon Resonance (SPR) has emerged as a key biophysical technique for label-free, real-time characterization of biomolecular interactions, offering insights into binding kinetics, affinity, and specificity. SPR-based approaches to glycosaminoglycan–protein interaction studies offer powerful tools for elucidating the roles of GAGs in a wide range of physiological and pathological processes. In this review, we systematically discuss experimental strategies, data analysis methods, and representative applications of SPR-based glycosaminoglycan–protein interactions. Special attention is given to the challenges associated with GAG heterogeneity and immobilization, as well as recent technological advances that enhance sensitivity and throughput. To our knowledge, this review represents one of the first systematic and up-to-date summaries specifically focused on recent advances in applying SPR to the study of glycosaminoglycan–protein interactions. Full article

Rheology plays an important role in the 3D concrete printing technology, because it directly governs the flowability and shape retention of the material, impacting both the printing process and the final quality of the obtained structure. Local raw materials such as Portland cement, washed sand, and tap water were used for the preparation of 3D-printed concrete mixtures. The solid-state polycarboxylate ether with an anti-foaming agent was used as superplasticizer. The Portland cement was partially replaced (by volume) with a natural zeolite additive in amounts ranging from 0% to 9% in 3D-printed concrete mixtures. A rotational rheometer with coaxial cylinders was used in this research for the determination of rheological characteristics of prepared 3D-printed concrete mixtures. The Herschel–Buckley model was used to approximate experimental flow curves and assess rheological parameters such as yield stress, plastic viscosity, and shear-thinning/thickening index. The additional experiments and calculations, such as water bleeding test and evaluation of the carbon footprint of 3D-printed concrete mixtures, were performed in this work. The replacement of Portland cement with natural zeolite additive positively influenced rheological and stability-related properties of 3D-printed concrete mixtures. Natural zeolite additive consistently reduced water bleeding, enhanced yield stress under increasing shear rates, and lowered plastic viscosity, thereby improving flowability and mixture transportation during the 3D printing process. As the shear-thinning/thickening index remained stable (indicating non-thixotropic behavior in most cases), higher amounts of natural zeolite additive introduced slight thixotropy (especially under decreased shear rates). These changes contributed to better shape retention, layer stability, and the ability to print taller and narrower structures without collapse, making natural zeolite additive suitable for use in the optimized processes of 3D concrete printing. A significant decrease in total carbon footprint (from 3% to 19%) was observed in 3D-printed concrete mixtures with an increase in the mentioned amounts of natural zeolite additive, compared to the mixture without this additive. Full article

To enhance PharmD student leadership and advocacy skills, combat the paucity of trained pre-health advisors for pharmacy admissions, augment community relationships, and increase pharmacy admissions volume, we designed, implemented, and assessed PAALS, a Pre-health Academic Advising and Leadership System. PAALS was grounded in Astin’s Theory of Student Involvement and evaluated using the RE-AIM implementation science framework. RE-AIM measured outcomes across Reach, Effectiveness, Adoption, Implementation, and Maintenance as indicators of PAALS’s scale, fidelity, sustainability, and institutional embedding. Analysis of PAALS using the RE-AIM framework demonstrated the following outcomes: (1) Reach: 42 P1-P3 PharmD students participated as mentors; external partnerships expanded from 2 to 8 regional high schools and community programs; and more than 25 mentored learners successfully matriculated into the PharmD program. (2) Effectiveness: students enacted sustained leadership, advocacy, and mentoring roles. (3) Adoption: voluntary uptake of mentoring and governance roles by PharmD students occurred with repeated engagement by external partner institutions. (4) Implementation: Core program components were delivered consistently using existing institutional resources. (5) Maintenance: PAALS remained operational across five academic years despite student turnover, with leadership succession and institutional embedding sustained across cohorts. Our findings demonstrate that student-led advising and advocacy ecosystems address critical gaps in pharmacy-specific pre-health advising models. Full article

:The growing demand for smart electronic devices in daily life requires sustainable, renewable energy sources that reliably power portable and wearable systems. Triboelectric nanogenerators (TENGs) have emerged as a promising platform for smart textile-based energy harvesting due to their material versatility and mechanical compliance. In this work, electrospun poly (vinylidene fluoride) (PVDF) fiber mats incorporating boron nitride (BN) nanoparticles and reduced graphene oxide (rGO) were investigated to elucidate the roles of insulating and conductive nanofillers in governing the structural and electroactive properties of PVDF-based triboelectric materials. Electrospun PVDF mats containing 5 wt.% BN exhibited enhanced β-phase content (82%), attributed to the nucleating effect of BN and strong interfacial interactions between the nanofiller and the PVDF matrix. In contrast, 7 wt.% rGO demonstrated a high electroactive β-phase fraction (81%), arising from filler-induced dipole alignment and enhanced charge transport within the fibrous network. A comparative analysis of BN and rGO highlights filler-driven mechanisms influencing the electroactive phase formation and triboelectric charge generation in PVDF mats. The corresponding triboelectric power density reached 231 μWcm⁻² for the 7 wt.% rGO/PVDF and 281 μWcm⁻² for the 5 wt.% BN/PVDF-based TENGs, providing valuable insights for the rational design of high-performance, flexible triboelectric materials for wearable energy-harvesting applications. Full article