Search Results (39,715)

Physically triggered drug release is an emerging field focused on developing materials capable of modulating release kinetics in response to external stimuli. In this work, we present a strategy for the fabrication and evaluation of heat-mediated drug release from electrospun fibers composed of a polyacrylonitrile (PAN) and poly (methyl vinyl ether-alt-maleic acid) (PMA-MVE) blend, encapsulating vitamin B₁₂ (B₁₂-NFs) as a model. Following thermal treatments at 90, 120, and 180 °C, results from SEM, TGA, DSC, and FTIR confirmed that increasing the crosslinking temperature promoted the formation of a more hydrophobic matrix (contact angle > 150°), which effectively reduced spontaneous drug leakage. As a proof-of-concept, we evaluated the sensitivity of the elaborated B₁₂ to heating in aqueous media using UV-Vis spectrometry. The results indicate that the release kinetics followed a sigmoidal profile governed by the dissolution Gompertz model. This laboratory-scale evaluation establishes the fundamental mechanisms for magnetically triggered platforms based on polymeric blends, providing a robust framework for the design of remotely activated, non-invasive drug delivery platforms. Full article

Precise modeling of narrow gorges is challenging due to extreme confinement, hindering visibility and accessibility. These environments often render Global Navigation Satellite Systems (GNSS)-based positioning unfeasible, a difficulty compounded by water and dense vegetation. Consequently, multi-technique data fusion is required. This study proposes a robust methodology to generate high-resolution 3D models of such complex environments by integrating multiple aerial (e.g., Unmanned Aerial Vehicles, UAVs) and terrestrial techniques. A multi-sensor approach combined UAV-Light Detection and Ranging (LiDAR) and UAV-photogrammetry for external areas with Terrestrial laser scanning (TLS), Mobile Mapping System (MMS), and Spherical Photogrammetry (SP) for the canyon floor. Furthermore, the representativeness of these 3D models was analyzed against standard Digital Terrain Models (DTMs) for determining water height levels during flood events. A one-dimensional hydraulic (1DH) model compared the 3D mesh approach with the traditional 2.5D perspective in a challenging, narrow canyon prone to flooding. Our results show that traditional 2.5D DTMs significantly over- or underestimate water levels in narrow sections—failing to account for overhangs and vertical wall irregularities—whereas high-resolution 3D meshes provide a more realistic representation of hydraulic behavior. This work demonstrates that multi-sensor data fusion is essential for accurate flood risk management and infrastructure planning in complex fluvial environments. Full article

Background/Objectives: Acute heart failure (AHF) is a common critical condition in emergency departments, and traditional diagnostic methods have limitations, including high subjectivity and limited accuracy. This study aimed to develop an integrated machine learning model based on lung ultrasound (LUS) radiomics and clinical data for diagnosing AHF in patients presenting with acute dyspnea. Methods: A total of 301 patients were included and randomly split into training (n = 210) and testing (n = 91) sets. Using PyRadiomics 3.0, 107 radiomics features were extracted from standardized 6-zone LUS images, combined with 52 clinical features. Three random forest models were developed: clinical-only, radiomics-only, and integrated models. Results: The integrated model achieved optimal performance on the testing set with an AUC of 0.976 (95% CI: 0.950–0.994), accuracy of 90.1%, sensitivity of 91.1%, and specificity of 89.1%, significantly outperforming the radiomics model (AUC 0.940, p = 0.046) and clinical model (AUC 0.931, p = 0.111). Feature importance analysis revealed that radiomics features contributed 75.6% of the model’s predictive power, with gray level run length matrix (GLRLM) features dominating the top-ranked features. Conclusions: As a proof-of-concept study, this research demonstrates the potential value of multimodal data fusion strategies for AHF diagnosis in the emergency department; however, external validation and prospective studies are required to further confirm its clinical applicability. Full article

Background: This study examines the influence of AI-driven supply chains on the adoption of automation and robotics within Jordanian manufacturing firms, emphasizing the role of supply chain adaptability and agility as mediators and market turbulence as a moderator. Methods: Drawing on dynamic capabilities theory and institutional theory, the study develops a conceptual model and tests it using data collected from 337 managers through an online survey. The analysis was carried out through partial least squares structural equation modeling (PLS-SEM). Results: The results show that AI-driven supply chains significantly enhance both adaptability and agility. However, only agility has a direct and significant effect on the adoption of automation and robotics, while market turbulence significantly moderates the connection between supply chain adaptability and the adoption of automation and robotics, but not the relationship between agility and adoption. Conclusions: Theoretically, the study provides insight into the interplay among internal dynamic capabilities in shaping technology adoption under external uncertainty. These results provide actionable implications for managers operating in developing economies like Jordan, highlighting the significance of building agile capabilities and adopting AI technologies to support innovation. The study is limited by its focus on a single country and sector; future research should explore other industries and incorporate additional moderating or mediating variables. Full article

Frugal innovation is an important area of academic research for organizations seeking to create value with limited resources. However, one important theoretical issue remains unclear: the available literature does not clarify how environmental orientations produce frugal innovation outcomes or how leadership capabilities determine this relationship within the resource-based view. Specifically, the processes by which both external and internal environmental orientations contribute to frugal innovation in resource-constrained environments, and the way transformational leadership is viewed as an intangible strategic resource, are not developed in theory. Therefore, the objective of this study is to explain the importance of frugal innovation in telecom companies in Saudi Arabia and to examine its theoretical link to the resource-based view. Furthermore, this study aims to investigate the relationship between environmental orientation and frugal innovation. Environmental orientation is estimated using both internal and external orientations. Furthermore, this study examines the moderating role of transformational leadership in the relationship between external and internal environmental orientation and frugal innovation in telecom companies in Saudi Arabia. For the empirical analysis, data were collected via a well-developed questionnaire sent to employees of three leading telecom companies: Zain, Mobily, and Saudi Telecom Company (STC). The sample size consisted of 436 employees. Furthermore, SmartPLS 4 software was used for PLS-SEM modeling to test hypotheses and assess model validity. After the data analysis, it was found that external environmental orientation has a positive and significant relationship with frugal innovation, and internal environmental orientation also shows a positive relationship. Furthermore, it was found that transformational leadership positively moderates the relationship between external and internal environmental orientations with frugal innovation in the telecommunications company. Furthermore, this study concluded that the telecommunications sector makes a significant contribution to economic development and that executives’ transformational leadership skills are an important moderator in analyzing environmental orientation and in enhancing employees’ abilities to utilize limited resources to achieve maximum benefits. This study is highly relevant to policymakers and researchers in the context of frugal innovation, in support of the organization’s objective. Full article

To address the small ATC-63 record set for collapse-oriented motion selection and the limited interpretability of data-driven approaches, this study proposes a framework for identifying structural-collapse-critical ground motions. Using 5074 records from the PEER NGA-West2 database, we applied STA/LTA event detection and extracted multi-source features. A Gaussian mixture model (GMM) was then used to perform unsupervised clustering and identify four physically interpretable groups. LightGBM, XGBoost, and Random Forest were employed to test the separability of the cluster labels, with all three models achieving F1 scores above 0.89 and LightGBM reaching an accuracy of about 93%. SHAP-based feature-importance analysis was used at the model level to clarify feature contributions and improve interpretability. Cluster 2 exhibits markedly higher relative seismic energy, stronger time-domain variability, and more dominant frequencies, forming a typical strong-motion hazard signature. For external engineering verification, 22 ATC-63 far-field records were mapped onto the full dataset to examine cluster-level enrichment and coverage. Cluster 2 shows significant enrichment in engineering markers and high coverage and is therefore identified as the collapse-sensitive phenotype cluster (COP). Overall, the framework provides a technical basis for ground-motion selection in collapse assessment, fragility analysis, and design evaluation. Full article

In the context of intensifying environmental regulation and sustainability pressures, firms increasingly face the challenge of sustaining green innovation under uncertainty. Green innovation resilience, which is defined as a firm’s capacity to maintain green innovation momentum and adaptively evolve technological capabilities amidst uncertainty, represents a critical organizational competence. Moving beyond static output measures, this resilience captures the intertemporal stability of firms’ green patenting activities during turbulent periods. From a systems perspective, executive compensation arrangements represent an important internal incentive mechanism that interacts with managerial characteristics and external policy environments. This study investigates how executive compensation stickiness—defined as asymmetric pay adjustment in response to firm performance—affects green innovation resilience. Using panel data from Chinese A-share-listed firms, we find that executive compensation stickiness significantly promotes green innovation resilience at the 5% level, suggesting that downward pay rigidity mitigates managerial risk aversion and supports tolerance for short-term setbacks in long-horizon green innovation. Furthermore, this positive relationship is further strengthened when executives possess environmental backgrounds (at the 5% level) and when firms receive government green innovation subsidies (at the 10% level), highlighting the interactive role of individual-level attributes and institutional policy support. Overall, the findings demonstrate how incentive asymmetry functions as a systemic property shaping firms’ adaptive responses and contribute to a broader understanding of green innovation resilience in complex socio-technical systems. Full article

This study presents a comparative analysis of cylindrical lithium-ion cell architectures, tracing the evolution from the conventional tabbed design (18650/21700) to the large-format 4680 cell with its tabless current collectors. This architectural shift is driven by the imperative to minimise internal ohmic resistance and enhance thermal management in high-power automotive battery applications. Forensic investigation reveals that the 4680 design replaces localised, high-resistance tab connections with a distributed, low-impedance interface, necessitating the adoption of advanced manufacturing techniques, including long ultrasonic torsional welding and highly controlled high-power density laser welding. Crucially, the welding of external aluminium busbars to the cell relies on sophisticated microstructural engineering, particularly for the challenging dissimilar Aluminium-Steel (Al-Steel) anode weld. This weld format employs a spiral laser path to limit the formation of brittle aluminium-iron (Al-Fe) intermetallic compounds (IMCs), leveraging the steel cell casing’s nickel plating to promote a more ductile Al-Fe-Ni phase for improved joint reliability. Furthermore, the 4680 cell incorporates a significantly thicker casing (≈0.54 to 0.7 mm) for enhanced mechanical strength. In conclusion, the 4680 cell achieves superior performance through robust mechanical design and advanced welding processes that prioritise microstructurally sound, low-resistance interfaces. Full article

Antiphospholipid syndrome (APS), first described in 1983, is a systemic autoimmune disorder characterized by recurrent arterial and venous thrombosis, pregnancy complications, and persistent antiphospholipid antibodies (aPL). Over four decades, significant advancements have been made in understanding APS pathogenesis, diagnostics, and treatment. Key discoveries include the development of standardized anticardiolipin antibody (aCL) assays, the identification of β2-glycoprotein I (β2GPI) as a critical cofactor, and the elucidation of the “two-hit” hypothesis, which explains thrombotic events through a combination of aPL-induced prothrombotic priming and secondary external triggers. Recent research has highlighted the roles of complement activation, neutrophil extracellular traps (NETs), and genetic predispositions shared with systemic lupus erythematosus (SLE). Innovations like the antiphospholipid score (aPL-S) and updated classification criteria, including the 2023 ACR/EULAR guidelines, have improved diagnostic precision and risk stratification. Despite these advances, challenges remain in assay standardization and addressing seronegative APS. Future directions emphasize the integration of multimodal biomarkers, precision diagnostics, and targeted therapies aimed at complement and NET pathways. These efforts aim to achieve individualized care, improving outcomes for APS patients through harmonized diagnostics, mechanistic therapeutics, and data-driven approaches. This review underscores the evolving understanding of APS and its potential for personalized management strategies. Full article

The conversion of biogas into high-value chemicals for pharmaceutical, cosmetic, and nutraceutical markets offers an attractive alternative to conventional fossil-based production routes, enabling circular value chains with significant socio-economic impact. This study evaluated the valorization of biogas into osmolyte and carotenoid compounds with market prices ranging from 1000 to 7000 $·kg⁻¹. Specifically, an algal–methanotrophic co-culture operated under saline conditions, preventing external microbial contamination and stimulating osmolytes and carotenoids, was assessed for its capacity to simultaneously remove methane (CH₄) and carbon dioxide (CO₂), with efficiencies of 92 and 89%, respectively. while producing ectoine, hydroxyectoine, lutein, β-carotene, and astaxanthin. Shotgun metagenomic analyses identified the key microorganisms driving the process, predominantly alkaliphilic and halophilic green algae (Chlorella, Dunaliella) and cyanobacteria (Leptolyngbya), and halotolerant methanotrophs (Methylotuvimicrobium) and methylotrophs (Methylophaga). Metagenomics further revealed the presence of key metabolisms related to C1 utilization and biosynthetic genes associated with carotenoid and osmolyte production, confirming the metabolic potential of the consortium to convert biogas-derived carbon directly into high-value compounds. Overall, these results demonstrate the feasibility of an efficient, biologically driven bio-platform capable of transforming greenhouse gas-rich waste streams into economically relevant bioactive molecules, contributing to global priorities in sustainable biomass-to-biochemical innovation. Full article

Despite stringent environmental regulations, the divergence between private costs and social benefits frequently induces symbolic rather than substantive firm compliance. This study investigates Executive Green Cognition (EGC) as an internal mechanism to mitigate this distortion. Using a text-based index derived from Management Discussion and Analysis (MD&A) disclosures of Chinese listed firms (2010–2024), we demonstrate that higher EGC significantly facilitates corporate green transition by enhancing both green innovation output and Total Factor Productivity. Supporting the micro-foundations of the Porter Hypothesis, we find that these productivity gains coincide with reduced Carbon Emission Intensity (CEI), thereby ruling out scale expansion effects. Mechanism tests indicate that EGC reduces agency costs by reallocating resources from non-productive defensive expenditures to substantive green investments. Furthermore, digital transformation positively moderates this relationship by lowering implementation costs. These findings highlight EGC as a critical micro-foundation for shifting firms from passive compliance to endogenous sustainable development. Full article

Identifying which power allocation patterns best enable firms to withstand external shocks and achieve sustainable development remains a central concern in crisis management. Drawing on data from Chinese A-share listed companies between 2018 and 2023, this study empirically investigates the mechanisms through which power distribution influences Enterprise Resilience (ER). It further explores how business strategies and innovation demand moderate this relationship. The study finds that decentralized decision-making authority significantly enhances ER during crises. Moreover, both Prospector business strategies and innovation demand positively and significantly strengthen the relationship between degree of power decentralization (DEC) and ER. These findings offer theoretical contributions and practical insights for firms seeking to develop sustainable, resilient, dynamic, and adaptive power allocation systems in times of crisis. Full article

Body size is a key trait influencing life history and ecological adaptation, and sexual size dimorphism (SSD) reflects divergent selective pressures acting on males and females. In morphologically conserved insect groups such as Cassidinae leaf beetles, the external similarity between sexes often impedes accurate dimorphism assessment. To address this, we conducted a systematic morphometric study of ten Cassidinae species from the Nanling Mountains—the largest east–west mountain system in southern China—where we definitively assigned sex via genital dissection. We measured body weight, body length, body width, length–width ratio, and corresponding wing traits. Across all species, SSD was consistently female biased, with statistically significant but subtle differences in most traits; body weight exhibited the greatest relative disparity. While this pattern aligns with the fecundity advantage hypothesis, direct fecundity data were not collected. Crucially, interspecific allometric analyses revealed that the scaling of male and female body sizes was statistically indistinguishable from that of isometry, providing no significant support for Rensch’s rule in this female-biased system. Our findings offer foundational insights into SSD evolution in cryptically dimorphic, herbivorous beetles and highlight the need for phylogenetically informed studies across broader geographic and taxonomic scales. Full article

Natural disasters disrupt maritime operations; yet, their environmental consequences remain underexplored. This study quantifies CO₂ emission changes following the February 2023 İskenderun Bay earthquakes (7.6 Mwg and 7.5 Mwg) using AIS-derived port visit data and graph neural network modeling. Analyzing 25,837 port visits across a 36-month period (January 2022–December 2024), we compared emissions during baseline (pre-earthquake), acute disruption (February–June 2023), and recovery phases. Results revealed a statistically significant 35.9% increase in per-visit CO₂ emissions during the acute phase (t = 11.79, p < 0.001, Cohen’s d = 0.27), driven by extended port visit durations (from 77.87 to 105.82 h). Counterfactual analysis estimated 27,574 tonnes of excess CO₂ emissions directly attributable to earthquake disruption. Network analysis showed a 23.8% reduction in edge density during the acute phase. The graph neural network (GNN) emission prediction model achieved R² = 0.985 (baseline) and R² = 0.997 (recovery) in predicting emission patterns, while the acute phase showed predictability collapse (R² = −1.591). These findings demonstrate that seismic events generate sustainability-relevant externalities beyond immediate physical damage, and that quantifying disruption-driven excess emissions supports sustainability-oriented port resilience planning and more robust maritime emission accounting (e.g., under the EU MRV framework). Full article

Artificial Intelligence (AI) has introduced transformative possibilities in orthodontics by enhancing diagnostic precision, treatment planning, and aesthetic outcomes. In face-driven orthodontics, treatment objectives extend beyond achieving proper occlusion to optimizing facial balance and harmony. With the growing patient demand for aesthetic improvements, AI technologies enable clinicians to integrate facial analysis and dynamic soft-tissue evaluation into personalized treatment approaches. Research in this scoping review analyzed current applications of AI in face-driven orthodontics, focusing on diagnosis, soft-tissue assessment, and individualized treatment planning. A comprehensive search was conducted in PubMed and Scopus for studies published between 2021 and 2025. The review followed the PRISMA-ScR guidelines. Of 54 initially identified studies, 24 met the inclusion criteria after title, abstract, and full-text screening. Extracted data were organized according to the main application areas of AI in face-driven orthodontics. Most studies focused on AI-assisted facial analysis, 3D reconstruction, and treatment simulation. Deep learning models demonstrated high performance in soft-tissue prediction, aesthetic evaluation, and diagnostic accuracy. However, heterogeneity in datasets, a lack of standardized validation protocols, limited external validation across included studies and limited clinical applicability were identified as key limitations. AI-based facial analysis supports a shift toward individualized, aesthetics-oriented orthodontic planning. Although current evidence highlights its potential for improving diagnostic precision and treatment outcomes, further validation through large-scale clinical studies is essential for broader implementation in everyday practice. Full article