Search Results (54,957)

This study examines the association between the formal (de jure) adoption of renewable energy source (RES) support instruments and observed RES deployment outcomes across 36 European countries. We assess whether broader legislative adoption—measured by a transparent breadth/coverage index (SIC/OIL) based on binary coding and equal sector weights—correlates with higher RES shares. The empirical design comprises three complementary steps: (i) hierarchical clustering (Ward’s method; Euclidean distance on standardised indicators) to classify countries by legislative adoption profiles; (ii) parallel clustering of countries by RES utilisation profiles using 10 z-score-standardised outcome indicators (total and sectoral RES shares and per capita RES use by source); and (iii) an integrated comparison of both typologies, followed by a cross-sectional regression test of the OIL–RES association. Legislative and utilisation clusters do not systematically coincide, and the baseline regression shows a weak, statistically insignificant association with very low explanatory power (R² = ≈ 0.015), supporting heterogeneity (H1) rather than a universal positive average relationship (H₂). Interpretation is conservative because SIC/OIL captures policy-mix coverage (not budgets, enforcement, or design stringency) and because some low/zero policy entries may reflect limited source coverage. Overall, the findings suggest that observed RES performance is primarily shaped by country-specific structural conditions (resource endowments, economic capacity, and sustained long-term investment), implying that context-sensitive instruments and stronger implementation capacities should complement formal policy adoption. Full article

The quality of the feedstock powder plays a key role in determining the properties of coatings produced by cold spray (CS). However, most commercially available powders are not specifically designed for CS, which makes it difficult to tailor powder characteristics for optimal performance. In this study, we examined the cold sprayability of five copper (Cu) powders manufactured using electrolysis, gas atomization, and mechanical grinding. The powders were characterized in terms of their microstructure, particle shape, and size distribution to evaluate how the production method influences powder properties. Powder flowability was measured using a shear cell test, while mechanical properties and deformability relevant to CS were assessed through nano-indentation. The results showed that gas-atomized powders with equiaxed grain structures offered the best combination of flowability and deformability, making them the most suitable for CS. Their spherical particle shape resulted in a lower surface area compared to the irregular electrolytic powder, which reduced inter-particle surface forces and allowed for smoother powder flow. Nano-indentation measurements indicated that the mechanically ground powder with ultra-fine grains and the gas-atomized powder containing fine dendrites had the highest nano-hardness values (HIT = 2.1 ± 0.15 GPa and 1.6 ± 0.1 GPa, respectively). In contrast, the porous electrolytic Cu powder showed the lowest hardness (HIT = 0.7 ± 0.2 GPa). These trends were confirmed by microstructural analysis of the deposited coatings. Coatings produced from the irregular electrolytic powder exhibited limited particle deformation, weak inter-particle bonding, and the highest porosity. Conversely, spherical gas-atomized powders produced much denser coatings. In particular, the powder with the most uniform spherical shape and no microsatellite particles resulted in the lowest coating porosity due to its superior deformation behavior upon impact. Full article

To address the challenge that traditional dam model materials are difficult to simultaneously meet the requirements of microstructural similarity, dynamic damage simulation, and environmental friendliness, a novel microparticle mortar simulated concrete was developed. This new material consists of cement, sand, gypsum, mineral oil, water, and baryte sand. Through systematic material mechanical tests, the effects of each component on the material’s strength, density, and elastic modulus were revealed, and the optimal mix ratio was determined. This enabled precise control of low elastic modulus and had a high density, while the material is environmentally friendly, non-toxic, and compatible with direct contact with natural water. Its mechanical properties are highly similar to those of the prototype concrete. Based on a 1:70 geometric scale, a shaking table model test of the concrete gravity dam-reservoir system was conducted. The dynamic response and damage evolution under empty and full reservoir conditions were compared and analyzed. The study shows that this material can accurately simulate the stress-strain relationship and failure mode of prototype concrete. Under the full reservoir condition, the dam’s fundamental frequency showed only a 2.72% deviation from the numerical simulation, and as the seismic excitation amplitude increased, the changes in the fundamental frequency effectively reflected the accumulation of damage. Under the design seismic motion, the measured accelerations and stress responses for both empty and full reservoir conditions were in good agreement with numerical calculations. Under overload conditions, the acceleration amplification factor at the dam crest decreased with damage accumulation, and the dam neck was identified as the seismic weak zone. As the peak ground acceleration (PGA) increased from 0.15 g to 0.70 g, the fundamental frequency changes effectively reflected the damage accumulation process in the dam, while the hydrodynamic pressure at the dam heel showed a linear increase (457% increase). The experimentally measured hydrodynamic pressure distribution was between the rigid dam and elastic dam hydrodynamic pressures, reflecting the real fluid-structure interaction effect. This study provides a reliable material solution and data support for dam seismic physical model testing. Full article

Background: Antibiotic misuse and overuse remain a critical driver of antimicrobial resistance (AMR), a global health threat associated with increased morbidity, mortality, and healthcare costs. In Greece, where antibiotic consumption and resistance rates are among the highest in Europe, community pharmacists are well-positioned to contribute to antimicrobial stewardship efforts. Objective: This study aimed to assess the knowledge and attitudes of community pharmacists in Achaia, Western Greece, regarding antibiotic use and AMR, in order to identify knowledge gaps and inform future educational interventions. Methods: A cross-sectional survey was conducted between May and July 2023 among 207 pharmacists using a structured, self-administered questionnaire. The survey assessed demographics, knowledge of antibiotic indications, dispensing practices, and awareness of AMR. Statistical analysis included Chi-square tests and multivariate logistic regression. Results: Pharmacists demonstrated high levels of knowledge regarding appropriate antibiotic use in conditions such as sore throat (95%), bronchitis (76%), influenza (77.5%), and diarrhea (95%). However, knowledge was lower for rhinitis (60%) and sinusitis (56%). Almost all pharmacists (99%) were aware of AMR, and 86% perceived it as a significant public health issue in Greece. Logistic regression showed that pharmacists with 5–10 years of experience were significantly less likely to believe that antibiotics are always effective (OR = 0.08, p = 0.042). Conclusion: Pharmacists in Western Greece are generally well-informed about antibiotic use and AMR, yet misconceptions persist, especially for viral infections. Targeted educational interventions, interprofessional collaboration, and stricter enforcement of prescription regulations are needed to strengthen the role of pharmacists in combating AMR at the community level. Full article

Designing structure-preserving numerical schemes for the generalized Poisson-Nernst-Planck (PNP) system is challenging due to its inherent strong nonlinearity and coupling. In this paper, we propose a class of efficient, unconditional energy-stable schemes based on the Stabilized Scalar Auxiliary Variable (S-SAV) framework combined with the finite element method. We construct both first-order (BE-S-SAV) and second-order (BDF2-S-SAV) fully discrete schemes. A distinguishing feature of our approach is the use of a linear decomposition strategy, which decouples the complex nonlinear system into a sequence of linear, constant-coefficient elliptic equations at each time step. This significantly reduces computational complexity by avoiding expensive nonlinear iterations. We provide rigorous theoretical proofs demonstrating that the proposed schemes are unconditionally energy stable and strictly preserve mass conservation. Numerical experiments satisfy the theoretical analysis, confirming optimal convergence rates and demonstrating robust preservation of mass conservation and modified energy stability in the tested regimes. Full article

Background/Objectives: Alzheimer’s disease (AD) is a progressive multifactorial neurodegenerative disorder. Current AD therapies offer minimal benefits and do not prevent or repair neuronal damage. More effective therapeutic approaches are needed to restore normal bioenergetics and metabolic function to AD neurons. Simufilam is a small-molecule oral drug that targets filamin A, a scaffolding protein in brain cells. Phase III clinical trials of simufilam failed to show any significant cognitive or functional improvements in AD patients. The purpose of this study is to identify and explain the molecular mechanisms that may have contributed to this drug’s lack of clinical success. Methods: Our study investigates the effects of simufilam on amyloid processing, neuronal health, and mitochondrial functioning in the SH-SY5Y human neuronal cell model. SH-SY5Y cells were differentiated into neurons using 10 µM retinoic acid. Undifferentiated and differentiated SH-SY5Y were exposed to simufilam (5 µM, 50 µM; 24 hr). Results: Simufilam did not affect the expression of genes involved in amyloid processing. Amyloid precursor protein (APP), β-secretase, and α-secretase mRNA levels in simufilam-treated SH-SY5Y cells were all unchanged compared to untreated cells. However, amyloidogenic β-secretase protein was significantly increased (fold change 1.17) at 50 µM of simufilam only in differentiated SH-SY5Y cells without affecting APP or α-secretase protein expression. Simufilam at the 50 µM concentration reduced brain-derived neurotrophic factor protein levels (fold change 0.7) only in differentiated SH-SY5Y. Further, simufilam did not improve mitochondrial genes or structure. Conclusions: Our results align with clinical outcomes and indicate that insufficient activity across multiple tests of ability to impact processes related to neuronal health can serve as a preliminary indicator of limited clinical utility. Full article

Flexible barrier systems are widely used for landslide and debris flow mitigation due to their ability to dissipate impact energy through large deformations. Conventional systems, however, rely on steel mesh components, which are associated with high environmental impact and durability concerns. This study examines the feasibility of a sustainable coir geotextile composite barrier as an alternative flexible barrier for mitigating small-to-moderate landslides. A woven geotextile barrier was developed using multi-strand coir ropes and evaluated through a comprehensive experimental program involving physical and mechanical characterization, accelerated degradation testing, incremental static loading, vertical drop impact tests, and sustained load retention tests. The developed barrier exhibited a high mass per unit area of approximately 3750 g/m² and tensile capacities exceeding 2 kN at the rope level. Accelerated weathering tests revealed a limited reduction in tensile strength of approximately 5% after three years of exposure, whereas prolonged exposure of five years led to strength losses exceeding 70%, underscoring durability as a key design consideration. Static loading tests confirmed stable behavior up to 550 kg, and sustained loading of approximately 1700 kg was maintained over 48 h without loss of structural integrity. Vertical drop tests demonstrated impact resistance in the range of 6–51 kN, depending on the drop height, mass, and connection density. The results demonstrate that coir geotextile barriers can function as flexible, energy-dissipating composite systems suitable for sustainable landslide mitigation in moderate hazard scenarios. Full article

The accelerating global demand for renewable heating, cooling and electricity, driven by climate change and rising living standards, presents both a challenge and an opportunity for sustainable energy transitions. This paper introduces the Initial-Aid Cashback (IAC) model, an innovative business model designed to finance renewable energy solutions, with a focus on space cooling, by leveraging citizen participation and collaborative financing mechanisms. The model incentivizes private investors through discounted energy prices, while system operators benefit from reduced upfront capital requirements and minimised financial risk. Through two case studies, an office building in Romania (small-scale case) and the application of the REGEN-BY-2 technology in a mixed housing–office area (large-scale case), the paper demonstrates the model’s potential to accelerate the adoption of renewable cooling technologies, enhance profitability for operators, and provide attractive returns for investors. The findings highlight the model’s adaptability to diverse stakeholder needs, its scalability, and its role in fostering the clean energy transition (CET). However, challenges such as the need for a minimum number of investors, legal complexities, and trust-building among stakeholders are identified as critical barriers to implementation. The paper concludes that the IAC model offers a promising pathway to integrate citizens and small investors into the CET, while emphasising the importance of supportive policies, clear governance structures, and practical testing to ensure its success. Full article

This study presents the high-speed visualization of the detonation wave structure in a small-scale hydrogen–oxygen rotating detonation combustor. A 68 mm Rotating Detonation Combustor was modified with a quartz-glass ring, such that radial optical access into the annular detonation chamber was realized. The optical access window covers approximately the first 22 mm of the detonation chamber. The modified experiment was hot-fire tested with the propellant combination gaseous hydrogen–oxygen. Simultaneous high-speed imaging from the back-end of the chamber and normal to the chamber axis allows a thorough investigation of the detonation wave characteristics. Both high-speed cameras were operated at 180,000 frames per second in order to resolve and capture the detonation waves. The downstream camera was used in order to investigate the number of waves and the spinning direction. A stable regime of three co-rotating waves was observed. The wave speed achieved 71% of the theoretical CJ-velocity. The second camera recorded the passing detonation waves through a quartz ring via OH* emissions. From the post-processed OH* images, a better understanding of the detonation wave structure, including the filling height of the fresh gas mixture as well as the approximate angles of the detonation and the shock wave, could be gained. The obtained height of the detonation wave is about 11–12 mm or 6–7 detonation cell sizes for the given setup and experimental conditions. Full article

Despite extensive research on blast-resistant concrete structures, a clear scientific deficiency remains in the quantitative understanding of how fiber-reinforced concrete slabs behave under blast loading, particularly when experimental and numerical investigations are not conducted together under identical loading conditions. Existing studies often focus on either conventional reinforced concrete or isolated material systems, providing limited validation of comparative blast performance across different fiber-reinforced concretes. This study addresses this gap by investigating the blast resistance performance of four types of reinforced concrete slabs: normal concrete (NC), ultra-high-performance fiber-reinforced concrete (UHPFRC), organic fiber-reinforced high-performance concrete (O-HPC), and basalt FRP-sheet-strengthened slurry-infiltrated fiber concrete (F-SIFCON), using full-scale blast experiments and validated numerical simulations conducted with ANSYS Explicit Dynamics. Blast tests were performed to obtain time histories of reflected pressure, displacement, acceleration, reaction force, and internal energy. The influence of different fiber systems and FRP strengthening on dynamic response and failure mechanisms was systematically analyzed. The numerical models showed good agreement with experimental measurements, confirming their reliability. The results indicate that the normal concrete slab exhibited brittle failure and poor blast resistance, whereas the F-SIFCON slab demonstrated the best overall performance. Compared with the normal concrete slab, the F-SIFCON slab achieved approximately a 47% reduction in maximum displacement, a 56% increase in peak reaction force, and the highest internal energy absorption of 236 kJ. The UHPFRC and O-HPC slabs also showed improved blast resistance, although with different post-peak response characteristics. These findings demonstrate that hybrid fiber reinforcement combined with FRP strengthening can significantly enhance the blast resistance of concrete slabs and that coupled experimental–numerical approaches provide a robust framework for evaluating structural performance under extreme dynamic loading. Full article

In the fabrication of ultrathin multilayer ceramic capacitors (MLCCs), the long-term stability of ceramic slurries is a critical yet often overlooked factor that can significantly influence coating uniformity, interfacial adhesion, and process reproducibility. Despite its industrial importance, the time-dependent evolution of slurry dispersion structures during storage and its direct impact on green sheet properties remain insufficiently understood. This study examined the time-dependent physicochemical evolution of barium titanate (BaTiO₃)-based green sheet slurries, which behave as colloidal gel-like dispersion systems, and their influence on the structural, optical, and interfacial properties of the resulting sheets. Dynamic light scattering revealed progressive yet uniform particle aggregation, while viscosity measurements indicated a gradual ~10% decrease over 960 h, reflecting reduced dispersion stability and progressive weakening of the slurry gel network during extended storage. The slurry, consisting of BaTiO₃ particles, polymeric binders, and plasticizers, forms a three-dimensional transient gel network, in which particle–particle and particle–binder interactions govern rheological behavior. The observed viscosity decrease and turbidity reduction indicate gel network relaxation and partial gel–sol–like transition behavior driven by aggregation. Cross-sectional scanning electron microscopy demonstrated that these changes produced a measurable reduction in final green sheet thickness, despite identical processing conditions. Furthermore, peel tests revealed that interfacial adhesion strength increased with storage time, attributable to localized solid enrichment within the slurry gel matrix and enhanced bonding at the release film interface. The reduced coating thickness also contributed to lower optical haze, reflecting a shortened light-transmission path. Collectively, these findings demonstrate that even moderate aggregation in a ceramic network-forming dispersion system substantially alters coating behavior, adhesion, and optical performance. The results underscore the importance of managing gel-network stability and rheology to ensure reliable green sheet fabrication and storage in MLCC manufacturing. Full article

This research investigates the impact of augmented and virtual reality (AR/VR) and AI-enabled chatbots, both individually and collectively, on consumer engagement of e-commerce platforms. Moreover, this research examines the mediating effects of perceived utility, ease of use, and enjoyment and the moderating effects of product type and technology readiness, respectively. By applying the theories of Technology Acceptance Model (TAM) and Stimulus–Organism–Response (S-O-R), this research proposed this theoretical framework and adopted a mixed-method research method. This research collected its empirical findings from 486 respondents who had utilized chatbots and AR/VR technology on three of China’s most popular e-commerce platforms, including Taobao, JD.com, and Pinduoduo. Structural equation modeling was utilized for hypothesis testing, and semi-structured interviews on 30 participants were used for validation of empirical findings. Results reveal that both AI chatbot features (β = 0.35, p < 0.001) and AR/VR technologies (β = 0.42, p < 0.001) significantly enhance consumer engagement, with AR/VR demonstrating stronger effects. Perceived enjoyment emerged as the strongest mediator (AI: β = 0.14; AR/VR: β = 0.18), surpassing traditional utilitarian factors. Technology readiness significantly moderated these relationships, with high-readiness consumers showing substantially stronger responses (AI: β = 0.45; AR/VR: β = 0.52). Experience goods amplified technology effects compared to search goods. Multi-group analysis revealed platform-specific variations, while robustness checks identified diminishing returns for AI chatbots but not AR/VR technologies. This research contributes to digital marketing and information systems literature by providing empirical evidence of differential technology impacts on engagement, highlighting the dominance of hedonic over utilitarian pathways in consumer technology adoption. The findings offer practical guidance for e-commerce platforms in optimizing technology investments and designing engagement strategies. Full article

This study presents a series of hydrodynamic experiments on a novel pontoon-type offshore floating photovoltaic (OFPV) structure, designed to improve wave attenuation performance and platform stability in marine environments. Using a 1:14 Froude-scaled physical model capable of representing different connector stiffness levels, nine structural configurations were tested, covering four array scales, three stiffness levels, and two floater sizes. Experiments were conducted under regular wave conditions, with structural responses measured at three representative positions: wave-facing front (T1), mid-array (T2), and leeward side (T3). Recorded parameters included surge acceleration, heave acceleration, pitch angle, and heave displacement. Results show that increasing array scale consistently reduced motion amplitudes at all positions, with heave acceleration at T3 substantially decreased compared with the smallest array. Enhancing connector stiffness significantly suppressed dynamic motions, particularly downstream, while larger floaters notably reduced heave responses under short-period waves. Despite variations in magnitude, response trends with respect to wave period remained broadly consistent across configurations. These findings provide quantitative evidence and engineering guidance for optimizing array configuration, connector stiffness, and floater dimensions to enhance the hydrodynamic performance and operational reliability of large-scale offshore FPV platforms. Full article

Rapid assessment of existing reinforced concrete (RC) buildings is essential for effective seismic risk mitigation, particularly in highly active regions such as Bingol, Turkiye. This study evaluates the local performance of three Rapid Visual Screening (RVS) methods—RBTY-2019, FEMA-P154, and IITK-GSDMA—using verified post-earthquake damage data from the 2003 Bingol Earthquake (SERU-2003). To overcome the limitations of traditional RVS approaches, an Artificial Neural Network (ANN) model was developed and trained with the same dataset to predict building damage levels based on structural deficiency parameters. The ANN achieved a regression coefficient above 0.90 and 100% consistency in test predictions, demonstrating superior accuracy and adaptability to local construction characteristics. A Local Scaling Function (LSF) was also proposed to translate RBTY-2019 performance scores into empirical damage states, achieving 100% consistency with observed data. The findings highlight the reliability of locally trained AI models and the importance of adapting national screening regulations to regional seismic experiences. This integrated ANN–RVS framework provides a practical, data-driven tool for local authorities to prioritize urban building stock and strengthen disaster risk management strategies. Full article

Background/Objectives: Dental diseases represent a great problem for oral health care, and early diagnosis is essential to reduce the risk of complications. Panoramic radiographs provide a detailed perspective of dental structures that is suitable for automated diagnostic methods. This paper aims to investigate the use of an advanced deep learning (DL) model for the multiclass classification of diseases at the sub-diagnosis level using panoramic radiographs to resolve the inconsistencies and skewed classes in the dataset. Methods: To classify and test the models, rich data of 10,580 high-quality panoramic radiographs, initially annotated in 93 classes and subsequently improved to 35 consolidated classes, was used. We applied extensive preprocessing techniques like class consolidation, mislabeled entry correction, redundancy removal and augmentation to reduce the ratio of class imbalance from 2560:1 to 61:1. Five modern convolutional neural network (CNN) architectures—InceptionV3, EfficientNetV2, DenseNet121, ResNet50, and VGG16—were assessed with respect to five metrics: accuracy, mean average precision (mAP), precision, recall, and F1-score. Results: InceptionV3 achieved the best performance with a 97.51% accuracy rate and a mAP of 96.61%, thus confirming its superior ability for diagnosing a wide range of dental conditions. The EfficientNetV2 and DenseNet121 models achieved accuracies of 97.04% and 96.70%, respectively, indicating strong classification performance. ResNet50 and VGG16 also yielded competitive accuracy values comparable to these models. Conclusions: Overall, the results show that deep learning models are successful in dental disease classification, especially the model with the highest accuracy, InceptionV3. New insights and clinical applications will be realized from a further study into dataset expansion, ensemble learning strategies, and the application of explainable artificial intelligence techniques. The findings provide a starting point for implementing automated diagnostic systems for dental diagnosis with greater efficiency, accuracy, and clinical utility in the deployment of oral healthcare. Full article