Search Results (18,003)

This study presents the development and experimental validation of a dynamic simulation model for a transcritical CO₂ heat pump system coupled with a stratified water tank, with particular focus on strong transient behavior and detailed heat exchanger characteristics. Due to the unique thermophysical properties of CO₂ under transcritical conditions, conventional modeling approaches are insufficient. The model was validated against experimental results under a range of operating conditions. It accurately predicted outlet water temperatures within ±3.2 °C and system COP within ±6.8% deviation from measurements. In contrast to previous models, this approach offers improved accuracy in capturing dynamic system responses, including startup transients, and demonstrates high adaptability across varying ambient temperatures and load profiles. Importantly, the model also considers the vertical installation layout of components, enabling analysis of gravitational effects on system dynamics and offering insights into optimal configuration strategies. The validated model serves as a powerful tool for system optimization and advanced control design in residential CO₂ heat pump applications. Full article

Among the many nanomaterials studied for biomedical uses, silica and gold nanoparticles have gained significant attention because of their unique physical and chemical properties and their compatibility with living tissues. Mesoporous silica nanoparticles (MSNs) have great stability and a large surface area, while gold nanoparticles (AuNPs) display remarkable optical features. Both types of nanoparticles have been widely researched for their individual roles in drug delivery, imaging, biosensing, and therapy. When combined with gadolinium (Gd), a common contrast agent, these nanostructures provide improved imaging due to gadolinium’s strong paramagnetic properties. This study focuses on incorporating gold nanoparticles and gadolinium into a silica matrix to develop a theranostic system. Various analytical techniques were used to characterize the nanocomposites, including infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-Vis), thermogravimetric analysis (TGA), nitrogen adsorption, scanning electron microscopy (SEM), dynamic light scattering (DLS), X-ray fluorescence (XRF), X-ray diffraction (XRD), vibrating sample magnetometry (VSM), and neutron activation analysis (NAA). Techniques like XRF mapping, XANES, nitrogen adsorption, SEM, and VSM were crucial in confirming the presence of gadolinium and gold within the silica network. VSM and EPR analyses confirmed the attenuation of the saturation magnetization for all nanocomposites. This validates their potential for biomedical applications in diagnostics. Moreover, activating gold nanoparticles in a nuclear reactor generated a promising radioisotope for cancer treatment. These results indicate the potential of using a theranostic nanoplatform that employs mesoporous silica as a carrier, gold nanoparticles for radioisotopes, and gadolinium for imaging purposes. Full article

Improving the isotropic magnetic properties of FeSi electrical steels has traditionally focused on enhancing their crystallographic texture and microstructural morphology. Strengthening the cube texture within a ferritic matrix of optimal grain size is known to reduce core losses and increase magnetic induction. However, conventional cold rolling followed by annealing remains insufficient to optimise the magnetic performance of thin FeSi strips fully. This study explores an alternative approach based on grain boundary migration driven by temperature gradients combined with deformation gradients, either across the sheet thickness or between neighbouring grains, in thin, weakly deformed non-oriented (NO) electrical steel sheets. The concept relies on deformation-induced grain growth supported by rapid heat transport to promote the preferential formation of coarse grains with favourable orientations. Experimental material consisted of vacuum-degassed FeSi steel with low silicon content. Controlled deformation was introduced by temper rolling at room temperature with 2–40% thickness reductions, followed by rapid recrystallisation annealing at 950 °C. Microstructure, texture, and residual strain distributions were analysed using inverse pole figure (IPF) maps, kernel average misorientation (KAM) maps, and orientation distribution function (ODF) sections derived from electron backscattered diffraction (EBSD) data. This combined thermomechanical treatment produced coarse-grained microstructures with an enhanced cube texture component, reducing coercivity from 162 A/m to 65 A/m. These results demonstrate that temper rolling combined with dynamic annealing can surpass the limitations of conventional processing routes for NO FeSi steels. Full article

Various corporate groups, such as third-sector institutions in Latin America, have shown increasing interest in evaluating organizational performance as a possible strategy for increasing their effectiveness and competitiveness. From this perspective, this study analyzes the psychometric properties of a scale that assesses organizational performance in third-sector institutions in Latin America. The design was instrumental. The sample consisted of 355 workers from nine Latin American countries, recruited through non-probability sampling. A validity and reliability analysis of the scale confirmed the items and original factors. In this sense, the accessibility and use of a brief and useful tool for measuring organizational performance enriches knowledge about organizational theory by facilitating the comparison and validation of existing approaches or even by suggesting new dimensions that reflect the dynamic complexity of current organizations in Latin America. Full article

This paper presents the implementation of a real-time nonlinear state observer applied to an erbium-doped fiber laser system. The observer is designed to estimate population inversion, a state variable that cannot be measured directly due to the physical limitations of measurement devices. Taking advantage of the fact that the laser intensity can be measured in real time, an observer was developed to reconstruct the dynamics of population inversion from this measurable variable. To validate and strengthen the estimate obtained by the observer, a Recurrent Wavelet First-Order Neural Network (RWFONN) was implemented and trained to identify both state variables: the laser intensity and the population inversion. This network efficiently captures the system’s nonlinear dynamic properties and complements the observer’s performance. Two metrics were applied to evaluate the accuracy and reliability of the results: the Euclidean distance and the mean square error (MSE), both of which confirm the consistency between the estimated and expected values. The ultimate goal of this research is to develop a neural control architecture that combines the estimation capabilities of state observers with the generalization and modeling power of artificial neural networks. This hybrid approach opens up the possibility of developing more robust and adaptive control systems for highly dynamic, complex laser systems. Full article

This paper introduces a new class of probability distributions, termed the generated log exponentiated polynomial (GLEP) family, designed to enhance flexibility in modeling complex real financial data. The proposed family is constructed through a novel cumulative distribution function that combines logarithmic and exponentiated polynomial structures, allowing for rich distributional shapes and tail behaviors. We present comprehensive mathematical properties, including useful series expansions for the density, cumulative, and quantile functions, which facilitate the derivation of moments, generating functions, and order statistics. Characterization results based on the reverse hazard function and conditional expectations are established. The model parameters are estimated using various frequentist methods, including Maximum Likelihood Estimation (MLE), Cramer–von Mises (CVM), Anderson–Darling (ADE), Right Tail Anderson–Darling (RTADE), and Left Tail Anderson–Darling (LEADE), with a comparative simulation study assessing their performance. Risk analysis is conducted using actuarial key risk indicators (KRIs) such as Value-at-Risk (VaR), Tail Value-at-Risk (TVaR), Tail Variance (TV), Tail Mean Variance (TMV), and excess function (EL), demonstrating the model’s applicability in financial and insurance contexts. The practical utility of the GLEP family is illustrated through applications to real and simulated datasets, including house price dynamics and insurance claim sizes. Peaks Over Random Threshold Value-at-Risk (PORT-VaR) analysis is applied to U.K. motor insurance claims and U.S. house prices datasets. Some recommendations are provided. Finally, a comparative study is presented to prove the superiority of the new family. Full article

Myelin is a membranous structure critically important for human health. Historically, it was believed that myelin remained largely unchanged in the adult brain. However, recent research has shown that myelin is remarkably dynamic, capable of adjusting axonal conduction velocity and playing a role in learning, memory, and recovery from injury, in response to both physiological and pathological signals. Axons are more efficiently insulated in myelinated fibers, where segments of the axonal membrane are wrapped by the myelin sheath. Although extensive data are available on the electrical properties of myelin, its structural and mechanical characteristics—as well as the role of its lipid composition—are also relevant, although much less explored. The objective of our review is derived from this point since alterations in lipid components can lead to axonal dysfunction, giving rise to neurological disorders such as multiple sclerosis and other demyelinating conditions. In this review, concerning the lipid composition of myelin, we focus on two distinct classes of lipids: sphingolipids and long-chain fatty acids, emphasizing the differential contributions of saturated versus polyunsaturated species. We analyze studies that correlate the mechanical vulnerability of myelin with its lipid composition, particularly sphingomyelin, thereby underscoring its role in protecting neurons against physical stress and providing a robust microstructural network that reinforces the white matter as a whole. From a biochemical perspective, we examine the susceptibility of myelin to oxidative stress, metabolic disorders, and extreme nutritional deficiencies in relation to the role of long-chain fatty acids. Both perspectives highlight that the aforementioned lipids participate in a complex biomechanical balance that is essential for maintaining the stability of myelin and, consequently, the integrity of the central and peripheral nervous systems. Full article

This article addresses the multifaceted challenges inherent in the development of the novel REEFS (Renewable Electric Energy From Sea) wave energy converter (WEC). Building on the submerged pressure differential principle, it frames similar WECs before focusing on REEFS that combines renewable energy generation with coastal protection, functioning as an artificial reef. The review follows chronological criteria, encompassing experimental proof-of-concept, small-scale laboratory modeling, simplified and advanced computational fluid dynamics (CFD) simulations, and the design of a forthcoming real-sea model deployment. Key milestones include the validation of a passive variable porosity system, demonstration of wave-to-wire energy conversion, and quantification of wave attenuation for coastal defense. Additionally, the study introduces a second patent-protected REEFS configuration, isolating internal components from seawater via an elastic enveloping membrane. Challenges related to scaling, numerical modeling, and funding are thoroughly examined. The results highlight the importance of the proof-of-concept as the keystone of the development process, underscore the relevance of mixed laboratory-computational approaches and emphasize the need for a balanced equilibrium between intellectual property safeguard and scientific publishing. The REEFS development trajectory offers interesting insights for researchers and developers navigating the complex innovation seas of emerging wave energy technologies. Full article

This work presents a systematic study of nonlinear differential equations within Sobolev spaces, focusing on mild solutions and their qualitative properties. An iterative reconstruction method is developed to obtain uniform a priori bounds, which ensure both the existence and tightness of invariant measures. Furthermore, uniqueness of these measures is established under appropriate structural conditions. The results provide a rigorous foundation for analyzing the asymptotic behavior of nonlinear dynamical systems. Full article

The earthquake of 6 February 2023, in Turkey and Syria, was catastrophic for many existing buildings. Various reasons have been given to try to understand what happened, since after 2000, changes in construction methods were introduced in this area, with the aim of improving buildings. In this research, the behavior of frame buildings with a concrete structure is analyzed. To do this, graphene oxide (GO) is introduced into traditional mixtures to improve the most deficient mechanical characteristics of traditional concrete. Laboratory tests performed with GO in traditional concrete mixtures produce improvements in the mechanical analyses performed, essential characteristics for improving the structural behavior of the frame models analyzed in this research. The mechanical results show increases of 13% in the modulus of elasticity, 22% in compression strength tests, 72% in flexural-tensile strength tests, and 14% in ductility, in addition to a 4% reduction in the density of the mixture. These characteristics are essential to understand the structural improvement of the models, helping to reduce the seismic vulnerability of the structures. To reach these conclusions, static and dynamic analyses (using records of the most intense seismic activity that occurred in Turkey in 2023) are performed on three frames of 5, 10, and 20 stories in height, considering the mechanical properties of the new mixtures (traditional and GO) obtained in the laboratory. The results obtained in the analyses of the frame models using GO in the new mixtures show improvements in the structural performance of the frames, improvements that increase with increasing height of the structures. To conclude this investigation, the analyses performed on the frame models are extended with the introduction of brick walls in the exterior bays of the bare frames, a solution commonly used to improve the resistant behavior of these structures, determining a structural improvement of the models, due to the high strength and stiffness that these infill walls impart to the bare frames. Full article

Understanding the societal impacts of armed conflict remains challenging due to limitations in current models, which often apply fixed-radius buffers or composite indices that obscure critical dynamics. These approaches struggle to account for indirect effects, cumulative damage, and context-specific vulnerabilities, especially the question of why similar events produce vastly different outcomes across regions. We introduce a novel computational framework that applies principles from engineering and material science to conflict analysis. Communities are modeled as elastic plates, “social fabrics”, whose physical properties (thickness, elasticity, coupling) are derived from spatial socioeconomic indicators. Conflict events are treated as external forces that deform this fabric, enabling the simulation of how repeated shocks propagate and accumulate. Using a custom Python-based finite element analysis implementation, we demonstrate how heterogeneous data sources can be integrated into a unified, interpretable model. Validation tests confirm theoretical behaviors, while a proof-of-concept application to Nigeria (2018) reveals emergent patterns of spillover, nonlinear accumulation, and context-sensitive impacts. This framework offers a rigorous method to distinguish structural vulnerability from external shocks and provides a tool for understanding how conflict interacts with local conditions, bridging physical modeling and social science to better capture the multifaceted nature of conflict impacts. Full article

The occurrence of pilling affects the appearance and aesthetic properties of knitwear and leads to a shortened lifespan of underwear, which is usually worn directly on the skin and under the outer layers of clothing and is exposed to direct contact with various textile materials in a dynamic microclimate. The interdependence of surface-induced pilling and electrical resistance (i.e., conductivity), which also affects wearing comfort, has not been sufficiently investigated. This paper therefore analyzes how surface-induced pilling of different intensities affects the surface resistivity and vertical resistance, physical properties and moisture content of double jersey cotton knitwear under different relative humidity conditions (25%, 40%, 65% and 80%) using Pearson’s correlation coefficient and coefficient of determination. Pilling was induced using the modified Martindale method and two types of abrasives, with higher intensity and larger pills obtained with a rougher wool reference abrasive. It was found that the surface resistivity and vertical resistance of cotton knitwear increased after prolonged wear due to surface-induced pilling and that mass, thickness and moisture content were not directly related to changes in electrical resistivity. The results of the Pearson correlation analysis showed a strong and quantifiable correlation between the intensity of surface pilling and surface resistivity at relative humidity up to 65%, despite their high moisture absorption. This statistically confirms that the occurrence of pilling reduces the electrical conductivity of cotton knitwear, resulting in a lower wearing comfort of cotton-based underwear. This finding can be useful in the development of underwear with high durability and comfort. Full article

Coastal wetlands play a crucial role in carbon sequestration, yet the invasion of Spartina alterniflora (SA) significantly alters the cycling and sequestration of soil organic carbon (SOC) in coastal wetlands. Nevertheless, the potential underlying mechanisms governing the dynamics of SOC in coastal wetlands following SA invasion remain poorly understood. Here, we investigated the impacts of SA invasion on the dynamics and potential sequestration mechanisms of SOC in the Hangzhou Bay Estuary Wetland, China. Compared to the bare flat (BF), SOC and its fractions in 0–20 cm increased by 1.37–2.24 times after 8 years of SA invasion. Variance partitioning analysis indicated that the combined effects of soil physicochemical properties, soil carbon cycle-related enzymes, and vegetation type were the primary drivers of SOC and its fractions. Redundancy analysis revealed significant positive correlations between SOC and key soil physicochemical properties and enzymes, including sucrase, clay particles, total nitrogen, ammonium nitrogen, and β-glucosidase. Structural equation modeling demonstrated that SA invasion was associated with significant alterations in soil physicochemical properties and positively correlated with both stable and labile carbon fractions, or indirectly linked to these fractions through carbon cycle-related enzymes, thereby substantially positively contributing to SOC. This study supports the hypothesis that the invasion of SA affects the linkage pathway of SOC sequestration and offers valuable guidance for carbon sequestration strategies of coastal wetlands. Full article

Framed within the lens of systems theory and sociotechnical systems thinking, this systematic review examines telehealth as a complex adaptive system and dynamic health system shaped by the interactions between interconnected technological, social, and institutional components. Recognizing telehealth as part of a complex adaptive system, the review identifies how interdependent factors, such as digital literacy, connectivity, and policy, evolve and influence access to and the emergent properties of care. A systematic review was conducted following the PRISMA 2020 guidelines and PROSPERO registration (CRD420251103608), analyzing 42 peer-reviewed articles published between January 2020 and June 2025, identified through the MEDLINE, Web of Science, EBSCOhost, ACM Digital Library, PsycINFO, and Scopus databases. Key findings include sustained but reduced telehealth use after the pandemic peak, as well as a small yet statistically significant positive effect of telehealth interventions on cognitive emergent properties, defined here as measurable outcomes like memory, attention, executive function, and processing speed (SMD = 0.29; 95% CI [0.04, 0.54]) with very low heterogeneity (I² = 0%). Significant system components such as digital illiteracy, poor internet connectivity, and complex technology interfaces disproportionately affected economically disadvantaged, minority, and rural older adults. Practical strategies rooted in systems thinking include digital literacy programs, simplified interfaces, caregiver support, improved broadband infrastructure, hybrid healthcare models, and supportive policies. Future research should focus on evidence-based, system-level interventions across diverse settings to bridge the digital divide and promote equitable access to telehealth for older adults. Full article

This work presents the development of a novel multi-scale modeling framework for investigating the beneficial impact of Ti-, Zr-, and V-doped magnesium hydride nanomaterials on hydrogen storage performance. The proposed model integrates atomistic-scale simulations based on density functional theory (DFT) with system-level dynamic heat and mass transfer modeling. At the nanoscale, DFT analysis provides key thermodynamic and kinetic parameters, including reaction enthalpy, entropy, and activation energy, which are incorporated into the macroscopic model to predict the hydrogenation behavior of MgH₂ nanostructures under realistic thermal boundary conditions. Model validation is performed through comparison with experimental data from the literature, showing excellent agreement. The DFT analysis reveals that doping MgH₂ nanomaterials with Ti, V, and Zr modifies their thermodynamic properties, including enthalpy of formation and desorption temperature. At the reactor scale, these modifications lead to enhanced hydrogenation kinetics and improved thermal management. Compared to pristine MgH₂, hydrogenation time is reduced by 21%, 40%, and 42% for Ti-, Zr-, and V-doped nanomaterials, respectively, while thermal energy consumption during hydrogenation decreases by ~17% for V doping. These results highlight the strong correlation between nanoscale modifications and macroscopic system performance. The proposed multi-scale model provides a powerful tool for guiding the design and optimization of advanced nanostructured hydrogen storage materials for sustainable energy applications. Full article