Search Results (15,973)

Alginate hydrogels are integral to many cell-based models in tissue engineering and regenerative medicine. As a natural biomaterial, the properties of alginates can vary and be widely adjusted through the gelation process, making them versatile additives or bulk materials for scaffolds, microcarriers or encapsulation matrices in tissue engineering and regenerative medicine. The requirements for alginates used in biomedical applications differ significantly from those for technical applications. Particularly, the generation of novel niches for stem cells requires reliable and predictable properties of the resulting hydrogel. Ultra-high viscosity (UHV) alginates possess alginates with special physicochemical properties, and thus far, numerical simulations for the gelation process are currently lacking but highly relevant for future designs of stem cell niches and cell-based models. In this article, the gelation of UHV alginates is studied using a microscopic approach for disc- and sphere-shaped hydrogels. Based on the collected data, a multiphase continuum model was implemented to describe the cross-linking process of UHV alginate polysaccharides. The model utilizes four coupled kinetic equations based on mixture theory, which are solved using finite element software. A good agreement between simulation results and experimental data was found, establishing a foundation for future refinements in the development of an interactive tool for cell biologists and material scientists. Full article

What shapes (occupational) success in later life? This study examines the differential importance of intelligence in late childhood, socioeconomic background, and education across later occupations. The quantity and quality of educational success are thought to mediate the other dimensions. We analyzed data from N = 4387 participants in a German longitudinal large-scale study in multiple regression and mediation models to examine how childhood intelligence and socioeconomic background predict income and occupational status at different career stages. Both childhood intelligence and socioeconomic background predict status and income in adulthood, with childhood intelligence being the stronger predictor. However, education is an even stronger predictor and—once included in the model—mediates virtually all effects of childhood intelligence and socioeconomic background. This pattern remains stable across career stages, and education has unique effects on income and occupational status in later work life, even when controlling for work experience. Our results emphasize the pivotal role of education in transitioning to the labor market and further development at work, even at later career stages. Given the stronger link between childhood intelligence and educational success in Germany than in other countries, we find that Germany is one of the more intelligence-driven systems. Full article

The mechanical stability of carbon aerogels, particularly their thermal insulation performance, is closely linked to their elastic modulus. This property plays a crucial role in determining the material’s overall mechanical stability. The objective of this study is to enhance the accuracy of elastic modulus predictions for carbon aerogels using a power-scaling model. By setting the prefactor of the Gibson and Ashby equation to 1.0, accurate predictions of the elastic modulus can be achieved if the correct scaling exponent is determined. Twelve sets of experimental data were used to fit the power-scaling model, revealing that the scaling exponent for the elastic modulus of carbon aerogels typically falls between 2.2 and 3.0. This range is narrower than the 2.0 to 4.0 range reported in the literature, with a median value of 2.6 providing reliable predictions. Additionally, a relationship between the solid thermal conductivity and the elastic modulus of carbon aerogels was established using a thermal conduction model. The study also examined the elastic modulus of carbon nanotube and graphene aerogels—both allotropes of carbon aerogel. By fitting experimental data into the power-scaling model, the scaling exponents for carbon nanotube aerogels and graphene aerogels were found to range from 2.7 to 3.5 and 2.7 to 3.7, respectively. Median exponent values of 3.1 and 3.2 were identified as optimal for predicting the elastic moduli of carbon nanotube and graphene aerogels. Full article

High-temperature strain gauges are widely used in the strain monitoring of the hot-end components of aero-engines. In the application of strain gauges, the calibration of the gauge factor ($GF$) is the most critical link. Evaluating the uncertainty of $GF$ is of great significance to the accuracy analysis of measurement results. Firstly, the calibration test of the $GF$ of the Pt-W high-temperature strain gauge was carried out in the range of 25 °C to 900 °C. The real test data required for the uncertainty evaluation were obtained. Secondly, the guide to the expression of uncertainty in measurement (GUM) and the Monte Carlo method (MCM) were used to evaluate the uncertainty of $GF$ calibration test. The evaluation results of GUM and MCM were compared. Finally, the concept of the weight coefficient $W$ was proposed to quantitatively analyze the influence of each input on the uncertainty of the output $GF$. The main uncertainty source was found, which had important engineering practical significance. The results show that the mean value of $GF$ decreases with the increase in temperature nonlinearly. At 25 °C, $GF$ is 3.29, and at 900 °C, $GF$ decreases to 1.6. Through comparison and verification, the uncertainty interval given by MCM is closer to the real situation. MCM is superior to GUM, which only uses prior information for uncertainty assessment. MCM is more suitable for evaluating $GF$ uncertainty. Among multiple uncertain sources, the weight coefficient $W$ can effectively analyze $\Delta \epsilon$ as the main uncertain source. Full article

A clean environment enhances well-being and drives economic growth. BRICS nations aim to cut emissions while sustaining growth, aligning with global sustainability goals. Their strong economic progress underscores the need to explore the links between communication technology, financial efficiency, education, and renewable energy consumption (RENC). Therefore, to analyze these dynamics, this study examines data spanning from 1990 to 2020 using a rigorous methodological framework. Initially, model selection was guided by AIC and BIC criteria by ensuring optimal model fit. Furthermore, multicollinearity was assessed using the Variance Inflation Factor (VIF), while heteroscedasticity and autocorrelation issues were tested through the Breusch–Pagan Test and the Ljung–Box Test, respectively. Additionally, cross-sectional dependence (CSD) was checked, followed by stationarity analysis using the second-generation CIPS. The Westerlund Cointegration Test was employed to confirm long-run relationships. As a final preliminary test, the study uses the Hausman test for selection of the appropriate model specification. Subsequently, the PMG-ARDL approach was utilized to examine both short- and long-term dynamics. The findings reveal a significant negative relationship between RENC, Gross Domestic Product (GDP), and CO₂ emissions. Conversely, RENC exhibits a strong positive association with education (EDUC), information and communication technology (IACT), the financial markets efficiency index (FMEI), and the financial institutions efficiency index (FIEI). Finally, the robustness of the PMG-ARDL results was validated through advanced techniques, including Fully Modified OLS (FMOLS) and the Generalized Method of Moments (GMM), reinforcing the reliability of the findings. The study offers valuable policy recommendations to support sustainable development in BRICS nations. Full article

Low Earth Orbit (LEO) constellations support the transmission of various communication services and have been widely applied in fields such as global Internet access, the Internet of Things, remote sensing monitoring, and emergency communication. With the surge in traffic volume, the quality of user services has faced unprecedented challenges. Achieving accurate low Earth orbit constellation network traffic prediction can optimize resource allocation, enhance the performance of LEO constellation networks, reduce unnecessary costs in operation management, and enable the system to adapt to the development of future services. Ground networks often adopt methods such as machine learning (support vector machine, SVM) or deep learning (convolutional neural network, CNN; generative adversarial network, GAN) to predict future short- and long-term traffic information, aiming to optimize network performance and ensure service quality. However, these methods lack an understanding of the high-dynamics of LEO satellites and are not applicable to LEO constellations. Therefore, designing an intelligent traffic prediction model that can accurately predict multi-service scenarios in LEO constellations remains an unsolved challenge. In this paper, in light of the characteristics of high-dynamics and the high-frequency data streams of LEO constellation traffic, the authors propose a DST-LEO satellite-traffic prediction model (a dynamic spatio-temporal low Earth orbit satellite traffic prediction model). This model captures the implicit features among satellite nodes through multiple attention mechanism modules and processes the traffic volume and traffic connection/disconnection data of inter-satellite links via a multi-source data separation and fusion strategy, respectively. After splicing and fusing at a specific scale, the model performs prediction through the attention mechanism. The model proposed by the authors achieved a short-term prediction RMSE of 0.0028 and an MAE of 0.0018 on the Abilene dataset. For long-term prediction on the Abilene dataset, the RMSE was 0.0054 and the MAE was 0.0039. The RMSE of the short-term prediction on the dataset simulated by the internal low Earth orbit constellation business simulation system was 0.0034, and the MAE was 0.0026. For the long-term prediction, the RMSE reached 0.0029 and the MAE reached 0.0022. Compared with other time series prediction models, it decreased by 22.3% in terms of the mean squared error and 18.0% in terms of the mean absolute error. The authors validated the functions of each module within the model through ablation experiments and further analyzed the effectiveness of this model in the task of LEO constellation network traffic prediction. Full article

The evolution of smart cities is intrinsically linked to advancements in computing paradigms that support real-time data processing, intelligent decision-making, and efficient resource utilization. Edge and cloud computing have emerged as fundamental pillars that enable scalable, distributed, and latency-aware services in urban environments. Cloud computing provides extensive computational capabilities and centralized data storage, whereas edge computing ensures localized processing to mitigate network congestion and latency. This survey presents an in-depth analysis of the integration of edge and cloud computing in smart cities, highlighting architectural frameworks, enabling technologies, application domains, and key research challenges. The study examines resource allocation strategies, real-time analytics, and security considerations, emphasizing the synergies and trade-offs between cloud and edge computing paradigms. The present survey also notes future directions that address critical challenges, paving the way for sustainable and intelligent urban development. Full article

Graphs are an effective data structure for characterizing ubiquitous connections as well as evolving behaviors that emerge in inter-wined systems. Limited by the stereotype of node-to-node connections, learning node representations is often confined in a graph diffusion process where local information has been excessively aggregated, as the random walk of graph neural networks (GNN) explores far-reaching neighborhoods layer-by-layer. In this regard, tremendous efforts have been made to alleviate feature over-smoothing issues such that current backbones can lend themselves to be used in a deep network architecture. However, compared to designing a new GNN, less attention has been paid to underlying topology by graph re-wiring, which mitigates not only flaws of the random walk but also the over-smoothing risk incurred by reducing unnecessary diffusion in deep layers. Inspired by the notion of non-local mean techniques in the area of image processing, we propose a non-local information exchange mechanism by establishing an express connection to the distant node, instead of propagating information along the (possibly very long) original pathway node-after-node. Since the process of seeking express connections throughout a graph can be computationally expensive in real-world applications, we propose a re-wiring framework (coined the express messenger wrapper) to progressively incorporate express links in a non-local manner, which allows us to capture multi-scale features without using a very deep model; our approach is thus free of the over-smoothing challenge. We integrate our express messenger wrapper with existing GNN backbones (either using graph convolution or tokenized transformer) and achieve a new record on the Roman-empire dataset as well as in terms of SOTA performance on both homophilous and heterophilous datasets. Full article

This study investigates the dynamic interdependencies among key sectors of Borsa Istanbul—industrial, services, technology, banking, and electricity—using a novel network-geometric framework. Daily closure prices from 2022 to 2024 are transformed into logarithmic returns and analyzed via a sliding window approach. In each window, mutual information is computed to construct weighted networks that are filtered using Triangulated Maximally Filtered Graphs (TMFG) to isolate the most significant links. Forman–Ricci curvature is then calculated at the node level, and entropy measures over k-neighborhoods ($k=1,2,3$) capture the complexity of both local and global network structures. Cross-correlation, Granger causality, and transfer entropy analyses reveal that sector responses to macroeconomic shocks—such as inflation surges, interest rate hikes, and currency depreciation—vary considerably. The services sector emerges as a critical intermediary, transmitting shocks between the banking and both the industrial and technology sectors, while the electricity sector displays robust, stable interconnections. These findings demonstrate that curvature-based metrics capture nuanced network characteristics beyond traditional measures. Future work could incorporate high-frequency data to capture finer interactions and empirically compare curvature metrics with conventional indicators. Full article

This systematic review answers questions about whether or not a model of metacognition is well accepted and if it can be used in cognitive architecture design. Self-planning, self-monitoring, and self-evaluation are the model concepts, which are viewed as metacognitive experiences. A newly formulated theoretical approach named Attention as Action was targeted, as it is shown to be used in cognitive architecture design. In order to link the model to the theoretical approach, specific concepts like mental imagery and learning experience were researched. The method includes the statistical analysis of key phrases in articles that were collected based on a system of criteria. Data were retrieved from 91 scientific papers to allow statistical analysis of the relationship between the model of metacognition and the theoretical approach to cognitive architecture design. Several observations from the data show that the model is applicable for designing cognitive monitoring systems that depict experiences of metacognition. Furthermore, the results point out that the researched fields require explanations about the concepts defined in the theoretical approach of Attention as Action. Systematically formulated as types of internal attentional experiences, new relations are provided for researching cognitive and metacognitive concepts in terms of the cognitive cycle. Full article

The Greiner–Hormann algorithm is a commonly used polygon overlay analysis algorithm. It uses a double-linked list structure to store vertex data, and its intersection calculation step has a significant effect on the overall operating efficiency of the algorithm. To address the time-consuming intersection calculation process in the Greiner–Hormann algorithm, this paper presents two kernel functions that implement a GPU parallel improvement algorithm based on CUDA multi-threading. This method allocates a thread to each edge of the subject polygon, determines in parallel whether it intersects with each edge of the clipping polygon, transfers the number of intersection points back to the CPU for calculation, and opens up corresponding storage space on the GPU side on the basis of the total number of intersection points; then, information such as intersection coordinates is calculated in parallel. In addition, experiments are conducted on the data of eight polygons with different complexities, and the optimal thread mode, running time, and speedup ratio of the parallel algorithm are statistically analyzed. The experimental results show that when a single CUDA thread block contains 64 threads or 128 threads, the parallel transformation step of the Greiner–Hormann algorithm has the highest computational efficiency. When the complexity of the subject polygon exceeds 53,000, the parallel improvement algorithm can obtain a speedup ratio of approximately three times that of the serial algorithm. This shows that the design method in this paper can effectively improve the operating efficiency of the polygon overlay analysis algorithm in the current large-scale data context. Full article

Background/Objectives: Despite substantial research, the causal relationships between gut microbiota (GM) and age-related macular degeneration (AMD) remain unclear. We aimed to explore these causal associations using Mendelian randomization (MR) and elucidate the potential mechanisms mediated by blood metabolites. Methods: We utilized the 211 GM dataset (n = 18,340) provided by the MiBioGen consortium. AMD outcome data were sourced from the MRC Integrated Epidemiology Unit (IEU) OpenGWAS Project. We performed bidirectional MR, two mediation analyses, and two-step MR to assess the causal links between GM and different stages of AMD (early, dry, and wet). Results: Our findings indicate that the Bacteroidales S24.7 group and genus Dorea are associated with an increased risk of early AMD, while Ruminococcaceae UCG011 and Parasutterella are linked to a higher risk of dry AMD. Conversely, Lachnospiraceae UCG004 and Anaerotruncus are protective against dry AMD. In the case of wet AMD, Intestinimonas and Sellimonas increase risk, whereas Anaerotruncus and Rikenellaceae RC9 reduce it. Additionally, various blood metabolites were implicated: valine, arabinose, creatine, lysine, alanine, and apolipoprotein A1 were associated with early AMD; glutamine and hyodeoxycholate—with a reduced risk of dry AMD; and androsterone sulfate, epiandrosterone sulfate, and lipopolysaccharide—with a reduced risk of wet AMD. Notably, the association between family Oxalobacteraceae and early AMD was mediated by valine, accounting for 19.1% of the association. Conclusions: This study establishes causal links between specific gut microbiota and AMD, mediated by blood metabolites, thereby enhancing our understanding of the gut–retina axis in AMD pathophysiology. Full article

Seafloor topography data are fundamental for marine resource development, oceanographic research, and maritime rights protection. However, approximately 75% of the ocean remains unsurveyed for bathymetry. Sole reliance on shipborne measurements is insufficient for constructing a global bathymetric model within a short timeframe; consequently, satellite altimetry-based inversion techniques are essential for filling data gaps. Recent advancements have improved the variety and quality of satellite altimetry gravity data. To leverage the complementary advantages of multiparameter gravity data, we propose a 1D convolutional neural network based on a convolutional attention module, termed the Attention Channel 1D Convolutional Network (AC1D). Results of a case study of the Mariana Trench indicated that the AC1D grid predictions exhibited improved agreement with single-beam depth checkpoints, with standard deviation reductions of 6.32%, 20.79%, and 36.77% and root mean square error reductions of 7.11%, 22.82%, and 50.99% compared with those of parallel linked backpropagation, the gravity–geological method, and a convolutional neural network, respectively. The AC1D grid demonstrated enhanced stability in multibeam bathymetric validation metrics and exhibited better consistency with multibeam bathymetry data and the GEBCO2023 grid. Power spectral density analysis revealed that AC1D effectively captured rich topographic signals when predicting terrain features with wavelengths below 6.33 km. Full article

Background: Changes in the daily rhythm can trigger migraine attacks. The sensitivity for triggering attacks is closely linked to the regulation of biological rhythms controlled by the hypothalamus. In over 70 countries around the world, the time is changed between daylight savings time and standard time twice a year due to legal regulations. The aim of this study was to investigate whether the time change has an influence on migraine. Methods: In this retrospective study, the headache frequency of patients with episodic or chronic migraine at a tertiary headache center in the years 2020, 2021, and 2022 was evaluated. The primary outcome measure was the frequency of migraine occurrence on either Sunday or Monday of the time change weekend compared to Sunday or Monday before or Sunday or Monday after the time change. Results: Data from 258 patients were analyzed (86.8% women; average age: 51.5 years; average headache frequency: 7.7 days/month; 83.3% episodic migraine). Our results showed a significant increase of 6.4% in migraine frequency on the Sunday and/or Monday in the week after the time change in spring compared to the week before the change. In autumn, conversely, there was a significant reduction of 5.5% in migraine frequency on the Sunday and/or Monday one week after the time change compared to the week before the change. The factor responsible for the significant changes was the increase in migraines on Monday one week after the time change in spring and the decrease in migraines on Sunday one week after the time change in autumn. Conclusions: When switching from standard time to daylight savings time in the spring, the frequency of migraines increases significantly one week after the time change. In autumn, in comparison, there is an inverse trend with a reduction in migraine frequency. These data suggest that synchronization is disturbed when switching to daylight savings time. Conversely, synchronization normalizes in autumn. In view of the high prevalence of migraines, this can have extensive individual and social consequences. Full article

The burgeoning digital economy in China serves as a significant impetus for extensive economic growth across the nation. However, regional development within China remains imbalanced, and the issue of the digital divide cannot be overlooked. This paper is dedicated to examining the effect of the digital economy on the aggregate economic growth of China, along with a heterogeneity analysis. Using Chinese province-level data during the period of full entry into the mobile Internet era (2014–2022), this study first constructs a digital economic development index system. Using the entropy method, it calculates the digital economic index for 30 provinces in China during the 2014–2022 period. Subsequently, a panel regression model is utilized to explore how the digital economy impacts economic growth. Ultimately, the Durbin model gauges the digital economy’s spatial impact on economic expansion in China. The key findings are as follows: (1) The burgeoning digital economy in China has instigated substantial expansion of the national economy. However, its impact on economic growth gradually decreases from the eastern to the western regions. (2) China reveals a notable spatial relationship linking its digital economy and economic expansion. A distinct positive spatial relationship exists between the two. Additionally, the digital economy of China presents a positive spatial spillover effect regarding economic growth. The strongest manifestation of this effect is detected in the eastern region, trailed by the central region, and the western region displays the feeblest effect. Based on these findings, it is clear the digital economy plays a pivotal role in China’s economic growth. To expedite the high-quality development of the digital economy, the government could take additional steps. Specifically, it could enhance the construction of infrastructure related to the digital economy and increase investment in scientific and technological innovation resources. Enterprises can consider digital transformation to achieve the digital development of industries. In addition, considering the existence of the spatial spillover effect, the digital divide can be bridged and regional imbalances alleviated by enhancing coordinated regional development. Full article