Search Results (2,087)

Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using O-information, a recently introduced metric to assess high-order dependencies in complex systems; in the proposed framework, latent factors and observed variables are jointly analyzed in terms of their joint informational character. Two case studies are reported: analyzing resting fMRI data, we find that DMN and FP networks show the highest synergy, consistent with their crucial role in higher cognitive functions; concerning HeLa cells, we find that the most synergistic gene is STK-12 (AURKB), suggesting that this gene is involved in controlling the HeLa cell cycle. We believe that our approach, representing a bridge between factor analysis and the field of high-order interactions, will find wide application across several domains. Full article

A brick kiln was experimentally studied to measure the transient temperature of hot gases and the compressive strength of the bricks, using pine wood as fuel, in order to evaluate the thermal performance of the actual system. In addition, a transient combustion model based on computational fluid dynamics (CFD) was used to simulate the combustion of natural gas in the brick kiln as a hypothetical case, with the aim of investigating the potential benefits of fuel switching. The theoretical stoichiometric combustion of both pine wood and natural gas was employed to compare the mole fractions and the adiabatic flame temperature. Also, the transient hot gas temperature obtained from the experimental wood-fired kiln were compared with those from the simulated natural gas-fired kiln. Furthermore, numerical simulations were carried out to obtain the transient hot gas temperature and NOx emissions under stoichiometric, fuel-rich, and excess air conditions. The results of CO₂ mole fractions from stoichiometric combustion demonstrate that natural gas may represent a cleaner alternative for use in brick kilns, due to a 44.08% reduction in emissions. Contour plots of transient hot gases temperature, velocity, and CO₂ emission inside the kiln are presented. Moreover, the time-dependent emissions of CO₂, H₂O, and CO at the kiln outlet are shown. It can be concluded that the presence of CO mole fractions at the kiln outlet suggests that the transient combustion process could be further improved. The low firing efficiency of bricks and the thermal efficiency obtained are attributed to uneven temperatures distributions inside the kiln. Moreover, hot gas temperature and NOx emissions were found to be higher under stoichiometric conditions than under fuel-rich or excess of air conditions. Therefore, this work could be useful for improving the thermal–hydraulic and emissions performance of brick kilns, as well as for future kiln design improvements. Full article

Gypsum (CaSO₄·2H₂O) crystallization underpins numerous industrial processes, yet its response to chemical admixtures remains incompletely understood. This study investigates diffusion-controlled crystal growth in a coaxial test tube system to evaluate how three Sika^® ViscoCrete^® superplasticizers—430P, 111P, and 120P—affect nucleation, growth kinetics, morphology, and thermal behavior. The superplasticizers, selected for their surface-active properties, were hypothesized to influence crystallization via interfacial interactions. Ion diffusion was maintained quasi-steadily for 12 weeks, with crystal evolution tracked weekly by macro-photography; scanning electron microscopy and thermogravimetric/differential scanning were performed at the final stage. All admixtures delayed nucleation in a concentration-dependent manner. Lower dosages (0.5–1.0 wt%) yielded platy-to-prismatic morphologies and higher dehydration enthalpies, indicating more ordered lattice formation. In contrast, higher dosages (1.5–2.0 wt%) produced denser, irregular crystals and shifted dehydration to lower temperatures, suggesting structural defects or increased hydration. Among the additives, 120P showed the strongest inhibitory effect, while 111P at 0.5 wt% resulted in the most uniform crystals. These results demonstrate that ViscoCrete^® superplasticizers can modulate gypsum crystallization and thermal properties. Full article

Background/Objectives. Previous research with extremely and very preterm children indicates that these children obtain significantly lower results in executive functions (EFs) and in reading skills than full-term (FT) children. The comparison results do not seem to be so clear when other PT children in lower-risk conditions are studied. Many studies with typically developing and preterm (PT) children indicate that reading ability is determined, in part, by EFs. Therefore, the study of EFs and reading and their relationships in low-risk PT children is pertinent. Methods. In the present study, 111 PT children, classified into three groups with different ranges of gestational age (GA), and one group of 34 FT children participated in a longitudinal study, carried out from 4 to 9 years of age. The results obtained from the four groups in different EFs measured at 4, 5, and 8 years of age, and in reading skills at 9 years of age were compared. The possible effects of EFs on reading skills were studied through multiple linear regression analyses. Results. The results obtained indicate that no significant difference was found between FT children and any of the GA groups of PT children, either in EFs or reading skills. The effect of EFs on reading skills was low to moderate. Verbal and non-verbal working memory had a positive significant effect on decoding skills (letter names, same–different, and word reading), but not on reading comprehension processes. Higher-order EFs (cognitive flexibility and planning), as well as inhibitory control, showed positive effects on reading comprehension skills. The effects of the different EFs varied depending on the reading process. Conclusions. In conclusion, low-risk PT children do not differ from FT children in their competence in EFs or reading skills. There are long-lasting effects of EFs, measured several years before, on reading skills measured at 9 years of age. Full article

T-2 toxin and HT-2 toxin are commonly found in agricultural products and animal feed, posing serious effects to both humans and animals. This study employed combination index (CI) modeling and metabolomics to assess the combined cytotoxic effects of T-2 and HT-2 on four porcine cell types: intestinal porcine epithelial cells (IPEC-J2), porcine Leydig cells (PLCs), porcine ear fibroblasts (PEFs), and porcine hepatocytes (PHs). Cell viability assays revealed a dose-dependent reduction in viability across all cell lines, with relative sensitivities in the order: IPEC-J2 > PLCs > PEFs > PHs. Synergistic cytotoxicity was observed at low concentrations, while antagonistic interactions emerged at higher doses. Untargeted metabolomic profiling identified consistent and significant metabolic perturbations in four different porcine cell lines under co-exposure conditions. Notably, combined treatment with T-2 and HT-2 resulted in a uniform downregulation of LysoPC (22:6), LysoPC (20:5), and LysoPC (20:4), implicating disruption of membrane phospholipid integrity. Additionally, glycerophospholipid metabolism was the most significantly affected pathway across all cell lines. Ether lipid metabolism was markedly altered in PLCs and PEFs, whereas PHs displayed a unique metabolic response characterized by dysregulation of tryptophan metabolism. This study identified markers of synergistic toxicity and common alterations in metabolic pathways across four homologous porcine cell types under the combined exposure to T-2 and HT-2 toxins. These findings enhance the current understanding of the molecular mechanisms underlying mycotoxin-induced the synergistic toxicity. Full article

Stock price forecasting remains a persistent challenge in time series analysis due to complex inter-stock relationships and dynamic textual signals such as financial news. While Graph Neural Networks (GNNs) can model relational structures, they often struggle with capturing higher-order dependencies and are sensitive to noise. Moreover, sentiment signals are typically aggregated using fixed time windows, which may introduce temporal bias. To address these issues, we propose DASF-Net (Diffusion-Aware Sentiment Fusion Network), a multimodal framework that integrates structural and textual information for robust prediction. DASF-Net leverages diffusion processes over two complementary financial graphs—one based on industry relationships, the other on fundamental indicators—to learn richer stock representations. Simultaneously, sentiment embeddings extracted from financial news using FinBERT are aggregated over an empirically optimized window to preserve temporal relevance. These modalities are fused via a multi-head attention mechanism and passed to a temporal forecasting module. DASF-Net integrates daily stock prices and news sentiment, using a 3-day sentiment aggregation window, to forecast stock prices over daily horizons (1–3 days). Experiments on 12 large-cap S&P 500 stocks over four years demonstrate that DASF-Net outperforms competitive baselines, achieving up to 91.6% relative reduction in Mean Squared Error (MSE). Results highlight the effectiveness of combining graph diffusion and sentiment-aware features for improved financial forecasting. Full article

This article reviews several numerical methods for the time-dependent Schrödinger Equation (TDSE). We consider both the most commonly used approach—short-time propagation, which solves the TDSE by assuming that the Hamiltonian is time-independent over sufficiently small (time) intervals—as well as a number of higher-order alternatives. Our goal is to dispel the notion that the latter are too computationally demanding for practical use. To that end, we cover methods whose numerical building blocks are shared by short-time propagators or can be handled by standard libraries. Moreover, we make the case that these methods are best positioned to take advantage of parallel computing environments. One of the alternatives considered is a “double DVR” solver, which applies an expansion in a product basis of functions in space and time to obtain a solution (over all space and at multiple time points simultaneously) with a single linear system solve. To our knowledge, and despite its simplicity, this approach has not previously been applied to the TDSE. Full article

Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making. Graph neural networks (GNNs), known for their ability to model complex spatial structures and relational data, have achieved remarkable success in meteorological forecasting by effectively capturing spatial dependencies among distributed weather stations. However, most existing GNN-based approaches rely on pairwise station connections, limiting their capacity to represent higher-order spatial interactions. Moreover, their dependence on supervised learning makes them vulnerable to spatial heterogeneity and temporal non-stationarity. This paper introduces a novel spatial–temporal pretraining framework, Hypergraph-enhanced Meteorological Pretraining (HyMePre), which combines hypergraph neural networks with self-supervised learning to model high-order spatial dependencies and improve generalization across diverse climate regimes. HyMePre employs a two-stage masking strategy, applying spatial and temporal masking separately, to learn disentangled representations from unlabeled meteorological time series. During forecasting, dynamic hypergraphs group stations based on meteorological similarity, explicitly capturing high-order dependencies. Extensive experiments on large-scale reanalysis datasets show that HyMePre outperforms conventional GNN models in predicting temperature, humidity, and wind speed. The integration of pretraining and hypergraph modeling enhances robustness to noisy data and improves generalization to unseen climate patterns, offering a scalable and effective solution for operational weather forecasting. Full article

Nonlinear photocurrents (NPs) are electrical currents expected to be measured at the electrodes of a device consisting of an active area, sensitive to light, with a higher-order in-electric field where light-impinging geometry (LIG) is the determining factor in the experimental observation. Although the phenomenology of this light–matter interaction is clear for light directed on a lateral device plane with well-defined azimuthal and incidence angles, as well as light polarization angle, it can be quite complicated for a vertical device structure and reconsideration of the expected NP contributions is necessary in the latter case. In this study, we used a visual approach to describe the LIG for vertical device structures using a specific example of a photodiode, and showed that these angles must be redefined, namely, the interchangeability of azimuthal and incidence angles. The influence of device geometry-dependent optical illumination is reflected on the behavior of NP; therefore, the NPs that are known to be forbidden in certain LIGs can be allowed and vice versa. These results pave the way for the utilization of NPs in flexible optoelectronic applications. Full article

As a result of the catalytic decomposition of H₂O₂, hydroxyl radicals are produced. Hydroxyl radicals are strong oxidants and effectively inactivate bacteria, ensuring water disinfection without toxic chlorinated organic by-products. The kinetics of bacterial inactivation were studied in a laboratory-scale flow catalytic reactor. A granular cobalt ferrite catalyst was thoroughly characterized using XRD and XRF techniques, SEM with EDS, and Raman spectroscopy. At lower H₂O₂ concentrations, H₂O₂ decomposition follows first-order reaction kinetics. At higher H₂O₂ concentrations, the obtained kinetics lines suggest that the reaction order increases. The kinetics of bacterial inactivation in the developed flow reactor depends largely on the initial number of bacteria. The initial bacterial concentrations in laboratory tests were within the range typical of real river water. A regression model was developed that relates the degree of bacterial inactivation to the initial number of bacteria, the initial H₂O₂ concentration, and the contact time of water with the catalyst. Full article

Grassland communities consist primarily of perennial herbaceous species, with grasses forming a dominant or significant component. These ecosystems have been utilised for economic purposes since the earliest periods of human history. In the natural environment, they fulfil numerous critical functions that, despite increasing awareness of climate change, often remain undervalued. Grasslands contribute directly to climate regulation, air purification, soil conservation, flood mitigation, and public health—all of which positively affect the well-being of nearby populations. Moreover, they satisfy higher-order human needs known as “cultural” services, providing aesthetic enjoyment and recreational opportunities. These services, in tangible terms, support the development of rural tourism. The objective of this study was to examine the perception of cultural ecosystem services provided by different types of grassland communities—meadows, pastures, and lawns. The study employed a structured questionnaire to evaluate the perceived significance and functions of these communities. Respondents assessed their aesthetic and recreational value based on land-use type. To quantify these dimensions, the study applies the Recreational and Leisure Attractiveness Index (RLAI), the Aesthetic Attractiveness Index (AAI), ranking methods, and contingent valuation techniques. Based on the respondents’ declared WTP (willingness to pay) and WTA (willingness to accept) values, statistically significant differences in the perceived value of land-use types were identified. Lawns were rated highest in terms of recreational attractiveness, meadows in terms of aesthetics, while pastures achieved the highest economic values. Significant differences were also observed depending on respondents’ place of residence and academic background. The results indicate that the valuation of cultural services encompasses both functional and psychological aspects and should be integrated into local land-use and landscape planning policies. Full article

The spread of COVID-19 is influenced by multiple factors, including control policies, virus characteristics, individual behaviors, and environmental conditions, exhibiting highly complex nonlinear dynamic features. The time series of new confirmed cases shows significant nonlinearity and non-stationarity. Traditional prediction methods that rely solely on one-dimensional case data struggle to capture the multi-dimensional features of the data and are limited in handling nonlinear and non-stationary characteristics. Their prediction accuracy and generalization capabilities remain insufficient, and most existing studies focus on single-step forecasting, with limited attention to multi-step prediction. To address these challenges, this paper proposes a multi-module fusion prediction model—TSMixer-BiKSA network—that integrates multi-feature inputs, Variational Mode Decomposition (VMD), and a dual-branch parallel architecture for 1- to 3-day-ahead multi-step forecasting of new COVID-19 cases. First, variables highly correlated with the target sequence are selected through correlation analysis to construct a feature matrix, which serves as one input branch. Simultaneously, the case sequence is decomposed using VMD to extract low-complexity, highly regular multi-scale modal components as the other input branch, enhancing the model’s ability to perceive and represent multi-source information. The two input branches are then processed in parallel by the TSMixer-BiKSA network model. Specifically, the TSMixer module employs a multilayer perceptron (MLP) structure to alternately model along the temporal and feature dimensions, capturing cross-time and cross-variable dependencies. The BiGRU module extracts bidirectional dynamic features of the sequence, improving long-term dependency modeling. The KAN module introduces hierarchical nonlinear transformations to enhance high-order feature interactions. Finally, the SA attention mechanism enables the adaptive weighted fusion of multi-source information, reinforcing inter-module synergy and enhancing the overall feature extraction and representation capability. Experimental results based on COVID-19 case data from Italy and the United States demonstrate that the proposed model significantly outperforms existing mainstream methods across various error metrics, achieving higher prediction accuracy and robustness. Full article

To address the limitations of existing knowledge graph-based recommendation algorithms, including insufficient utilization of semantic information and inadequate modeling of user behavior motivations, we propose SKGRec, a novel recommendation model that integrates knowledge graph and semantic features. The model constructs a semantic interaction graph (USIG) of user behaviors and employs a self-attention mechanism and a ranked optimization loss function to mine user interactions in fine-grained semantic associations. A relationship-aware aggregation module is designed to dynamically integrate higher-order relational features in the knowledge graph through the attention scoring function. In addition, a multi-hop relational path inference mechanism is introduced to capture long-distance dependencies to improve the depth of user interest modeling. Experiments on the Amazon-Book and Last-FM datasets show that SKGRec significantly outperforms several state-of-the-art recommendation algorithms on the Recall@20 and NDCG@20 metrics. Comparison experiments validate the effectiveness of semantic analysis of user behavior and multi-hop path inference, while cold-start experiments further confirm the robustness of the model in sparse-data scenarios. This study provides a new optimization approach for knowledge graph and semantic-driven recommendation systems, enabling more accurate capture of user preferences and alleviating the problem of noise interference. Full article

Background: Soccer performance is largely dependent on high-intensity, unilateral actions such as sprints, jumps, and changes of direction. These demands can lead to strength and power differences between limbs, highlighting the importance of individualised assessment in professional players. Rotational inertial devices offer a valuable method to evaluate and train these mechanical variables separately for each leg. The aim of this study was twofold: (a) to characterise the mechanical variables derived from several lower-body strength exercises performed on rotational inertial devices, all targeting the same muscle group; and (b) to compare the mechanical variables between the dominant and non-dominant leg for each exercise. Methods: Twenty-six male professional soccer players (age = 26.3 ± 5.1 years; height = 182.3 ± 0.6 cm; weight = 75.9 ± 5.9 kg; body mass index = 22.8 ± 1.1 kg/m²; fat mass percentage = 9.1 ± 0.6%; fat-free mass = 68.8 ± 5.3 kg), all belonging to the same professional Belgian team, voluntarily participated in this study. The players completed a single assessment session consisting of six unilateral exercises (i.e., quadriceps hip, hamstring knee, adductor, quadriceps knee, hamstring hip, and abductor). For each exercise, they performed two sets of eight repetitions with each leg (i.e., dominant and non-dominant) in a randomised order. Results: The quadriceps hip exercise resulted in higher mechanical values compared to the quadriceps knee exercise in both limbs (p < 0.004). Similarly, the hamstring hip exercise produced greater values across all variables and limbs (p < 0.004), except for peak force, where the hamstring knee exercise exhibited higher values (p < 0.004). The adductor exercise showed higher peak force values for the dominant limb (p < 0.004). The between-limb comparison revealed differences only in the abductor exercise (p < 0.004). Conclusions: These findings suggest the necessity of prioritising movement selection based on targeted outcomes, although it should be considered that the differences between limb differences are very limited. Full article

Understanding the biodiversity of aquatic communities and the underlying mechanisms that shape biodiversity patterns and community dynamics is crucial for the effective conservation and management of freshwater ecosystems. However, traditional survey methods often fail to comprehensively capture species diversity, particularly for low-abundance taxa. Moreover, studies integrating both metazoan and fish communities at fine spatial scales remain limited. To address these gaps, we employed a multi-marker eDNA metabarcoding approach, targeting both the 12S and 18S rRNA gene regions, to comprehensively investigate the composition of metazoan and fish communities in the Yujiang River. A total of 12 metazoan orders were detected, encompassing 15 families, 21 genera, and 19 species. For the fish community, 32 species were identified, belonging to 25 genera, 10 families, and 7 orders. Among these, Adula falcatoides and Coptodon zillii were identified as the most prevalent and abundant metazoan and fish species, respectively. Notably, the most prevalent fish species, C. zillii and Oreochromis niloticus, are both recognized as invasive species. The Bray–Curtis distance of metazoa (average: 0.464) was significantly lower than that of fish communities (average: 0.797), suggesting higher community heterogeneity among fish assemblages. Beta-diversity decomposition indicated that variations in the metazoan and fish communities were predominantly driven by species replacement (turnover) (65.4% and 70.9% for metazoa and fish, respectively) rather than nestedness. Mantel tests further revealed that species turnover in metazoan communities was most strongly influenced by water temperature, while fish community turnover was primarily affected by water transparency, likely reflecting the physiological sensitivity of metazoans to thermal gradients and the dependence of fish on visual cues for foraging and habitat selection. In addition, a co-occurrence network of metazoan and fish species was constructed, highlighting potential predator-prey interactions between native species and Corbicula fluminea, which emerged as a potential keystone species. Overall, this study demonstrates the utility of multi-marker eDNA metabarcoding in characterizing aquatic community structures and provides new insights into the spatial dynamics and species interactions within river ecosystems. Full article