Search Results (4,573)

Topic modeling in digital news faces the dual challenge of thematic overlap and evolving semantic boundaries, especially in morphologically rich languages like Turkish. To address these obstacles, we propose a topic modeling framework enhanced with knowledge graphs that explicitly incorporates uncertainty in topic assignment. We focus on the diversity of Fuzzy Latent Semantic Analysis (FLSA) and compare the performance with Latent Dirichlet Allocation (LDA), BERTopic, and embedding-based Top2Vec on a corpus drawn from two Turkish news agencies. We evaluate each model using standard metrics for topic coherence, diversity, and interpretability. We propose Shannon entropy of node-degree distributions to measure the network complexity of knowledge graphs as topic similarity. Our results indicate that FLSA achieves perfect topic diversity, 1.000 and improved interpretability, 0.33 over LDA, 0.09 while also enhancing coherence, 0.33 vs. 0.27. Top2Vec demonstrates the strongest coherence, 0.81 and interpretability, 0.78 with high diversity, 0.97, reflecting its capacity to form semantically cohesive clusters. Entropy analysis further shows that FLSA produces the most information-rich topic networks. These findings suggest that fuzzy modeling and embedding-based approaches offer complementary strengths, uncertainty-aware flexibility, and semantic precision, thereby improving topic discovery in complex, unstructured news environments. Full article

Background/Objectives: This meta-analytical review assesses the relationship between effect size and replication success in genetic studies of athletic performance, focusing on the ACTN3 and ACE polymorphisms across power- and endurance-based sports. The analysis revealed substantial heterogeneity in reported effect sizes (overall I² = 72.3%), indicating considerable variability between studies, likely influenced by differences in population genetics, study design, and sample size. Methods: For ACTN3, the pooled effect sizes were 1.40 (95% CI: 1.18–1.65) for power sports and 1.35 (95% CI: 1.12–1.58) for endurance sports. Although the difference between these estimates is small, it reached statistical significance (p = 0.0237), reflecting the large sample size, but it remains of limited practical and clinical significance. For the ACE polymorphism, effect sizes were similar in both endurance (ES = 1.22, 95% CI: 1.05–1.41) and power sports (ES = 1.20, 95% CI: 1.03–1.43), with overlapping confidence intervals, indicating no meaningful difference in association strength between sport types. Effect sizes were calculated as odds ratios (OR) with 95% confidence intervals for case–control designs, with standardized conversion protocols applied for alternative study designs reporting standardized mean differences or regression coefficients. Results: Publication bias was detected, particularly in smaller studies on ACTN3 and power sports (Egger’s test p = 0.007). The pooled effect of ACTN3 in power sports (OR 1.40, 95% CI: 1.18–1.65, 95% PI: 0.89–2.20) was adjusted to OR 1.32 (95% CI: 1.15–1.51) following trim-and-fill publication bias correction. The high degree of heterogeneity (I² = 72.3%) cautions against overgeneralization of the pooled results and highlights the need for careful interpretation, robust replication studies, and standardized methodologies. Conclusions: The findings emphasize that, while genetic markers such as ACTN3 and ACE are statistically associated with athletic performance, the magnitude of these associations is modest and should be interpreted conservatively. Methodological differences and publication bias continue to limit the reliability of the evidence. Future research should prioritize large, well-powered, and methodologically consistent studies—ideally genome-wide approaches—to better account for the polygenic and multifactorial nature of elite athletic ability. Full article

QX-314 is a positively charged lidocaine derivative with the membrane-impermeant property. This compound applied at the intracellular side has been shown to suppress the voltage-gated Na⁺ current (I_Na), while lidocaine itself acts to suppress the hyperpolarization-activated cation current (I_h). To what extent this drug may exert any effects on various plasmalemmal ionic currents still remains largely unknown. This investigation focused on the impact of QX-314 on ionic currents in GH₃ cells derived from pituitary tumors. This compound applied extracellularly was noted to differentially suppress the amplitude of transient and late I_Na with an IC₅₀ value of 93 and 42 μM, respectively. In GH₃ cells dialyzed with QX-314 (10 μM), the I_Na(T) amplitude evoked by a brief depolarizing step was decreased, and its inactivation was increased. Moreover, QX-314, when applied extracellularly at 100 μM, diminished the amplitude of the I_h current with an IC₅₀ of 68 μM. Intracellular dialysis with QX-314 also suppressed I_h amplitude; moreover, the later application of oxaliplatin reversed this suppression. As cells were extracellularly and continually exposed to QX-314, the magnitude of the erg-mediated K⁺ current (I_K(erg)) was also effectively suppressed with an IC₅₀ value of 73 μM. Furthermore, upon intracellular dialysis with QX-314 (10 μM), the degree of the voltage-dependent hysteresis (Hys_(V)) of I_K(erg) during the long-lasting isosceles-triangular ramp voltage was decreased; during continued exposure to QX-314, further extracellular bath additions of PD118057 (10 μM) counteracted QX-314-induced suppression. However, the extracellular addition of QX-314 (100 μM) mildly suppressed the outward delayed rectifier K⁺ current in GH₃ cells. Collectively, QX-314 effectively suppressed I_Na, I_h, and I_K(erg) in GH₃ cells, a model of endocrine function, and these actions may contribute to their physiological functions, if similar effects are observed in vivo. Full article

Background/Objectives: The continued evolution and cross-species transmission of clade 2.3.4.4b H5Nx highly pathogenic avian influenza (HPAI) viruses underscores the need for broadly protective vaccines in swine, a key intermediary host. This study aimed to evaluate systemic and mucosal immune responses elicited by adenoviral-vectored (Ad) vaccines encoding a centralized consensus hemagglutinin antigen (H5CC) in mice and swine. Methods: We constructed H5CC-based vaccines that were delivered using replication-defective (Ad5 and Ad6) and replication-competent (Ad28 and Ad48) human adenoviral vectors. Using a serotype-switched prime-boost strategy, vaccines were delivered intramuscularly (IM) or intranasally (IN) in mice and swine. We determined humoral, mucosal, and cell-mediated immune responses by hemagglutination inhibition (HI), microneutralization assay (MNA), ELISA, and IFN-γ ELISpot. Protective efficacy was evaluated by lethal H5N1 challenge in mice. Results: All vaccine strategies and routes induced significant levels of anti-H5 immunity. Ad5/Ad6 IM immunization elicited strong systemic IgG and MNA titers and robust T cell responses. IN delivery with Ad5/Ad6 induced superior mucosal IgA levels in lungs and nasal secretion. In swine, Ad5/Ad6 IM conferred the highest MNA titer and T cell responses, while the IN route enhanced mucosal IgA. The Ad28/Ad48 vaccines induced immunity in a similar pattern as compared to the Ad5/Ad6 strategy, but to a slightly lesser degree, in general. The commercial H1/H3 swine influenza vaccine failed to elicit cross-protective immunity. All H5CC vaccinated mice survived lethal H5N1 challenge without weight loss. Conclusions: Adenoviral-vectored H5CC vaccines elicit broad, cross-clade immunity with route-dependent immune profiles. IM vaccination is optimal for systemic and cellular responses, while IN delivery enhances mucosal immunity. These findings support the advancement of adenoviral platforms for influenza control in swine and pandemic preparedness. Full article

Inflation is an indicator that signals emerging crises. The period of 2001–2024 witnessed numerous crises. Energy crises affect countries to varying degrees, making it important to identify those most sensitive to inflationary changes in energy prices. This study aims to assess the similarity in the dynamics of the annual inflation rates for the electricity, gas, and other fuels category (HICP—COICOP group 04.5) across Europe. In particular, we identify sub-periods and countries in which inflation indicators either led price changes in Poland or followed the inflation dynamics observed in Poland. This assessment of leading and lagging inflation dynamics is conducted using wavelet analysis, specifically analysis of the wavelet coherence with a phase difference, for Poland and 27 European countries. The analysis addresses two main questions. First, was there statistically significant coherence (correlation in the frequency domain over specific sub-periods) in energy price inflation processes between Poland and other countries? Second, which countries exhibited energy price inflation dynamics that led or lagged behind the pattern in Poland? For many countries, coherence with Poland was not significant in regard to short-term fluctuations (2–6 months) but became significant over longer time scales. Furthermore, at longer periodicities, Poland’s energy inflation dynamics were synchronous with those of many European countries, especially during the period of Russian aggression against Ukraine. This analysis identifies statistically significant coherence between Poland and the chosen European countries. Germany and Lithuania frequently led Polish energy price inflation, whereas other countries, such as Bulgaria and Spain, often lagged behind. These results reveal dynamic patterns in the time–frequency co-movements of energy inflation across Europe. Full article

Geohazard investigation in volcanic fields is essential for understanding and mitigating risks associated with volcanic activity. Volcanic vents are often concealed by processes such as faulting, subsidence, or uplift, which complicates their detection and hampers hazard assessment. To address this challenge, we developed a predictive framework that integrates high-resolution gravity data with multiple machine learning algorithms. Logistic Regression, Gradient Boosting Machine (GBM), Decision Tree, Support Vector Machine (SVM), and Random Forest models were applied to analyze the gravitational characteristics of known volcanic vents and predict the likelihood of undiscovered vents at other locations. The problem was formulated as a binary classification task, and model performance was assessed using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The Random Forest algorithm yielded optimal outcomes: 95% classification accuracy, AUC-ROC score of 0.99, 75% geographic correspondence between real and modeled vent sites, and a 95% certainty degree. Spatial density analysis showed that the distribution patterns of predicted and actual vents are highly similar, underscoring the model’s reliability in identifying vent-prone areas. The proposed method offers a valuable tool for geoscientists and disaster management authorities to improve volcanic hazard evaluation and implement effective mitigation strategies. These results represent a significant step forward in our ability to model volcanic dynamics and enhance predictive capabilities for volcanic hazard assessment. Full article

To study the adsorption of lanthanide (Ln) atoms on graphene containing a Stone–Wales defect, we used a cluster model (SWG) and performed calculations at the PBE-D2/DNP level of the density functional theory. Our previous study, where the above combination was complemented with the ECP pseudopotentials, was only partially successful due to the impossibility of calculating terbium-containing systems and a serious error found for the SWG complex with dysprosium. In the present study we employed the DSPP pseudopotentials and completely eliminated the latter two failures. We analyzed the optimized geometries of the full series of fifteen SWG + Ln complexes, along with their formation energies and electronic parameters, such as frontier orbital energies, atomic charges, and spins. In many regards, the two series of calculations show qualitatively similar features, such as roughly M-shaped curves of the adsorption energies and trends in the changes in charge and spin of the adsorbed Ln atoms, as well as the spin density plots. However, the quantitative results can differ significantly. For most characteristics we found no evident correlation with the lanthanide contraction. The only dataset where this phenomenon apparently manifests itself (albeit to a limited and irregular degree) is the changes in the closest Ln^…C approaches. Full article

The identification of interdependent edges plays a critical role in improving information propagation efficiency and enhancing network robustness in interdependent networks. However, existing methods exhibit significant limitations when identifying interdependent edges between networks with substantial differences in edge density. This paper proposes a greedy module matching-based method for sparse interdependent edge identification in similar-order heterogeneous networks. The method utilizes degree entropy and betweenness centrality as node characteristic values for sparse and dense networks, respectively. It first leverages structural differences between sparse and dense networks to determine the upper bound of interdependent edges. Then, a clustering algorithm is employed to identify modules in both networks that align with the estimated number of interdependent edges. Finally, a greedy algorithm is applied to infer interdependent edges between sparse and dense networks. The proposed method is validated using synthetic networks and power-communication networks, with network robustness and connection efficiency as evaluation metrics. Additionally, further validation is conducted through applications in problem–answer networks. Experimental results demonstrate that the proposed approach significantly improves the prediction of sparse interdependent relationships in heterogeneous complex networks and has broad applicability across multiple domains. Full article

Sour camel milk, as a nutritious fermented dairy product, faces challenges in terms of quality stability. Sparassis crispa, due to its antioxidant and antibacterial properties, shows potential in improving food quality. This study aimed to investigate the effects of different active components of Sparassis crispa on the quality of sour camel milk. The results indicated that Component I was the most effective Sparassis crispa component in enhancing the quality of sour camel milk. The components of Component I were identified as LysoPC(0_0_18_2(9Z,12Z)), LysoPC(18_1(11Z)_0_0), and N-(2-hydroxymethyl-3-chloro-4-hydroxyphenyl) anthranilic acid, among others. It increased the total viable count of lactic acid bacteria (LAB) and water-holding capacity (WHC) while improving the texture of sour camel milk. Metabolomics analysis revealed that the first component of sour camel milk (FCS) and Sparassis crispa sour camel milk (SS) have a high degree of similarity in the composition of flavor substances. The characteristic flavor metabolites included 2-amylfuran, isoamyl alcohol, 2-methylbutyraldehyde, and 2-ethyl-1-hexanol. Additionally, the supplementation of Component I increased the levels of metabolites such as amino acids, free fatty acids, organic acids, and carbohydrates, thereby contributing to the enhanced taste and nutritional quality of sour camel milk. This intervention also strengthened carbohydrate and amino acid metabolism in LAB. These findings provide a theoretical basis for utilizing Component I to improve the quality of sour camel milk. Full article

Bees are vital pollinators that maintain plant populations by transporting pollen among individuals; however, bees are declining, and information on how habitat characteristics alter the catch of bees in traps is needed to better assess monitoring. Few studies have measured how catch in passive traps may be altered by floral resources despite the well-known dependence of pollinators on forbs. We investigated the degree to which pollinating insects were attracted to vane traps and bee bowls placed at sites that varied in flower densities (0–800 flowers/m²). We also assessed if the catch of bees was better explained by flower characteristics directly around traps (subsite) or average flower characteristics at a site. Floral density, richness and surface area were measured in 1 m² quadrats at each subsite. The surface area of flowers explained more variance in bees captured compared to the density or richness of flowers. Traps placed in areas with lower flower surface area captured the more bees and a more diverse sample. Floral resources at the subsite and site explained a similar amount of variance in the number of bees captured, suggesting that pollinators respond to flowers at both scales. We provide a method of correcting pollinator abundance by flower surface area to make catch in passive traps more comparable among areas. We can select sites that minimize or maximize the catch of bees by understanding how floral resources change the effectiveness of passive traps. Full article

We found analytic approximations for the Bessel function of the first kind $J_{\nu }\left(x\right)$, valid for any real value of x and any value of $\nu$ in the interval (−1/2, 3/2). The present approximation is exact for $\nu =-1/2$, $\nu =1/2$, and $\nu =3/2$, where an exact function for each case is well known. The maximum absolute errors for $\nu$ near these peculiar values are very small. Throughout the interval, the absolute values remain below 0.05. The structure of the approximate function is defined considering the corresponding power series and asymptotic expansions, and they are quotients of three polynomials of the second degree combined with trigonometrical functions and fractional powers. This is, in some way, the Multipoint Quasi-rational Approximation (MPQA) technique, but now only two variables are considered, x and $\nu$, which is novel, since in all previous publications only the variable x was considered and $\nu$ was given. Furthermore, in the case of $J_{-1/2}\left(x\right)$, $J_{1/2}\left(x\right)$, and $J_{3/2}\left(x\right)$, the corresponding exact function was also a condition to be considered and fulfilled. It is important to point out that the zeros of the exact functions and the approximate ones are also almost coincident with small relative errors. Finally, the approximation presented here has the property of preservation of symmetry for $\nu >0$, i.e., when there is a sign change in the variable x, the corresponding change agrees with a similar change in the power series of the exact function. Full article

Motor impairments significantly affect individuals’ ability to perform activities of daily living, reducing autonomy and quality of life. In response to this, robot-assisted rehabilitation has emerged as an effective and practical solution, enabling controlled limb movements and supporting functional recovery. This study presents the development of an upper-limb exoskeleton designed to assist rehabilitation by integrating neurophysiological signal processing and real-time control strategies. The system incorporates a proportional–derivative (PD) controller to execute cyclic flexion and extension movements based on a sinusoidal reference signal, providing repeatability and precision in motion. The exoskeleton integrates a brain–computer interface (BCI) that utilizes electroencephalographic signals for therapy selection and engagement enabling user-driven interaction. The EEG data extraction was possible by using the UltraCortex Mark IV headset, with electrodes positioned according to the international 10–20 system, targeting alpha-band activity in channels O1, O2, P3, P4, Fp1, and Fp2. These channels correspond to occipital (O1, O2), parietal (P3, P4), and frontal pole (Fp1, Fp2) regions, associated with visual processing, sensorimotor integration, and attention-related activity, respectively. This approach enables a more adaptive and personalized rehabilitation experience by allowing the user to influence therapy mode selection through real-time feedback. Experimental evaluation across five subjects showed an overall mean accuracy of 86.25% in alpha wave detection for EEG-based therapy selection. The PD control strategy achieved smooth trajectory tracking with a mean angular error of approximately 1.70°, confirming both the reliability of intention detection and the mechanical precision of the exoskeleton. Also, our core contributions in this research are compared with similar studies inspired by the rehabilitation needs of stroke patients. In this research, the proposed system demonstrates the potential of integrating robotic systems, control theory, and EEG data processing to improve rehabilitation outcomes for individuals with upper-limb motor deficits, particularly post-stroke patients. By focusing the exoskeleton on a single degree of freedom and employing low-cost manufacturing through 3D printing, the system remains affordable across a wide range of economic contexts. This design choice enables deployment in diverse clinical settings, both public and private. Full article

Individual cow identification is a prerequisite for automatically monitoring behavior patterns, health status, and growth data of each cow, and can provide the assistance in selecting excellent cow individuals for breeding. Despite high recognition accuracy, traditional implantable electronic devices such as RFID (i.e., Radio Frequency Identification) can cause some degree of harm or stress reactions to cows. Image-based methods are widely used due to their non-invasive advantages, but these methods have poor adaptability to different environments and target size, and low detection accuracy in complex scenes. To solve these issues, this study designs a Dy_Conv (i.e., dynamic convolution) module and innovatively constructs a Dynamic_Bottleneck module based on the Dy_Conv and S2Attention (Sparse-shift Attention) mechanism. On this basis, we replaces the first and fourth bottleneck layers of Resnet50 with the Dynamic_Bottleneck to achieve accurate extraction of local features and global information of cows. Furthermore, the QAConv (i.e., query adaptive convolution) module is introduced into the front end of the backbone network, and can adjust the parameters and sizes of convolution kernels to adapt to the scale changes in cow targets and input images. At the same time, NAM (i.e., normalization-based attention module) attention is embedded into the backend of the network to achieve the feature fusion in the channels and spatial dimensions, which contributes to better distinguish visually similar individual cows. The experiments are conducted on the public datasets collected from different cowsheds. The experimental results showed that the Rank-1, Rank-5, and mAP metrics reached 96.8%, 98.9%, and 95.3%, respectively. Therefore, the proposed model can effectively capture and integrate multi-scale features of cow body appearance, enhancing the accuracy of individual cow identification in complex scenes. Full article

Rapid regional development and intensified human activities increasingly disturb ecosystems, posing substantial threats to the integrity of large-scale ecological zones. As a World Natural Heritage site and a crucial national ecological zone, the Zhangjiajie Scenic Area plays a pivotal role in China’s environmental conservation efforts. To comprehensively assess tourism ecological security in the Scenic Area and strengthen the scientific basis for resource management and policymaking, this study developed a multi-dimensional ecological security evaluation system covering 2010–2024, incorporating dynamic changes in perturbation, reaction, and governance. Using entropy weight–TOPSIS and coupling coordination models, combined with obstacle degree analysis, we examined the temporal trajectory of ecological security and analyzed its underlying driving mechanisms. The study also examined factors influencing the sustainable development of the ecosystem. The results indicate the following: (1) Tourism ecological security in the Scenic Area followed a V-shaped trajectory of “rapid degradation—steady recovery—impact and rebound.” It declined sharply to an unsafe level between 2010 and 2014, steadily recovered from 2015 to 2019, briefly dropped in 2020, and then rebounded, reaching a peak evaluation value of 0.519 in 2024. (2) The co-evolution of perturbation, reaction, and governance subsystems has matured: their coupling coordination degree has increased annually and has remained at the level of “intermediate coordination” since 2020. The reaction subsystem plays a central role, serving as a bridge between perturbation and governance. (3) The driving factors exhibit a phased evolutionary pattern of “elements—facilities—structure—function.” Cultivated land area, total road mileage, and artificial afforestation area constitute the main long-term constraints. This research provides important insights for strengthening ecological security and sustainability in the Scenic Area while advancing regional ecosystem development. It also offers valuable guidance for ecological security management and policymaking in similar nature reserves. Full article

Plant heat shock proteins (Hsps) are from a diverse and ancient protein family, with small Hsps of ~20 kDa molecular weight classified as Hsp20s. As a key transcription factor in the jasmonic acid (JA) pathway, myelocytomatosis protein 2 (MYC2) plays a vital role in stamen development. In this study, we identified six genes with significantly altered expression levels using previous RNA-Seq data from PhMYC2a-overexpressing and methyl jasmonate (MeJA)-treated petunia. Interestingly, five of these are Hsp20 family members (PhHsp16.0A, PhHsp16.1, PhHsp16.8, PhHsp21.9, and PhHsp40.8). Yeast one-hybrid (Y1H) and dual-luciferase assays demonstrated that PhMYC2a directly binds their promoters, indicating a collective effect. Thus, a genome-wide analysis was conducted and a total of 38 genes encoding Hsp20s were identified in the reference genome of Petunia axillaris. Phylogenetic analysis revealed that 38 members of Hsp20s were irregularly distributed on 34 chromosome scaffolds and separated into 13 subfamilies, with only PaHsp16.0A and 16.1, among the five selected Hsp20s, being in the same Cytosol IV (CIV) subfamily. Conserved motif analysis suggested that the PaHsp20 gene family members may have a high degree of conservation. The promoter sequence analysis suggested that the promoter regions of PaHsp20 genes contained multiple light- and hormone-related cis-regulatory elements. Subsequently, spatiotemporal expression patterns, analyzed by qRT-PCR, showed that PhHsp16.0A and PhHsp16.1 had relatively high expression levels in flowers, with similar expression patterns at various stages of flower bud and anther development. Furthermore, virus-induced gene silencing (VIGS) of PhHsp16.0A and PhHsp16.1 resulted in significantly reduced pollen fertility, indicating their regulation in the process of flower development and echoing the role of PhMYC2a. This study highlights the pivotal role of Hsp20s in MYC2a-mediated regulatory mechanisms during petunia pollen development. Full article