Search Results (14,643)

To address the challenges of image inpainting in scenarios with extensive or irregular missing regions—particularly detail oversmoothing, structural ambiguity, and textural incoherence—this paper proposes an Image Structure-Guided (ISG) framework that hierarchically integrates structural priors with semantic-aware texture synthesis. The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent structural maps through contextual feature propagation; (2) Structure-Constrained Texture Synthesis, wherein a multi-scale generator with hybrid dilated convolutions and channel attention mechanisms iteratively refines high-fidelity textures under explicit structural guidance. The framework introduces three innovations: (1) a hierarchical feature fusion architecture that synergizes multi-scale receptive fields with spatial-channel attention to preserve long-range dependencies and local details simultaneously; (2) spectral-normalized Markovian discriminator with gradient-penalty regularization, enabling adversarial training stability while enforcing patch-level structural consistency; and (3) dual-branch loss formulation combining perceptual similarity metrics with edge-aware constraints to align synthesized content with both semantic coherence and geometric fidelity. Our experiments on the two benchmark datasets (Places2 and CelebA) have demonstrated that our framework achieves more unified textures and structures, bringing the restored images closer to their original semantic content. Full article

The aim of this paper is to present our experience with the diagnosis and management of nine patients diagnosed with Leber’s hereditay optic neuropathy. Materials and methods: We conducted a prospective, observational study that included nine patients treated with idebenone, followed for a period of 18 months. Results: Our findings suggest that the impact of treatment varies significantly depending on the disease phase. In the acute phase, visual acuity deteriorated from 0.67 logMAR at onset to 0.97 logMAR at 3 months, followed by a slight improvement to 0.88 logMAR at 9 months. In the chronic phase, average values decreased progressively from 1.44 logMAR at onset to 1.26 logMAR at 12 and 18 months. We also observed a consistent treatment benefit over time in eyes harbouring the m.11778 G > A mutation. Although the most powerful predictor of visual outcome remains the mtDNA genotype, young age at onset is correlated with a better prognosis. In the acute phase, more cases of a clinically relevant benefit were observed than expected (33.33% versus 22.22% expected), and fewer clinically relevant worsening cases were observed (0% versus 11.11% expected). Regarding OCT measurement, our study highlighted a significant difference in peripapillary retinal nerve fiber layer thickness between the initial evaluation and the 6-month follow-up (100.83 µm ± 30.2 at baseline versus 96.7 µm ± 24.8 at 6 months). Conclusions: Our paper demonstrates the benefit of idebenone treatment in improving visual acuity in patients with Leber hereditary optic neuropathy. We highlighted the importance of long-term treatment, emphasizing that extended administration is key to achieving favorable outcomes. Full article

Growth hormone (GH) signaling has been implicated in tumor progression and therapy resistance across multiple cancer types, yet its role in bladder cancer remains largely unexplored. In this study, we investigated the impact of GH and its receptor (GHR) on therapy resistance and disease progression in urothelial carcinoma (UC) through integrated transcriptomic and in vitro analyses. Transcriptomic profiling of The Cancer Genome Atlas bladder cancer cohort revealed that high tumoral GHR expression was associated with differential upregulation of genes involved in drug efflux, epithelial-to-mesenchymal transition (EMT), and extracellular matrix (ECM) remodeling. Notably, elevated GHR levels correlated with significantly reduced overall survival in patients with UC. In parallel, in vitro experiments demonstrated that GH promotes chemoresistance in UC cell lines via upregulation of ATP-binding cassette-containing (ABC) transporters and activation of EMT. GH also modulated ECM-remodeling-associated genes in a chemotherapy-dependent manner, including matrix metalloproteinases and tissue inhibitors of metalloproteinases. Importantly, these effects were abrogated by Pegvisomant, a GHR antagonist, indicating the functional relevance of GH/GHR signaling in the mediation of these phenotypes. Collectively, our findings support a mechanistic role for GH signaling in driving therapy resistance and tumor aggressiveness in bladder cancer and suggest GHR antagonism as a potential therapeutic strategy to improve treatment outcomes. Full article

The performance of the UAVs while executing various mission profiles greatly depends on the selection of planning algorithms. Reinforcement learning (RL) algorithms can effectively be utilized for robot path planning. Due to random action selection in case of action ties, the traditional Q-learning algorithm and its other variants face the issues of slow convergence and suboptimal path planning in high-dimensional navigational environments. To solve these problems, we propose an improved deep Q-network (DQN), incorporating an efficient tie-breaking mechanism, prioritized experience replay (PER), and L2-regularization. The adopted tie-breaking mechanism improves the action selection and ultimately helps in generating an optimal trajectory for the UAV in a 3D cluttered environment. To improve the convergence speed of the traditional Q-algorithm, prioritized experience replay is used, which learns from experiences with high temporal difference (TD) error and avoids uniform sampling of stored transitions during training. This also allows the prioritization of high-reward experiences (e.g., reaching a goal), which helps the agent to rediscover these valuable states and improve learning. Moreover, L2-regularization is adopted that encourages smaller weights for more stable and smoother Q-values to reduce the erratic action selections and promote smoother UAV flight paths. Finally, the performance of the proposed method is presented and thoroughly compared against the traditional DQN, demonstrating its superior effectiveness. Full article

Incorporation of lanthanide (Ln) and actinide (An) ions into the human body poses significant chemotoxic and radiotoxic risks, necessitating effective decorporation strategies. This study investigates the displacement of biologically relevant ligands from trivalent ions of europium, Eu(III), and curium, Cm(III), in artificial biofluids by various complexing agents, i.e., ethylenediaminetetraacetic acid (EDTA), ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid (EGTA), diethylenetriaminepentaacetic acid (DTPA), and spermine-based hydroxypyridonate chelator 3,4,3-LI(1,2-HOPO) (HOPO). Utilizing a modified unified bioaccessibility method (UBM) to simulate gastrointestinal conditions, we conducted concentration-dependent displacement experiments at both room and body temperatures. Time-resolved laser-induced fluorescence spectroscopy (TRLFS) supported by ²H nuclear magnetic resonance (NMR) spectroscopy and thermodynamic modelling revealed the complexation efficacy of the agents under physiological conditions. Results demonstrate that high affinity, governed by complex stability constants and ligand pK_a values, is critical to overcome cation and anion competition and leads to effective decorporation. Additionally, there is evidence that cyclic ligands are inferior to linear ligands for this application. HOPO and DTPA exhibited superior displacement efficacy, particularly in the complete gastrointestinal tract simulation. This study highlights the utility of in vitro workflows for evaluating decorporation agents and emphasizes the need for ligands with optimal binding characteristics for enhanced chelation therapies. Full article

The intensifying global competition for cobalt resources and the increasing likelihood of trade decoupling and disruption are profoundly impacting the global energy transition. In a globalized trade environment, a decline in cobalt supply from exporting countries can spread through the trade network, negatively affecting demand countries. Quantitative analysis of the negative impacts of export supply declines in various countries can help identify early risks in the global supply chain, providing a scientific basis for energy security, industrial development, and policy responses. This study constructs a trade network using trade data on metal cobalt, cobalt powder, cobalt concentrate, and ore sand from the upstream (mining, selection, and smelting) stages of the cobalt industry chain across 155 countries and regions from 2000 to 2023. Based on this, an impact diffusion model is established, incorporating the trade volumes and production levels of cobalt resources in each country to measure their resilience to shocks and determine their direct or indirect dependencies. The study then simulates the impact on countries (regions) when each country’s supply is completely interrupted or reduced by 50%. The results show that: (1) The global cobalt trade network exhibits a ‘one superpower, multiple strong players’ characteristic. Congo (DRC) has a far greater destructive power than other countries, while South Africa, Zambia, Australia, Russia, and other countries have higher destructive power due to their strong storage and production capabilities, strong smelting capabilities, or as important trade transit countries. (2) The global cobalt trade network primarily consists of three major risk areas. The African continent, the Philippines and Indonesia in Southeast Asia, Australia in Oceania, and Russia, the United States, China, and the United Kingdom in Eurasia and North America form the primary risk zones for global cobalt trade. (3) When there is a complete disruption or a 50% reduction in export supply, China will suffer the greatest average demand loss, far exceeding the second-tier countries such as the United States, South Africa, and Zambia. In contrast, European countries and other regions worldwide will experience the smallest average demand loss. Full article

Brain-derived neurotrophic factor (BDNF) is associated with nicotine use behaviours, the intensity of nicotine cravings, and the experience of withdrawal symptoms. Given the established influence of sex, brain-derived neurotrophic factor variants, personality traits and anxiety levels on nicotine use, this study aimed to conduct a comprehensive association analysis of these factors within a cohort of women who use nicotine. The study included 239 female participants: 112 cigarette users (mean age = 29.19, SD = 13.18) and 127 never-smokers (mean age = 28.1, SD =10.65). Study participants were examined using the NEO Five-Factor Inventory and the State–Trait Anxiety Inventory. Genotyping of rs6265, rs10767664, and rs2030323 was performed by real-time PCR using an oligonucleotide assay. We did not observe significant differences in the distribution of either genotype or allele of rs6265, rs10767664 and rs2030323 between groups. However, compared to the never-smokers, cigarette users scored significantly lower on the Agreeableness (5.446 vs. 6.315; p = 0.005767; d_Cohen’s = 0.363; η² = 0.032) and the Conscientiousness (5.571 vs. 6.882; p = 0.000012; d_Cohen’s = 0.591; η²= 0.08) scales. There was significant linkage disequilibrium between all three analysed polymorphic variants—between rs6265 and rs10767664 (D′ = 0.9994962; p < 2.2204 × 10⁻¹⁶), between rs6265 and rs2030323 (D′ = 0.9994935; p < 2.2204 × 10⁻¹⁶) and between rs10767664 and rs20330323 (D′ = 0.9838157; p < 2.2204 × 10⁻¹⁶), but the haplotype association analysis revealed no significant differences. While our study did not reveal an association between the investigated brain-derived neurotrophic factor polymorphisms (rs6265, rs10767664 and rs2030323) and nicotine use, it is essential to acknowledge that nicotine dependence is a complex, multifactorial phenotype. Our study expands the current knowledge of BDNF ’s potential role in addictive behaviours by exploring the understudied variants (rs10767664 and rs2030323), offering a novel contribution to the field and paving the way for future research into their functional relevance in addiction-related phenotypes. The lower Agreeableness and Conscientiousness scores observed in women who use nicotine compared to never-smokers suggest that personality traits play a significant role in nicotine use in women. The observed relationship between personality traits and nicotine use lends support to the self-medication hypothesis, suggesting that some women may initiate or maintain nicotine use as a coping mechanism for stress and negative affect. Public health initiatives targeting women should consider personality and psychological risk factors in addition to biological risks. Full article

An innovative high-order dimensionality reduction approach, which integrates a condensed finite-difference scheme with proper orthogonal decomposition techniques, has been explored for solving diffusion equations. The difference scheme with forth order accurate in both space and time is introduced through the idea of interpolation approximation. The quartic spline function and $(2,2)$ Padé approximation were utilized in space and time discretization, respectively. The stability and convergence were proven. Moreover, the dimensionality reduction formulas were derived using the proper orthogonal decomposition (POD) method, which is based on the matrix representation of the compact finite-difference scheme. The bases of the POD method were established by cumulative contribution rate of the eigenvalues of snapshot matrix that is different from the traditional ways in which the bases were established by the first eigenvalues. The method of cumulative contribution rate can optimize the degree of freedom. The error analysis of the reduced bases high-order POD finite-difference scheme was provided. Numerical experiments are conducted to validate the soundness and dependability of the reduced-order algorithm. The comparisons between the $(2,2)$ finite-difference method, the traditional POD method, and reduced dimensional method with cumulative contribution rate were discussed. Full article

Liquid rocket engines occasionally experience abnormal phenomena with unclear mechanisms, causing difficulty in design improvements. To address the above issue, a data mining method that combines ante hoc explainability, post hoc explainability, and prediction accuracy is proposed. For ante hoc explainability, a feature selection method driven by data, models, and domain knowledge is established. Global sensitivity analysis of a physical model combined with expert knowledge and data correlation is utilized to establish the correlations between different types of parameters. Then a two-stage optimization approach is proposed to obtain the best feature subset and train the prediction model. For the post hoc explainability, the partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP) analysis are used to discover complex patterns between input features and the dependent variable. The effectiveness of the hybrid feature selection method and its applicability under different noise combinations are validated using synthesized data from a high-fidelity simulation model of a pressurization system. Then the analysis of the causes of a large vibration phenomenon in an active engine shows that the prediction model has good accuracy, and the feature selection results have a clear mechanism and align with domain knowledge, providing both accuracy and interpretability. The proposed method shows significant potential for data mining in complex aerospace products. Full article

Olive stones (OS) are a by-product of great interest from olive oil mills and the table olive industry due to their high content of phenolic compounds. In this work, the extraction of phenolic compounds from OS via microwave-assisted extraction (MAE) with aqueous acetone was assayed. A central composite design of experiments was used to determine the optimal extraction conditions, with the independent variables being temperature, process time, and aqueous acetone (v/v). The dependent variables were the total content of phenolic compounds (TPC) measured by the Folin–Ciocalteu method and the main phenolic compounds identified and quantified by UPLC. Under optimal conditions (75 °C, 20 min, and 60% acetone), 3.32 mg TPC was extracted from 100 g of dry matter (DM) OS. The most suitable extraction conditions were different for each polyphenol. Therefore, 292.11 μg vanillin/g DM; 10.94 μg oleuropein/g DM; and 10.11 protocatechuic acid μg/g DM were obtained under conditions of 60 °C, 15 min, and 100% acetone; 43.8 °C, 10.45 min, and 61.3% acetone; and 64.8 °C, 16.58 min, and 97.8% acetone, respectively. Finally, MAE was compared with the traditional Soxhlet method under the same conditions. As a result, MAE was proven to be an enhanced and more feasible method for polyphenol extraction from OS. Full article

The adoption of artificial intelligence (AI) in marketing and social media is gaining scholarly interest. While AI technologies offer significant potential for enhancing customer engagement (CE), their effectiveness depends on an industry’s level of digital and AI readiness. This is especially relevant for people-centric sectors such as tourism and hospitality, where digital maturity remains relatively low. This study aims to understand how AI supports CE and social media marketing (SMM), and to identify the key antecedents and consequences of its use. Using the PRISMA approach, we conduct a systematic review of 55 peer-reviewed empirical studies on AI-based CE and SMM. Our analysis identifies the main contributing theories and AI technologies in the field, and uncovers four central themes: (1) AI in customer service and user experience design, (2) AI-based customer relationships with brands, (3) AI-driven development of customer trust, and (4) cultural differences and varying levels of AI readiness. We also develop a conceptual framework that outlines the determinants and outcomes of AI-based CE, including relevant moderators and mediators. The study concludes with directions for future research and provides theoretical and managerial implications, particularly for the tourism and hospitality industries. Full article

By integrating EarthCARE W-band doppler cloud radar observations with GPM Ku/Ka-band dual-frequency precipitation radar data, this study constructs a novel global “pseudo tripe-frequency” radar coincidence dataset comprising 2886 coincidence events (about one-third of the events detected precipitation), aiming to systematically investigating band-dependent responses to cloud and precipitation structure. Results demonstrate that the W-band is highly sensitive to high-altitude cloud particles and snowfall (reflectivity < 0 dBZ), yet it experiences substantial signal attenuation under heavy precipitation conditions, and with low-altitude reflectivity reductions exceeding 50 dBZ, its probability density distribution is more widespread, with low-altitude peaks increasing first, and then decreasing as precipitation increases. In contrast, the Ku and Ka-band radars maintain relatively stable detection capabilities, with attenuation differences generally within 15 dBZ, but its probability density distribution exhibits multiple peaks. As the precipitation rate increases, the peak value of the dual-frequency ratio (Ka/W) gradually rises from approximately 10 dBZ to 20 dBZ, and can even reach up to 60 dBZ under heavy rainfall conditions. Several cases analyses reveal clear contrasts: In stratiform precipitation regions, W-band radar reflectivity is higher above the melting layer than below, whereas the opposite pattern is observed in the Ku and Ka bands. Doppler velocities exceeding 5 m s⁻¹ and precipitation rates surpassing 30 mm h⁻¹ exhibit strong positive correlations in convection-dominated regimes. Furthermore, the dataset confirms the impact of ice–water cloud phase interactions and terrain-induced precipitation variability, underscoring the complementary strengths of multi-frequency radar observations for capturing diverse precipitation processes. Full article

The “That Girl” self-optimization trend on TikTok, promoting beauty and productivity, had over 17.4 billion views by August 2024. “That Girl” video clips showcase perfectly organized daily routines, fitness activities, and healthy eating—allegedly to inspire other users to aspire to similar flawlessness. Based on social comparison theory, the “That Girl” archetype serves as an upward comparison target. We expected detrimental effects of viewing “That Girl” content on young women in terms of positive and negative affect and body satisfaction. Expanding other research in this area, possible effects on self-discipline and dieting intentions were explored. Focusing on immediate intraindividual changes, a preregistered two-group online experiment using a pre–post measurement design was conducted. Female participants (N = 76) watched four minutes of either 16 video clips showing “That Girl” content or nature videos (control condition). Mixed ANOVAs provided evidence of a significant adverse influence of watching “That Girl” videos on female recipients regarding all dependent variables with medium or large effect sizes. Post-hoc analyses revealed that these effects were driven by participants who reported upward comparisons to “That Girls”. Based on these results, the positive impact on self-improvement—as proclaimed by contributors of the “That Girl” trend—is critically questioned. Full article

Dense video captioning involves identifying, localizing, and describing multiple events within a video. Capturing temporal and contextual dependencies between events is essential for generating coherent and accurate captions. To effectively capture temporal and contextual dependencies between events, we propose Dense Video Captioning with Dual Contextual, Systematic, and Linguistic Modules (DVC-DCSL), a novel dense video captioning model that integrates contextual, semantic, and linguistic modules. The proposed approach employs two uni-directional LSTMs (forward and backward) to generate distinct captions for each event. A caption selection mechanism then processes these outputs to determine the final caption. In addition, contextual alignment is improved by incorporating visual and textual features from previous video segments into the captioning module, ensuring smoother narrative transitions. Comprehensive experiments conducted using the ActivityNet dataset demonstrate that DVC-DCSL increases the Meteor score from 11.28 to 12.71, representing a 12% improvement over state-of-the-art models in the field of dense video captioning. These results highlight the effectiveness of the proposed approach in improving dense video captioning quality through contextual and linguistic integration. Full article

The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density estimation, projection pursuit, log-spline estimation, and wavelet-based estimation. The study is extended with modified versions of these methods, where the sample is first clustered using the EM algorithm based on Gaussian mixture components prior to density estimation. Estimation accuracy is quantitatively evaluated using MAE and MAPE criteria, with simulation experiments conducted over 100,000 replications for various sample sizes. The results show that estimation accuracy strongly depends on the density structure, sample size, and degree of component overlap. Clustering before density estimation significantly improves accuracy for multimodal and asymmetric densities. Although no formal statistical tests are conducted, the performance improvement is validated through non-overlapping confidence intervals obtained from 100,000 simulation replications. In addition, several decision-making systems are compared for automatically selecting the most appropriate estimation method based on the sample’s statistical features. Among the tested systems, kernel discriminant analysis yielded the lowest error rates, while neural networks and hybrid methods showed competitive but more variable performance depending on the evaluation criterion. The findings highlight the importance of using structurally adaptive estimators and automation of method selection in nonparametric statistics. The article concludes with recommendations for method selection based on sample characteristics and outlines future research directions, including extensions to multivariate settings and real-time decision-making systems. Full article