Search Results (3,251)

Remote sensing visual question answering (RS-VQA) is essential to intelligent Earth observation, as it supports interactive querying of high-resolution aerial images. Many existing methods struggle with fine-detail geospatial reasoning with remote sensing (RS) scenes due to RS scenes having intrinsic multi-scale object variance and pronounced spatial heterogeneity. The models tend to rely more on the linguistic prior than reasoning based on visual evidence. In this paper, we present PMA-VQA, a progressive multi-scale feature fusion with spatially adaptive attention, to embed the RS-VQA task in spatially based hierarchical feature integration. For hierarchical, multi-level, language-informed integration, we propose a spatial attention aggregation module (SAAM) and a progressive feature fusion and classification module (PFCM). The SAAM employs spatially adaptive gating to align cross-modal features with semantic context, while the PFCM integrates multi-scale representations across high-level semantic abstractions and low-level space. The experimental results on RS-VQA LR and HR benchmarks validate that PMA-VQA outperformed all competing methods in terms of accuracy and robustness. Evaluation of HRVQA further confirmed the effectiveness of the SAAM and PFCM across diverse RS scenes. Full article

The dielectric properties of cement-based composites (CBC) are highly sensitive to environmental humidity, which seriously restricts the quantitative interpretation accuracy of ground-penetrating radar (GPR) in the non-destructive testing of cement concrete pavement. In view of the lack of targeted prediction models due to the unclear mechanism of humidity influence in existing research, the core innovations of this study are: (1) the synergistic mechanism of water vapor dipole polarization and adsorbed water multi-layer polarization is clarified, revealing the intrinsic reason for the accelerated growth of permittivity in the high humidity range; (2) the constructed four-component dielectric model of “cement mortar–aggregate–water vapor–adsorbed water” achieves high-precision prediction within the range of 50~100% RH (R² > 0.94, relative error < 5%), and shows good predictive ability within the test scope of this study; (3) a GPR humidity correction protocol based on the model is proposed, which can effectively improve the accuracy of nondestructive testing of cement concrete structures. In this study, CBC samples with water–cement ratios of 0.4~0.6 were prepared using P.O 32.5/P.O 42.5 cement and limestone aggregate. Under the conditions of 20 ± 0.5 °C, relative humidity (RH) of 50~100%, and 2 GHz (common GPR frequency), the permittivity was measured using an Agilent P5001A network analyzer to verify the model. The results show that the permittivity increases monotonically with humidity, and the growth rate in the high humidity range (70~100%) is 2.2 times that of the low humidity range (50~70%); The higher the water–cement ratio, the shorter the age, and the lower the cement strength grade, the stronger the humidity sensitivity of CBC dielectric properties. This model provides a reliable humidity correction tool for GPR detection, and significantly improves the accuracy of nondestructive evaluation of cement concrete structures. Full article

Sleep plays a fundamental role in human health and cognitive functioning, motivating the development of reliable and scalable methodologies for sleep stage classification (SSC). Recent advances in non-invasive and economically sustainable sensing technologies enable continuous sleep monitoring beyond laboratory settings. However, SSC remains a challenging data analytics task due to the intrinsic class imbalance among sleep stages. This study investigates the effectiveness of different imbalanced data management strategies within a machine learning framework for non-invasive SSC. The proposed approach relies exclusively on heart rate and motion signals, which can be acquired through wearable devices or contactless under-mattress sensors, making it suitable for longitudinal monitoring scenarios. Using the PhysioNet DREAMT dataset, 32 experimental scenarios are defined by combining data-level techniques (ADASYN oversampling with different balancing weights), algorithm-level strategies (cost-sensitive learning), and hybrid solutions. Four model families are evaluated—Decision Tree, k-Nearest Neighbors, Ensemble Classifiers, and Artificial Neural Networks—across classification tasks involving 2, 3, 4, and 5 sleep stages. The experimental results show that ensemble-based models provide robust and consistent performance under severe class imbalance, achieving macro accuracies of 82% for sleep–wake detection, 73% for 3-stage classification, 72% for 4-stage classification, and 64% for 5-stage classification. These findings confirm the relevance of imbalance-aware analytics and demonstrate the feasibility of accurate, minimally invasive SSC within big data and cognitive computing paradigms. Full article

The detection of sulfur hexafluoride (SF₆) decomposition products is critical for diagnosing insulation faults in gas-insulated switchgear (GIS). In this study, a vanadium-doping strategy was incorporated into the graphene/MoS₂ (GM) heterojunction to design a vanadium-doped graphene/MoS₂ (GMV) heterojunction material. Leveraging first-principles density functional theory (DFT), the adsorption behaviors of five characteristic SF₆ and its decomposition gases (H₂S, SO₂, SOF₂, SO₂F₂) on intrinsic GM and GMV were systematically investigated to evaluate their potential for gas sensing applications. Computational results reveal that intrinsic GM exhibits only weak physical adsorption toward all target molecules, with low adsorption energies and negligible charge transfer, which fails to meet practical application requirements. In contrast, GMV demonstrates significantly enhanced adsorption energies for H₂S, SO₂, and SOF₂ at vanadium sites (with a maximum value of −0.388 eV for SO₂) and shorter adsorption distances, while SO₂F₂ and SF₆ preferentially adsorb near electron-deficient carbon regions. Intrinsic GMV displays semimetallic properties, with a Fermi level at 0.126 eV and a band gap of 0.0017 eV. Upon adsorption of H₂S, SOF₂, SO₂F₂, or SF₆, the Fermi level undergoes a moderate shift (ranging from −1.083 eV to +0.349 eV), with minimal changes in the band gap. Conversely, SO₂ adsorption induces a substantial downward shift of the Fermi level to −1.732 eV, accompanied by the emergence of a sharp partial density of states (PDOS) peak near the Fermi level (0–1.5 eV), indicating strong orbital coupling and significant charge transfer. Furthermore, recovery times calculated using classical formulas show that at room temperature and a frequency of 1 × 10⁶ Hz, the recovery time of GMV for SO₂ is 2.43 s, outperforming the other four gases and satisfying practical gas sensing requirements. Through comprehensive analysis of adsorption distances, electronic structure changes, and recovery times, GMV exhibits higher selectivity toward SO₂. Thus, GMV can serve as a sensing material for detecting GIS insulation faults associated with elevated SO₂ concentrations, offering a viable strategy for advancing online monitoring technologies in power systems. Full article

With the increasing demand for privacy-preserving and real-time personalized services in large-scale video platforms, designing robust federated recommendation frameworks over practical communication networks has become increasingly important. To this end, this paper proposes a bias-corrected federated learning framework tailored for video recommendation over stochastic communication links. At the local training stage, a bias-corrected mechanism is introduced to explicitly account for video duration and user activity, mitigating feature-level bias and enabling the learned representations to more accurately reflect users’ intrinsic preferences. To meet the timeliness requirements of real-time federated learning, the successful upload probability of local model transmission is analytically characterized under time-varying channel conditions. Building upon this probabilistic model, a statistically corrected global aggregation strategy is designed to preserve the unbiasedness of the global update with respect to the ideal fully reliable FedAvg scheme, even when a subset of local nodes fails to upload their models within the specified delay constraint. Comprehensive experimental evaluations validate that the proposed framework significantly improves recommendation accuracy and maintains robustness against communication unreliability in practical distributed environments. Full article

The rapid proliferation of text-to-image generative systems has transformed visual content production, yet the structural characteristics embedded in their compositional outputs remain insufficiently understood. Rather than approaching human–AI differentiation as a purely classification problem, this study investigates whether a controlled set of AI-generated and human-designed posters exhibits measurable structural divergence under thematically matched conditions. A dataset of jazz festival posters was analyzed using interpretable geometric and information-theoretic descriptors, including spatial density (padding ratio), edge density, chromatic dispersion, and entropy-based measures. Instead of relying on deep neural detection architectures, we employed a transparent machine-learning framework to examine intrinsic structural separability within feature space. Results demonstrated highly stable group separation (ROC-AUC = 0.99; 95% CI: 0.978–0.999) under cross-validated evaluation. Distributional analysis further revealed a pronounced divergence in spatial density allocation (Kolmogorov–Smirnov statistic = 0.76, p < 10⁻²⁸), accompanied by a very large effect size (Cohen’s d = 1.365). While padding ratio emerged as the dominant discriminative factor, additional entropy- and chromatic-based descriptors contributed to group separation even when spatial density was excluded (AUC = 0.903). These findings indicate that AI-generated and human-designed posters can diverge in negative space allocation and chromatic organization under controlled thematic and platform-specific conditions. The study contributes to the explainable analysis of generative visual systems by reframing human–AI differentiation as a structural divergence problem grounded in interpretable image statistics rather than as a model-specific artifact detection task. Full article

This study investigated mechanical properties of composite materials consisting of an isotactic polypropylene (iPP) matrix reinforced with whisker-like fillers: carbon nanofibers (CBNF) and wollastonite (WN). We strove to develop mechanical models specifically for predicting yield stress and fracture toughness. Experimentally obtained results validated findings obtained using the proposed models. Regarding the elastic modulus, data suggest that conventional rules of mixture, typically used for glass fiber-reinforced polymers, remain applicable, indicating that filler addition enhances stiffness in a predictable manner. However, yield stress and fracture toughness exhibited distinct behaviors. Results revealed that these properties are governed predominantly by shear yielding of the iPP matrix rather than reinforcement effect of the fillers. Despite the presence of whiskers, the overall yield and fracture mechanisms depend heavily on the matrix’s plastic deformation and energy dissipation. The constructed models consistently explain these findings, supporting quantitative evaluation of the matrix’s contribution. These results emphasize that developing high-performance iPP composites requires knowledge of the intrinsic ductile properties of the matrix alongside filler selection and dispersion. Full article

Modeling and forecasting nonstationary and nonlinear economic time series remain fundamentally challenging due to structural breaks, volatility clustering, and noise contamination that distort the intrinsic stochastic structure. To address these limitations, this study proposes a novel three-stage hybrid statistical framework that systematically integrates multi-level signal decomposition with structured parametric modeling to enhance predictive accuracy. The proposed hybrid architectures—EMD–EEMD–ARIMA, EMD–EEMD–GMDH, and EMD–EEMD–ETS—employ a hierarchical decomposition–reconstruction strategy before forecasting. In the first stage, Empirical Mode Decomposition (EMD) decomposes the observed series into intrinsic mode functions (IMFs) and a residual component. In the second stage, Ensemble Empirical Mode Decomposition (EEMD) is applied to further refine the extracted components, mitigating mode mixing and improving signal separability. In the final stage, each reconstructed component is modeled using ARIMA, Exponential Smoothing State Space (ETS), and Group Method of Data Handling (GMDH) frameworks, and the individual forecasts are aggregated to obtain the final prediction. Empirical evaluation based on a recursive one-step-ahead forecasting scheme demonstrates consistent numerical improvements across all standard accuracy measures. In particular, the proposed EMD–EEMD–ARIMA model achieves the lowest forecasting error, reducing the root-mean-square error (RMSE) by approximately 6–7% relative to the best-performing single-stage model and by about 3–4% relative to the two-stage EMD-based hybrids. Similar improvements are observed in mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), indicating enhanced stability and robustness of the three-stage architecture. The results provide strong numerical evidence that multi-level decomposition combined with structured statistical modeling yields superior predictive performance for complex nonlinear and nonstationary time series. The proposed framework offers a mathematically coherent, computationally tractable, and systematically structured hybrid modeling strategy that effectively integrates noise-assisted decomposition with parametric and data-driven forecasting techniques. Full article

The SH-SY5Y cell line is widely used as an in vitro model for pharmacological and molecular investigations of Parkinson’s disease (PD). The use of SH-SY5Y cells in PD research critically relies on their ability to differentiate into a mature, post-mitotic, dopaminergic (DAergic) neuronal phenotype. However, SH-SY5Y cells are inherently heterogeneous since they are firstly catecholaminergic cells and may express diverse phenotypic markers besides the DAergic ones. These properties seem to be determined by the differentiation protocol that is employed, thus meaning it is crucial to obtain proper cell types. This systematic review aims to discuss the main differentiation protocols used in PD research over the last 30 years. They include inducers such as retinoic acid (RA), the phorbol ester TPA, and the BDNF. Among the 514 studies that were screened, 249 employed these inducers. Then, we quantitatively report the ability of these protocols to differentiate SH-SY5Y cells in mature DAergic neurons, evaluating morphology, differentiation markers, and DAergic markers among the studies that specifically compared differentiated to undifferentiated SH-SY5Y cells (61 studies over 249). As our research shows, despite the highest usage of the RA differentiation protocol, the combination of RA with the BDNF inducer seems to increase the expression and the acquisition of a DAergic phenotype. Nevertheless, during this analysis, some limitations emerged, highlighting the intrinsic phenotypic heterogeneity of these cells, thereby limiting their suitability according to the specific biological question under investigation. A deep investigation into the literature about the molecular phenotypic features of differentiated SH-SY5Y cells may eventually help us to understand the advantages and disadvantages of each protocol that was employed, and adequately set experiments around the PD research. Full article

This work evaluated the influence of oil type (sunflower vs. fish oil) and hydroxypropyl methylcellulose (HPMC) concentration on the properties of oleogels obtained by the emulsion-templated method. Oil-in-water emulsions were prepared and air-dried to produce oleogels containing 2.9–5.8% (w/w) HPMC. All oleogels exhibited solid-like behaviour, with viscoelastic moduli increasing with polymer concentration, and showed a high thermal stability. At a comparable HPMC content, fish oil oleogels developed stiffer networks than those obtained with sunflower oil. Texture analysis indicated a linear increase in hardness with HPMC content across both oils, while cohesiveness and adhesiveness were more influenced by oil nature. Oil-binding capacity (OBC) increased markedly with polymer content, exceeding 90% in most systems. However, fish oil oleogels consistently showed lower retention. Colour parameters were only slightly affected by HPMC concentration and were mainly determined by the intrinsic colour of each oil. Overall, both oil type and polymer concentration were shown to be critical factors determining the structural, mechanical, and functional characteristics of HPMC-based oleogels, providing useful information for the development of structured lipid systems as potential substitutes for conventional solid fats. Full article

This article develops a boundary-adaptive nonparametric methodology for estimating the geometric conditional quantiles of a multivariate response when the conditioning covariate is supported on the simplex—an important case, as it is the natural domain of compositional data. The statistical difficulty addressed here is twofold. First, geometric conditional quantiles for multivariate responses must be defined and estimated through a genuinely directional and convex framework rather than through any scalar ordering. Second, when the covariate is compositional or otherwise simplex-constrained, conventional symmetric kernel procedures suffer from intrinsic support mismatch and severe boundary distortion, thereby compromising both estimation accuracy and inferential validity near faces and edges of the simplex. The method proposed in this paper is designed precisely to overcome this combined obstacle. Our main innovation consists in embedding the spatial quantile formalism of Chaudhuri within a Dirichlet–Kernel smoothing scheme whose shape parameters depend deterministically on the evaluation point. This produces a convex M-estimator that respects the simplex geometry exactly, automatically adapts its local shape to the position of the target point, and removes the need for artificial boundary corrections. To the best of our knowledge, this is the first contribution to provide a complete asymptotic treatment of geometric conditional quantile estimation under simplex-supported covariates with location-adaptive asymmetric kernels. We establish a Bahadur-type linear representation with an explicit negligible remainder, from which we derive refined asymptotic bias and variance expansions. The variance analysis reveals a distinctive geometric phenomenon: each coordinate direction approaching the simplex boundary induces an additional $b^{-1/2}$ inflation factor, so that the variance at a face of codimension $\left|\mathcal{J}\right|$ scales as $n^{-1}{b}^{-(s+|\mathcal{J}\left|\right)/2}$. We further obtain the asymptotic mean squared error, an explicit optimal bandwidth rate, asymptotic normality under the nonstandard normalization $n^{1/2}{b}^{-s/4}$, and consistent plug-in covariance estimators yielding valid confidence ellipsoids. Numerical experiments and a real-data illustration based on the GEMAS data confirm the practical merit of the approach, especially in boundary regions where classical methods are known to deteriorate. Full article

Brewer’s spent grain (BSG) is one of the major by-products of the brewing industry and an abundant lignocellulosic stream with potential for energy recovery and broader biorefinery use. This review evaluates the main BSG-to-energy pathways, including anaerobic digestion (AD), combustion/co-combustion, pyrolysis, gasification, and hydrothermal processes (HTC/HTL), with emphasis on technical performance, environmental aspects, implementation constraints, and integration into brewery systems. Particular attention is given to the effect of BSG heterogeneity, high moisture content, protein and ash composition, and storage instability on process selection and operability. In addition to summarizing pathway-specific evidence, the manuscript proposes a harmonized comparative framework and an integrated technical–economic–environmental interpretation of BSG valorization options. The analysis shows that wet-feed-compatible pathways, especially AD and hydrothermal processing, are generally better aligned with the intrinsic properties of fresh BSG, whereas thermochemical routes usually require more intensive feedstock conditioning and tighter control of ash-related and gas cleaning risks. The review also highlights that long-term operational reliability, scale-up constraints, and utility integration are as important as nominal conversion efficiency when assessing practical deployment. Current evidence suggests that the most realistic implementation strategies are context-dependent and should be selected according to brewery scale, energy demand profile, available heat integration, and acceptable operational risk. Future research should prioritize harmonized reporting, long-term industrial validation, and the development of robust hybrid systems and brewery-integrated biorefinery configurations. Full article

Accurate forecasting of pork prices is important yet challenging because pork price series are highly volatile and non-stationary. Existing hybrid forecasting models often rely on fixed-weight integration, which may limit their ability to adapt to multi-scale temporal variation and complex temporal dependencies. To address these issues, this study proposes VMD–EMSA–HCTM–Informer, a hybrid forecasting framework that combines signal decomposition with an enhanced encoder–decoder architecture. Variational Mode Decomposition (VMD) is first used to reduce signal non-stationarity by extracting intrinsic mode functions. Within the Informer backbone, an Enhanced Multi-Scale Attention (EMSA) encoder is introduced to capture local fluctuations at different temporal scales, while a Hybrid Convolutional–Temporal Module (HCTM) decoder is used to strengthen temporal feature extraction and channel interaction modeling. Empirical evaluation was conducted on daily pork price data from the China Pig Industry Network and a large-scale intensive breeding enterprise in southern China over the period 2013–2025. Under the current experimental setting, the proposed framework achieved the lowest average errors among the compared baselines across five independent runs, with an average MAE of 0.4875 and an average MAPE of 3.0540%. These results suggest that the proposed framework provides a useful and relatively stable univariate forecasting approach for volatile pork prices. However, the findings should be interpreted within the scope of the present dataset and experimental design, and future work will extend the framework to multivariate forecasting with exogenous drivers and uncertainty quantification. Full article

Factor XII (FXII) is a central mediator at the intersection of coagulation, fibrinolysis, inflammation, and immunity. It is activated upon contact with negatively charged surfaces, triggering the intrinsic coagulation pathway and driving thrombus formation and stabilization. Beyond clotting, FXII contributes to activation of the kallikrein–kinin system, generation of bradykinin, and modulation of inflammatory and immune responses. Congenital FXII deficiency does not increase bleeding risk, highlighting its unique role and making FXII inhibition an attractive strategy for anticoagulation and immune modulation with a potentially superior safety profile. Preclinical studies provide compelling evidence for this concept. In models of ischemic stroke and traumatic brain injury, FXII blockade significantly reduced infarct volume, improved neurological outcomes, and attenuated neuroinflammation without increasing hemorrhage. Similarly, in extracorporeal circulation and vascular stent implantation, FXII inhibition prevented thrombus formation and reduced fibrin deposition, achieving effects comparable to heparin but with markedly lower bleeding risk. Several classes of FXII inhibitors are currently in development, including antisense oligonucleotides, peptides, recombinant proteins, and monoclonal antibodies. Among them, Ixodes ricinus contact phase inhibitor (Ir-CPI) and recombinant human albumin-fused Infestin-4 (rHA-Infestin-4) have demonstrated strong antithrombotic efficacy in animal models. Most notably, garadacimab, a monoclonal anti-FXIIa antibody, has completed phase 3 trials and received regulatory approval for hereditary angioedema (HAE) prophylaxis, where it markedly reduces attack frequency with a favorable safety profile. This review summarizes current knowledge on FXII biology and evaluates its translational potential as a novel target for anticoagulant and anti-inflammatory therapies. Full article

Honey authenticity control remains analytically challenging due to the complexity of its matrix and the increasing sophistication of adulteration practices. While chromatographic, spectrometric, and isotopic methods provide high confirmatory accuracy, their routine application is constrained by cost, time, and infrastructure requirements. In this context, fluorescence spectroscopy has emerged as a rapid, non-destructive, and cost-efficient screening approach capable of capturing subtle matrix-level compositional variations. This review critically evaluates the application of steady-state and excitation–emission matrix (EEM) fluorescence in honey quality and authenticity assessment. Fluorescence is positioned within tiered analytical frameworks as a first-line or intermediate screening tool preceding confirmatory chromatographic or NMR-based analyses. Emphasis is placed on intrinsic fluorophore domains, excitation–emission measurement strategies, and chemometric interpretation, including multiway analysis and supervised classification models. Recent developments in portable LED-based systems, laser-induced fluorescence, nanoparticle-based probes, and data-fusion strategies are discussed alongside key limitations related to matrix effects, spectral overlap, reproducibility, and model transferability. The review provides a structured framework for the strategic integration of fluorescence spectroscopy into contemporary honey authentication workflows. Full article