Search Results (338)

This study presents a methanol-assisted pathway that converts hydroxyl-containing amidine into a polymeric ionic liquid (PIL) through direct CO₂ fixation, followed by its transformation into a cross-linked ionic polymer (CL-IP). Methanol plays a crucial role in this process, acting as both a structural and electronic mediator. Its strong hydrogen-bonding interactions with amidine activate the molecule toward CO₂ capture and promote the formation of ionic intermediates. Spectroscopic analyses (FTIR, ¹H and ¹³C NMR) revealed the emergence of amidinium and alkyl-carbonate groups, while viscosity and mass measurements indicated progressive polymerization during CO₂ absorption. Density functional theory calculations confirmed the stabilizing effect of methanol and the reduced HOMO–LUMO gap, which facilitates PIL formation. The subsequent condensation of the PIL with glutaraldehyde produced a dense three-dimensional cross-linked network (CL-IP), as verified by FTIR, XPS, SEM, and TGA analyses. These results highlight a straightforward and sustainable strategy for constructing hydrogen-bond-mediated ionic polymers capable of tunable CO₂ capture and potential application in environmentally compatible materials. Full article

Research on deep neural network (DNN)-based multi-dimensional data visualization has thoroughly explored cross-modal hash retrieval (CMHR) systems, yet their vulnerability to malicious adversarial examples remains evident. Recent work improves the robustness of CMHR networks by augmenting training datasets with adversarial examples. Prior approaches typically formulate the generation of cross-modal adversarial examples as an optimization problem solved through iterative methods. Although effective, such techniques often suffer from slow generation speed, limiting research efficiency. To address this, we propose a generative-based method that enables rapid synthesis of adversarial examples via a carefully designed adversarial generator network. Specifically, we introduce Cross-Gen, a parallel cross-modal framework that constructs semantic triplet data by interacting with the target model through query-based feedback. The generator is optimized using a tailored objective comprising adversarial loss, reconstruction loss, and quantization loss. The experimental results show that Cross-Gen generates adversarial examples significantly faster than iterative methods while achieving competitive attack performance. Full article

Accurate multi-energy load forecasting is essential for the low-carbon, efficient, and resilient operation of park-level Integrated Energy Systems (PIESs), where cooling, heating, and electricity networks interact closely and increasingly incorporate renewable energy resources. However, forecasting in such systems remains challenging due to complex cross-energy coupling, high-dimensional feature interactions, and pronounced nonlinearities under diverse meteorological and operational conditions. To address these challenges, this study develops a novel three-stage hybrid forecasting framework that integrates Recursive Feature Elimination with Cross-Validation (RFECV), a Multi-Task Long Short-Term Memory network (MTL-LSTM), and Random Forest (RF). In the first stage, RFECV performs adaptive and interpretable feature selection, ensuring robust model inputs and capturing meteorological drivers relevant to renewable energy dynamics. The second stage employs MTL-LSTM to jointly learn shared temporal dependencies and intrinsic coupling relationships among multiple energy loads. The final RF-based residual correction enhances local accuracy by capturing nonlinear residual patterns overlooked by deep learning. A real-world case study from an East China PIES verifies the superior predictive performance of the proposed framework, achieving mean absolute percentage errors of 4.65%, 2.79%, and 3.01% for cooling, heating, and electricity loads, respectively—substantially outperforming benchmark models. These results demonstrate that the proposed method offers a reliable, interpretable, and data-driven solution to support refined scheduling, renewable energy integration, and sustainable operational planning in modern multi-energy systems. Full article

Intrusive phase-detection probes remain a standard tool for local characterization of gas–liquid bubbly flows, but their accuracy is strongly affected by probe geometry and bubble–probe interaction kinematics. This work presents a Monte Carlo-based framework to evaluate four-sensor intrusive probes in bubbly flow, relaxing the classical assumptions of spherical bubbles and purely axial trajectories. Bubbles are represented as spheres or ellipsoids, a broad range of non-dimensional probe geometries are explored, and local quantities such as interfacial area concentration, bubble and flux velocities, and chord lengths are recovered from synthetic four-sensor signals. The purpose of the framework is threefold: (i) it treats four-sensor probes in a unified way for interfacial area, velocity, and chord length estimation; (ii) it includes ellipsoidal bubbles and statistically distributed incidence angles; and (iii) it yields compact correction laws and design maps expressed in terms of the spacing-to-diameter ratio $a_p/D$, the dimensionless probe radius $r_p/D$, and the missing ratio $m_r$ (defined as the fraction of bubbles that cross the probe footprint without being detected), which can be applied to different intrusive four-sensor probes. The numerical results show that, within a recommended geometric range $0.5\le a_p/D\le 2$ and $r_p/D\le 0.25$ and for missing ratios $m_r\le 0.7$, the axial velocity $V_z$ estimates the bubble centroid velocity and its projection with typical errors within $\pm 10\%$, while a chord length correction $C{L}_{corr}\left(m_r\right)$ recovers the underlying chord length distribution with a residual bias of only a few percent. The proposed interfacial area correction, written solely in terms of $m_r$, remains accurate in polydisperse bubbly flows. Outside the recommended $(a_p/D,r_p/D)$ range, large probe radius or extreme tip spacing lead to velocity and chord length errors that can exceed 20–30%. Overall, the framework provides quantitative guidelines for designing and using four-sensor intrusive probes in bubbly flows and for interpreting their measurements through geometry-aware correction factors. Full article

Background: Shoulder exercises using elastic resistance integrated within the kinetic chain appear to modify scapular control strategies; however, a deeper understanding of these mechanisms is still needed. Objectives: We aim to compare three-dimensional scapular kinematics during two exercises performed on different bases of support, under both non-resisted and resisted conditions in asymptomatic adults. Methods: This cross-sectional study analyzed three-dimensional shoulder kinematics in 36 healthy adult male participants during the overhead squat and kneeling position exercises. Movement patterns were evaluated by phase using statistical parametric mapping. Results: Scapular internal/external rotation demonstrated a main effect for exercise type (p = 0.04), a main effect for resistance conditions (p < 0.00), and a significant exercise–resistance interaction (p = 0.04) during arm elevation. During the lowering phase, a main effect was observed for exercise types (p = 0.04) and exercise conditions (p < 0.00). Scapular upward rotation showed a main effect for exercise type (p = 0.02) and resistance conditions (p = 0.04) during arm elevation. During the lowering phase, a significant main effect was observed for exercise type (p = 0.01) and exercise conditions (p < 0.00). Scapular posterior tilt presented a main effect for exercise type (p < 0.00), a main effect for exercise condition (p = 0.01), and an exercise–resistance interaction (p = 0.04) during arm elevation. During the lowering phase, a main effect for exercise type (p < 0.00), a main effect for exercise condition (p = 0.02), and an exercise–resistance interaction (p = 0.00). Conclusions: The resistance and exercises demonstrated different kinematic strategies that helped maintain scapular stability during movement. Full article

Cross-beam glued-laminated timber (glulam) floor systems offer material efficiency but pose a complex design challenge due to three-dimensional (3D) load interactions, and systematic optimization guidelines are lacking. This study implements a parametric optimization framework using a three-factor Design of Experiments (DOE) approach (beam spacing ratio, height-to-span ratio, width-to-height ratio). A total of 27 full-factorial finite element models (FEMs) were simulated in Dlubal RFEM. A second-order response surface methodology (RSM) model was developed to predict the load utilization factor (Y) in accordance with Eurocode 5. The predictive model demonstrated high statistical accuracy (R² > 0.98). A multi-criteria optimization using the Pareto frontier identified a balanced solution (x₁ = 0.250, x₂ = 0.042, x₃ = 0.5) that achieved 97.4% load utilization (Y = 0.974). This optimal configuration reduces the required timber volume by approximately 10% compared with other efficient designs and by over 60% compared with inefficient (Y ≈ 0.5) but safe designs within the experimental space. The resulting regression model provides a validated engineering tool for designing materially efficient glulam floor systems, allowing designers to balance structural safety with material economy. Full article

Artificial emotional intelligence is a sub-domain of human–computer interaction research that aims to develop deep learning models capable of detecting and interpreting human emotional states through various modalities. A major challenge in this domain is identifying meaningful correlations between heterogeneous modalities—for example, between audio and visual data—due to their distinct temporal and spatial properties. Traditional fusion techniques used in multimodal learning to combine data from different sources often fail to adequately capture meaningful and less computational cross-modal interactions, and struggle to adapt to varying modality reliability. Following a review of the relevant literature, this study adopts an experimental research method to develop and evaluate a mathematical cross-modal fusion model, thereby addressing a gap in the extant research literature. The framework uses the Tucker tensor decomposition to analyse the multi-dimensional array of data into a set of matrices to support the integration of temporal features from audio and spatiotemporal features from visual modalities. A cross-attention mechanism is incorporated to enhance cross-modal interaction, enabling each modality to attend to the relevant information from the other. The efficacy of the model is rigorously evaluated on three publicly available datasets and the results conclusively demonstrate that the proposed fusion technique outperforms conventional fusion methods and several more recent approaches. The findings break new ground in this field of study and will be of interest to researchers and developers in artificial emotional intelligence. Full article

This study develops a systematic kinematic upper-bound framework to evaluate the ultimate bearing capacity and failure mechanisms of prefabricated cast-in-place slab–wall joints in overlapped metro stations. Recognizing the complex shear–compression interaction in these critical structural nodes, a novel three-dimensional short-block shear failure model is established based on the principle of energy balance. The analysis employs a modified Mohr–Coulomb strength criterion incorporating a finite tensile strength cut-off, enabling more accurate representation of cracking and tensile resistance effects. Analytical solutions are derived to predict the ultimate capacity and critical failure angle, followed by a comprehensive parametric analysis. The results reveal that cross-sectional dimensions dominate the bearing capacity, while the internal friction angle and tensile-to-compressive strength ratio significantly influence both the magnitude and mode of failure. A narrower load distribution width enhances capacity and reduces the optimal failure angle. Overall, the proposed 3D model provides a rigorous and efficient theoretical tool for the design optimization and safety assessment of prefabricated underground structures. Full article

Amidrapid advances in intelligent medicine, decoding brain activity from electroencephalogram (EEG) signals has emerged as a critical technical frontier for brain–computer interfaces and medical AI systems. Given the inherent spatial resolution limitations of an EEG, researchers frequently integrate functional magnetic resonance imaging (fMRI) to enhance neural activity representation. However, fMRI acquisition is inherently complex. Consequently, efforts increasingly focus on cross-modal transformation methods that map EEG signals to fMRI data, thereby extending EEG applications in neural mechanism studies. The central challenge remains generating high-fidelity fMRI images from EEG signals. To address this, we propose a diffusion model-based framework for cross-modal EEG-to-fMRI generation. To address pronounced noise contamination in electroencephalographic (EEG) signals acquired via simultaneous recording systems and temporal misalignments between EEGs and functional magnetic resonance imaging (fMRI), we first apply Fourier transforms to EEG signals and perform dimensionality expansion. This constructs a spatiotemporally aligned EEG–fMRI paired dataset. Building on this foundation, we design an EEG encoder integrating a multi-layer recursive spectral attention mechanism with a residual architecture.In response to the limited dynamic mapping capabilities and suboptimal image quality prevalent in existing cross-modal generation research, we propose a diffusion-model-driven EEG-to-fMRI generation algorithm. This framework unifies the EEG feature encoder and a cross-modal interaction module within an end-to-end denoising U-Net architecture. By leveraging the diffusion process, EEG-derived features serve as conditional priors to guide fMRI reconstruction, enabling high-fidelity cross-modal image generation. Empirical evaluations on the resting-state NODDI dataset and the task-based XP-2 dataset demonstrate that our EEG encoder significantly enhances cross-modal representational congruence, providing robust semantic features for fMRI synthesis. Furthermore, the proposed cross-modal generative model achieves marked improvements in structural similarity, the root mean square error, and the peak signal-to-noise ratio in generated fMRI images, effectively resolving the nonlinear mapping challenge inherent in EEG–fMRI data. Full article

The simultaneous detection of multiple cancer biomarkers using microfluidic multiplexed immunosensors is gaining significant interest in the field of Point-of-Care diagnostics. This study highlights integrating surface-enhanced infrared Fourier transform (SE-FTIR) with a plasmonic-active nanostructure thin film (PANTF) on a printed circuit board (PCB), housed within a microfluidic device for rapid, non-destructive detection of breast cancer (BC). Detection uses monoclonal antibody (mAb)-functionalized gold nanourchins (GNUs) on dual sensing regions. A total of 12 serum samples (24 data points) were tested for HER-II and CA 15-3. The system demonstrated a SE-FTIR enhancement factor (EF) of ~0.18 × 10⁵ using Rhodamine 6G (R6G). Calibration with HER-II (1–100 ng/mL) and CA 15-3 (10–100 U/mL) showed linear responses (R² = 0.8 and 0.76, respectively). Measurements of unknowns were performed at 1 µL/min over 68 min, with 43 min for biomarker interaction. SE-FTIR spectra were recorded at active zones and analyzed using SpectraView (SV), a custom Python 3.12-based tool. Data preprocessing included filtering (SciPy’s filtfilt) and baseline correction using the Improved Asymmetric Least Squares (IASLS) algorithm (pybaselines.Whittaker). Fourier cross-correlation (FCC) showed stronger signal consistency for HER-II. Partial Least Squares (PLS) regression, a dimensionality reduction technique, enabled clear discrimination between the samples and types, with classification accuracy reaching 1.0. Cancer staging based on these biomarkers yielded an overall accuracy of 0.54, indicating that classification regardless of biomarker type. Further studies involving larger and more diverse sample sets are critical before any definitive conclusions can be drawn. Full article

Gel materials are widely used in underground mining for air leakage sealing and coal spontaneous combustion prevention. In this study, a novel self-healing carboxymethyl cellulose-modified amphiphilic polymer hydrogel with fire prevention and extinguishing capabilities is synthesized through ionic crosslinking between CMC-graft-poly(AM-co-NaA-co-BAM) and aluminum citrate (AlCit). The copolymer is constructed by grafting sodium carboxymethyl cellulose (CMC) onto an amphiphilic polymer backbone composed of acrylamide (AM), sodium acrylate (NaA), and N-benzylacrylamide (BAM), forming a dual-network structure via hydrophobic association and hydrogen bonding. The carboxymethyl cellulose-modified amphiphilic polymer demonstrates optimal viscosity-enhancing performance at a CMC content of 7.5 wt%. CMC-graft-poly(AM-co-NaA-co-BAM) demonstrated superior temperature, shear, and salt resistant performance compared with poly(AM-co-NaA-co-BAM), poly(AM-co-NaA), and CMC polymers, as well as enhanced viscoelasticity and self-healing capability. When crosslinked with AlCit, CMC-graft-poly(AM-co-NaA-co-BAM)-AlCit gel demonstrated superior viscoelastic properties and self-healing capability, as well as thermal stability, which gave the superior fire prevention and extinguishing performance for charcoal in fire extinction tests. CMC-graft-poly(AM-co-NaA-co-BAM) has abundant cross-linking sites, which lead to accelerated gelation and improved mechanical strength, while the hydrophobic microdomains acted as physical cross-linking points that interconnected polymer chains into a three-dimensional network. The hydrophobic interactions within the hydrogel are dynamically reversible. This intrinsic property allows physical cross-links to spontaneously reassociate when fracture surfaces make contact. Consequently, the material exhibits autonomous self-healing. Full article

Hyperspectral image (HSI) classification requires models that can simultaneously capture spatial structures and spectral continuity. Although state space models (SSMs), particularly Mamba, have shown strong capability in long-sequence modeling, their application to HSI remains limited due to insufficient spectral relation modeling and the constraints of unidirectional processing. To address these challenges, we propose BiMambaHSI, a novel bidirectional spectral—spatial framework. First, we proposed a joint spectral—spatial gated mamba (JGM) encoder that applies forward–backward state modeling with input-dependent gating, explicitly capturing bidirectional spectral—spatial dependencies. This bidirectional mechanism explicitly captures long-range spectral—spatial dependencies, overcoming the limitations of conventional unidirectional Mamba. Second, we introduced the spatial—spectral mamba block (SSMB), which employs parallel bidirectional branches to extract spatial and spectral features separately and integrates them through a lightweight adaptive fusion mechanism. This design enhanced spectral continuity, spatial discrimination, and cross-dimensional interactions while preserving the linear complexity of pure SSMs. Extensive experiments on five public benchmark datasets (Pavia University, Houston, Indian Pines, WHU-Hi-HanChuan, and WHU-Hi-LongKou) demonstrate that BiMambaHSI consistently achieves state-of-the-art performance, improving classification accuracy and robustness compared with existing CNN- and Transformer-based methods. Full article

Focusing on mediatized urban images, this examination of Jiangnan water towns analyzes 1000 user-generated posts on Xiaohongshu through word frequency statistics, content categorization, and textual interpretation to demonstrate how “Seeding Strategy” transforms the symbolic representation and cultural identity of ancient towns. The results reveal that mediatized conceptions of water towns operate within a four-dimensional symbolic framework—natural, cultural, interactive, and Sentiment symbols—shaped by user co-creation and local cultural assets. Through photo-taking and check-ins, users convert historic towns from static geographical locations into dynamic media environments with visual and emotional resonance. Platform algorithms amplify engaging content, reinforcing spatial imaginaries. The concept of “symbolic effects on media platforms” elucidates how local culture is reconstructed and disseminated within digital frameworks, offering theoretical insights and practical recommendations for cultural tourism branding and cross-platform place research in the digital age. Full article

Emotions are complex phenomena arising from cooperative interactions among multiple brain regions. Electroencephalography (EEG) provides a non-invasive means to observe such neural activity; however, as it captures only electrode-level signals from the scalp, accurately classifying dimensional emotions requires considering both local electrode activity and the global spatial distribution across the scalp. Motivated by this, we propose a brain-inspired EEG electrode-cluster-based framework for dimensional emotion classification. The model organizes EEG electrodes into nine clusters based on spatial and functional proximity, applying an EEG Deformer to each cluster to learn frequency characteristics, temporal dynamics, and local signal patterns. The features extracted from each cluster are then integrated using a bidirectional cross-attention (BCA) mechanism and a temporal convolutional network (TCN), effectively modeling long-term inter-cluster interactions and global signal dependencies. Finally, a multilayer perceptron (MLP) is used to classify valence and arousal levels. Experiments on three public EEG datasets demonstrate that the proposed model significantly outperforms existing EEG-based dimensional emotion recognition methods. Cluster-based learning, reflecting electrode proximity and signal distribution, effectively captures structural patterns at the electrode-cluster level, while inter-cluster information integration further captures global signal interactions, thereby enhancing the interpretability and physiological validity of EEG-based dimensional emotion analysis. This approach provides a scalable framework for future affective computing and brain–computer interface (BCI) applications. Full article

Background: The assessment of emotion recognition holds growing significance in research on the brain–computer interface and human–computer interaction. Among diverse physiological signals, electroencephalography (EEG) occupies a pivotal position in affective computing due to its exceptional temporal resolution and non-invasive acquisition. However, EEG signals are inherently complex, characterized by substantial noise contamination and high variability, posing considerable challenges to accurate assessment. Methods: To tackle these challenges, we propose a Triple Attention Network (TANet), a triple-attention EEG emotion recognition framework that integrates Conformer, Convolutional Block Attention Module (CBAM), and Mutual Cross-Modal Attention (MCA). The Conformer component captures temporal feature dependencies, CBAM refines spatial channel representations, and MCA performs cross-modal fusion of differential entropy and power spectral density features. Results: We evaluated TANet on two benchmark EEG emotion datasets, DEAP and SEED. On SEED, using a subject-specific cross-validation protocol, the model reached an average accuracy of 98.51 ± 1.40%. On DEAP, we deliberately adopted a segment-level splitting paradigm—in line with influential state-of-the-art methods—to ensure a direct and fair comparison of model architecture under an identical evaluation protocol. This approach, designed specifically to assess fine-grained within-trial pattern discrimination rather than cross-subject generalization, yielded accuracies of 99.69 ± 0.15% and 99.67 ± 0.13% for the valence and arousal dimensions, respectively. Compared with existing benchmark approaches under similar evaluation protocols, TANet delivers substantially better results, underscoring the strong complementary effects of its attention mechanisms in improving EEG-based emotion recognition performance. Conclusions: This work provides both theoretical insights into multi-dimensional attention for physiological signal processing and practical guidance for developing high-performance, robust EEG emotion assessment systems. Full article