Search Results (355)

Post-occlusive reactive hyperemia (PORH) is a classical probe of microvascular function, yet its assessment remains largely based on amplitude-derived indices that do not capture the temporal organization of vascular regulation. Photoplethysmography (PPG), widely used in clinical and wearable technologies, offers a practical platform for nonlinear characterization of PORH. Twelve healthy adults underwent a standardized PORH protocol (10 min baseline, 5 min suprasystolic occlusion, 10 min reperfusion) with bilateral reflective green-light PPG. Pulse amplitude, detrended fluctuation analysis (global DFA α exponent), and multiscale entropy (Complexity Index, CI) were computed in 5 min epochs. Occlusion nearly abolished pulsatility in the test limb but produced only modest changes in fractal structure, as α decreased minimally despite near-zero flow. In contrast, CI showed a marked collapse, indicating loss of multiscale organization. During reperfusion, α exhibited a trend toward increased fractal persistence, whereas CI recovered only partially. Contralateral responses were small and detectable mainly through subtle reductions in α during occlusion and consistently higher CI compared with the test limb. These findings indicate that occlusion disrupts multiscale complexity without eliminating fractal persistence, whereas reperfusion restores correlation structure and only partially re-establishes dynamical richness. Overall, DFA and MSE reveal nonlinear features of PORH that are not captured by conventional amplitude-based metrics, extending the physiological interpretation of microvascular responses using widely available PPG technology. Full article

Revealing scale effects and the mechanisms underlying the relationships between plant species and functional diversity is crucial for understanding the stability of desert ecosystems and formulating multiscale conservation strategies. In this study, the spatial patterns of plant species and functional diversity in the Gurbantunggut Desert were analysed via multiscale grid sampling. The results indicated that (1) both species diversity and functional diversity indices exhibited high spatial heterogeneity. At the small scale (10 m × 10 m), the values of the Shannon–Wiener and Pielou indices for fixed dunes were higher in the south than in the north. At the medium and large scales (20 m × 20 m and 50 m × 50 m, respectively), the index values were highest in the southwest, with generally greater values in the south than in the north. For semifixed and mobile dunes, the Shannon–Wiener and Pielou index values exhibited an east-high–west-low pattern at the 10 m × 10 m scale. This differentiation decreased with increasing scale, with the highest values observed in the northeast and southwest at the 50 m × 50 m scale. The spatial differentiation in functional diversity indices (Rao’s second-order entropy index and functional evenness index) exhibited distinct characteristics across the different dune types. (2) The spatial variation in all the diversity indices monotonically decreased with increasing scale, with the variance in the species diversity indices indicating the following order: Shannon–Wiener index > Pielou index > Simpson index. (3) The relationships between species richness and diversity indices exhibited significant scale dependence. At the small and medium scales, species richness was significantly positively correlated with the Shannon–Wiener index, Simpson index, and Rao’s quadratic entropy index and significantly negatively correlated with the Pielou evenness index and functional evenness index. However, at the large scale, none of these correlations were significant. (4) The species diversity indices and Rao’s quadratic entropy index were significantly positively correlated at the small and medium scales (p < 0.01), whereas a significant positive correlation with the functional evenness index was observed only at the 10 m × 10 m scale (p < 0.01). At the larger scale, these correlations became insignificant. In fixed dunes, areas of high Simpson index values exhibited a spatially complementary distribution with areas of high Shannon–Wiener index and Pielou index values, providing evidence for the combined effect of local processes such as competitive exclusion and dispersal limitation. Through comprehensive multiscale analysis, this study revealed the mechanisms underlying the scale-dependent relationships between plant species and functional diversity, thereby providing a theoretical basis for protecting and restoring desert biodiversity. Full article

With the advancement of systematic ecological protection and restoration, ecosystem coordination assessment and multi-scale differentiation analysis have become increasingly important for regional ecological governance. In this context, this study develops a multi-scale coupling coordination evaluation framework for the mountain–water–forest–farmland–lake (MWFFL) system in Dangtu County, Anhui Province. The framework integrates 14 indicators across five subsystems, uses a combined weighting method based on the Entropy Weight Method and Analytic Hierarchy Process, and applies the coupling coordination degree (CCD) model and trend analysis to characterize inter-system coordination and its spatiotemporal patterns at the regional and ecosystem scales. The results indicate that land use is dominated by arable land, with water bodies forming the structural backbone and construction land distributed in clusters. From 2020 to 2024, the mean CCD remained stable around 0.675, indicating that the overall coupling coordination level was relatively stable. Spatially, the CCD pattern remained higher in the southwest and lower in the northwest, with a new high-value clustering zone emerging in the south. At the ecosystem scale, the four ecological restoration units showed distinct spatiotemporal patterns of coupling coordination. This multi-scale MWFFL evaluation framework supports regional ecological monitoring and provides a reference for restoration effectiveness assessment in similar regions under the life community concept. Full article

Power systems continuously generate large-scale load time series data for forecasting, consumption analysis, and equipment health monitoring. However, real-world load measurements are often contaminated by anomalies caused by sensor faults, communication errors, and abnormal consumption behaviors, which may degrade data quality and affect operational decision-making. To address this issue, this paper proposes an integrated anomaly detection framework named PEL, which combines Prophet-based seasonal-trend decomposition, ensemble empirical mode decomposition (EEMD), and a multilayer long short-term memory (LSTM) network. Prophet is first employed to decompose the original series into trend, seasonal, holiday, and residual components. Sample entropy analysis and white noise tests are then adopted to evaluate whether the residual component still contains complex structured information requiring secondary decomposition. Next, EEMD is applied to the residual component to extract multi-scale intrinsic mode functions. Finally, all decomposed components are normalized and fed into a multilayer LSTM model for anomaly detection. Experiments on a real-world power load dataset demonstrate that the proposed PEL framework achieves an accuracy of 99.92%, a precision of 97.33%, a recall of 100%, an F1-score of 98.65%, and an AUC of 0.9996, outperforming or matching several baseline and hybrid models. Full article

The integration of wind energy into power systems relies on forecasting technologies to address operational challenges caused by its volatility and intermittency. This paper proposes a computing architecture for ultra-short-term wind power forecasting. The methodology integrates an adaptive dual-stage signal processing technique with an optimized deep learning model. To manage the non-stationarity of meteorological variables, the Pearson and Maximal Information Coefficient (MIC) analyses are employed for feature selection. The ICEEMDAN algorithm is then used for initial decomposition, followed by sample entropy and K-Means clustering to assess component complexity. Variational Mode Decomposition (VMD) is applied only to the high-frequency component to further separate stochastic fluctuations while preserving relatively stable trend components. A Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network is constructed to forecast the resulting multi-scale components. To reduce reliance on manual empirical tuning, the Crested Porcupine Optimizer (CPO) is used to fine-tune key network hyperparameters. Evaluations using operational wind-farm data indicate that the developed hybrid method captures the temporal dynamics of wind power and yields lower prediction errors than the tested benchmark models. This research provides a data-driven computing framework for renewable-energy forecasting and related operational analysis. Full article

Whey protein isolate (WPI) can assemble into supramolecular complexes with flavonoids via non-covalent interactions, although the underlying binding mechanisms remain not fully understood. In this work, the formation mechanism of the WPI–phloridzin (PHL) complex was systematically investigated using an integrated experimental and computational approach. High-performance liquid chromatography quantified the binding content of PHL as 1.3% (w/w). Isothermal titration calorimetry indicated that the process was entropy-driven and governed predominantly by hydrophobic and electrostatic interactions. Complementary circular dichroism spectroscopy and molecular dynamics simulations revealed that complexation induces modest conformational adjustments in the protein’s secondary structure. Collectively, this multi-scale analysis provides mechanistic insights into the dynamic formation of the WPI–PHL complex, offering theoretical insights into protein–flavonoid recognition. Full article

Magnitude alone fails to capture the full complexity of pavement roughness; its spatial distribution along a road is equally vital for effective maintenance planning. While traditional assessment has long relied on specialized survey vehicles, the rise of mobile crowdsensing now allows for massive data acquisition via smartphone sensors. This study investigates the spatial structure of pavement roughness using crowdsensed data from the SmartRoadSense platform. Roughness is quantified through the Power of Prediction Error (PPE) indicator derived from smartphone accelerometer signals. The dataset consists of 475 observations sampled at 20 m intervals over approximately 9.5 km of the A3/E45 motorway in southern Italy. A multi-scale spatial–statistical framework is adopted to analyse the roughness signal. The analysis includes the evaluation of scale-dependent statistical descriptors (mean and coefficient of variation), as well as spatial correlation, spectral, and entropy-based measures. The results indicate a short spatial correlation length (approximately 60–100 m) and the absence of a dominant spatial wavelength, suggesting that pavement roughness behaves as a localized multiscale process. A complementary segmentation analysis based on Classification and Regression Trees (CART) is performed to explore the spatial partitioning of the roughness signal. Our analysis indicates that segmentation complexity spikes once the minimum node size drops below roughly 10 observations. This trend points to the existence of localized irregularities that coarser scales simply overlook. Ultimately, these results suggest that mean roughness values alone are insufficient for describing pavement condition and that hybrid spatial–statistical approaches may support more scalable, data-driven, and spatially targeted pavement monitoring strategies for sustainable transportation infrastructure management. Full article

Urban flood susceptibility mapping is essential for disaster risk management in rapidly urbanizing regions. Although high-resolution Earth observation (EO) data provide detailed information for fine-scale flood analysis, existing studies are often limited by inadequate representation of drainage capacity, inappropriate spatial scales, and model uncertainty under sparse flood sample conditions. To address these issues, this study develops a multi-scale urban flood susceptibility mapping framework based on the Maximum Entropy (MaxEnt) model, integrating multi-source high-resolution geospatial data. A three-tier spatial unit system, including catchment, street, and grid scales, was constructed. Two models were developed at each scale using per capita drainage density (PCDD) and pipe density (PipeDen) as drainage capacity indicators. The results reveal significant scale-dependent differences in spatial autocorrelation, model performance, and variable responses. Compared with the PipeDen-based model, the standard deviation of AUC decreased by 37.5% and 25.0% at the catchment and street scales, respectively, and the model produced a more physically consistent relationship between drainage capacity and urban flood susceptibility. Considering the combined results of model performance, spatial autocorrelation, and response-curve analysis, the street scale PCDD-based model achieved the best overall performance among the six multi-scale models. Impervious area ratio, distance to roads, and annual maximum daily precipitation were identified as dominant factors influencing urban flood susceptibility. Based on the optimal street scale PCDD-based model, a 2 m resolution susceptibility map was generated, showing that high-susceptibility areas are mainly concentrated in highly urbanized central districts and along major transportation corridors. This study highlights the importance of spatial scale and drainage capacity representation in high-resolution urban flood susceptibility mapping. Full article

With the continuous advancement of thrust-to-weight ratios in modern aero-engines, turbine inlet temperatures have reached levels that far exceed the thermal endurance limits of conventional superalloys and emerging ceramic matrix composites (CMCs). Consequently, thermal barrier coatings (TBCs) and environmental barrier coatings (EBCs) have become indispensable multifunctional systems for hot-section component protection. This review systematically delineates the evolutionary trajectory of TBC/EBC systems, transitioning from traditional yttria-stabilized zirconia (YSZ) and simple silicates to advanced multi-rare-earth-doped oxides, A₂B₂O₇ pyrochlore structures, and high-entropy ceramic systems. A critical comparative assessment is provided regarding their phase stability, thermal-physical properties, and durability challenges above 1200 °C. Furthermore, this paper provides an in-depth analysis of high-temperature degradation mechanisms, focusing on the thermochemical and thermomechanical interactions under calcium-magnesium-alumino-silicate (CMAS) attack, water-oxygen corrosion, and molten salt infiltration. By synthesizing current research gaps, we highlight the trade-offs between low thermal conductivity, high toughness, and environmental resistance. Finally, a strategic roadmap for next-generation coatings is proposed, emphasizing the integration of high-entropy material design, multi-scale structural optimization, and AI-driven life prediction models to meet the stringent reliability requirements of future propulsion systems. Full article

Detecting small ship targets in sea–sky background environments is challenging due to interference from clouds, islands, sea clutter, and the limited spatial information in long-range infrared imagery. To address these issues, this paper proposes a robust detection framework that integrates multi-scale spatial feature enhancement with temporal trajectory analysis. First, a candidate target extraction method based on a multi-scale differential histogram of oriented gradients is introduced. By exploiting gradient distribution differences between targets and surrounding backgrounds, our method effectively enhances target responses while suppressing structured background edges. This response is further fused with a log-spectrum-based saliency map to improve target contrast and reduce clutter. Next, a candidate trajectory extraction algorithm based on inverse optical flow matching is developed to utilize temporal consistency. Optical flow-based grayscale compensation predicts target intensity changes between frames, while Kalman filtering estimates motion states and performs trajectory association. Finally, a multi-feature trajectory filtering strategy is designed, combining motion entropy stability, peak signal-to-noise ratio, and trajectory lifecycle to distinguish true targets from false alarms. Experimental results on eight infrared maritime sequences demonstrate superior performance. The proposed method achieves an average Background Suppression Factor (BSF) of 45.2 and an average Signal-to-Clutter Ratio Gain (SCRG) of 22.3 × 10³, representing a substantial improvement over all baseline algorithms. Receiver Operating Characteristic analysis further confirms a mean detection rate exceeding 90% at a false-alarm rate of 10⁻³ across all sequences, confirming improved detection performance and robustness in complex maritime environments. Full article

Breast cancer remains the leading cause of cancer-related deaths among women globally. Early detection through mammography is vital for improving survival rates; however, the large volume of medical images and subtle variations in lesion characteristics pose significant challenges to manual interpretation. Recent automated diagnostic models based on deep learning have shown strong potential for breast cancer classification, but challenges such as overfitting, high computational complexity, limited generalization, and insufficient interpretability remain unresolved. This paper proposes a computationally efficient and context-aware deep learning framework for breast cancer classification using transformer-based multi-scale attention mechanisms and explainable artificial intelligence (XAI). The proposed architecture integrates the Hierarchical Multi-Scale Transformer (HMT) and Edge-Aware Local Transformer (ELT) modules to jointly capture global contextual dependencies and boundary-sensitive local representations from mammographic images. ELT improves feature refinement in high-entropy regions, while HMT models global semantic interactions across multiple feature scales. In addition, an Adaptive Contextual Refinement (ACR) module is introduced to preserve semantically consistent feature representations across spatial resolutions. A Meta-Ensemble Classification (MEC) framework combining weighted SVM and K-Nearest Neighbors (KNN) classifiers is further employed using validation-guided class-adaptive weighting. The proposed framework is evaluated on four benchmark mammography datasets, namely CBIS-DDSM, DDSM, INBreast, and MIAS. The proposed model has demonstrated superior accuracy of over 99% across all breast cancer datasets. The model surpassed transformer-based baselines including Swin-T and ViT while maintaining lower parameter complexity and achieving approximately 7% higher accuracy on the CBIS-DDSM dataset. The proposed framework also demonstrated strong cross-dataset generalization and consistently achieved high precision, recall, and F1-score values across all benchmark datasets. To improve model interpretability, Grad-CAM, SHAP, Occlusion Sensitivity Analysis (OSA), and the proposed TIxAI consistency analysis framework are incorporated to provide preliminary explainability assessment for mammographic classification. The explainability analysis demonstrated spatially consistent saliency behavior across benchmark datasets; however, the current evaluation is based on internal saliency consistency rather than external clinical validation using expert lesion annotations. Overall, the proposed framework provides an effective and computationally efficient approach for automated breast cancer classification while improving model explainability and interpretability. Full article

Aiming at solving the problems of the complex impact vibration characteristics of reciprocating compressor valves, the inability of a single signal to fully characterize state characteristics, and the difficulty of effectively extracting and fusing feature information from multi-source signals, this paper constructs a fault diagnosis and prediction model combining Improved Swarm Decomposition (ISWD) and t-SNE dimensionality reduction and fusion with a Multi-scale Convolutional Neural Network–Bidirectional Gated Recurrent Unit (MCNN-BiGRU) based on multi-source signals and applies it to the fault diagnosis and pattern recognition prediction of reciprocating compressor valves. Firstly, atom search optimization (ASO) is adopted to optimize the decomposition parameters of Swarm Decomposition (SWD) to obtain the ISWD algorithm, which is applied to decompose the multi-source signals of compressors to extract the oscillating components (OCs). Secondly, the correlation coefficient method is used to screen the OCs and conduct signal reconstruction, and various entropy feature values are extracted from the reconstructed signals to form an initial feature set. Then the t-SNE algorithm is employed to perform dimensionality reduction and fusion on the initial feature set, yielding a more concise and representative fused feature set. Finally, the fused feature set after dimensionality reduction and fusion is input into the MCNN-BiGRU model for training, so as to realize the pattern recognition and prediction of valve faults. The effectiveness and superiority of this method in the fault diagnosis of reciprocating compressor valves are verified through numerical simulation and experimental analysis. Full article

Cupping therapy has been demonstrated to improve hemodynamic regulation. Existing studies have reported mean changes of oxyhemoglobin (OxyHb) and deoxyhemoglobin (DeoxyHb), which do not capture the multi-scale regulatory dynamics of the microvasculature. It is therefore unclear whether cupping therapy modulates the complexity and fractal property of hemodynamic signals. The objective of this study was to examine complexity of hemodynamic response to cupping therapy. A 2 by 2 factorial design with repeated measures was used to examine the main effect of pressure (−225 and −300 mmHg) and duration (5 and 10 min) and their interaction. A near infrared spectroscopy (NIRS) was used to measure OxyHb and DeoxyHb concentrations before and after cupping therapy. A total of 18 healthy participants were enrolled in this study. The wavelet analysis, sample entropy and detrended fluctuation analysis (DFA) were used to quantify the oscillatory, complexity, and fractal scaling properties of OxyHb and DeoxyHb signals. A two-way ANOVA with Bonferroni correction was used to examine the main and interaction effects. The results demonstrated that the combined effects of pressure and duration, rather than either factor independently, were the primary determinants of the dynamic hemodynamic response to cupping therapy, with significant Pressure × Duration interactions observed in DeoxyHb myogenic wavelet power (F = 4.636, p = 0.046, η²p = 0.214), OxyHb (F = 5.704, p = 0.029, η²p = 0.251) and DeoxyHb (F = 6.600, p = 0.020, η²p = 0.280) sample entropy, and DeoxyHb DFA scaling exponent (F = 5.598, p = 0.030, η²p = 0.248). In addition, cupping pressure selectively modulated neurogenic DeoxyHb oscillatory power (F = 5.001, p = 0.039, η²p = 0.227), and cupping duration significantly altered the fractal scaling properties of DeoxyHb signals (F = 7.775, p = 0.013, η²p = 0.314). The findings indicate that the interaction of pressure and duration of cupping therapy could effectively modulate hemodynamic responses. To the best of our knowledge, this is the first study investigating the complexity of hemodynamic responses after cupping therapy. Full article

Accurate multi-class segmentation of the prostate in T2-weighted magnetic resonance imaging (MRI) into the peripheral zone (PZ), central gland (CG) and tumour is essential for targeted biopsy guidance and treatment planning. We present DSBANet, an encoder–decoder architecture that combines a pretrained ResNet-50 encoder, Atrous Spatial Pyramid Pooling, Multi-Scale Attention Fusion on skip connections, a Feature Fusion Module, deep supervision and boundary refinement. We evaluate eight architectures across three input dimensionalities (2D, 2.5D, 3D), yielding 24 models trained under identical conditions on the Prostate158 dataset. DSBANet achieves the best anatomy segmentation with PZ DSC of 0.8176 and CG DSC of 0.7888 among 2D models. To address the severe class imbalance of the tumour class, we further train DSBANet 2D with a class-weighted cross-entropy term and tumour-positive slice oversampling, raising per-case tumour DSC from 0.003 to 0.170 (a sixty-fold absolute improvement). A systematic eight-variant ablation study, evaluated under matched-pairs effect-size analysis, identifies the SE-Residual blocks and skip-connection attention as the largest contributors to tumour segmentation, while every architectural component contributes a directionally consistent gain. Full article

Underwater images often exhibit low contrast and loss of detail due to light scattering and absorption, which poses significant challenges for visual analysis in aquatic environments. Polarization imaging addresses these issues by exploiting the polarization states of light, effectively reducing backscatter and enhancing image contrast. In this paper, we propose a polarization image fusion method guided by information entropy and a hierarchical-adaptive fusion strategy. Local information entropy is first employed to perform multiscale denoising on Degree of Linear Polarization (DOLP) images, enabling adaptive detail reconstruction while distinguishing texture from noise. Subsequently, a hierarchical fusion framework is applied: low-frequency components are enhanced through detail injection, while high-frequency components are fused using a structure-guided mechanism that leverages low-frequency gradient information to generate soft masks for phase-aligned detail integration and edge sharpening. Experiments conducted on self-collected underwater images, two public underwater datasets, and three general-scene datasets demonstrate that the proposed method improves objective metrics, including information entropy, average gradient, and edge strength. Subjective evaluations further confirm its effectiveness in preserving details and adapting to diverse scenes. Furthermore, rigorous ablation studies and runtime analyses demonstrate that the optimized framework achieves a highly favorable balance between robust, artifact-free detail enhancement and computational efficiency. The proposed approach provides a practical solution for underwater image enhancement, with potential applications in target detection and infrastructure inspection. Full article