Search Results (540)

We develop a convergence framework for Grünwald–Letnikov (GL) fractional and classical integer difference operators acting on sequences in fuzzy-paranormed (fp) spaces, motivated by data that are imprecise and contain sporadic outliers. Fuzzy paranorms provide a resolution-dependent notion of proximity, while statistical and lacunary statistical convergence downweight sparse deviations by natural density; together, they yield robust criteria for difference-filtered signals. Within this setting, we establish uniqueness of fp–${\mathsf{\Delta }}^m$ statistical limits; an equivalence between fp-statistical convergence of ${\mathsf{\Delta }}^m$ (and its GL extension ${\mathsf{\Delta }}^{\alpha }$) and fp-strong p-Cesàro summability; an equivalence between lacunary fp-${\mathsf{\Delta }}^m$ statistical convergence and blockwise strong p-Cesàro summability; and a density-based decomposition into a classically convergent part plus an fp-null remainder. We also show that GL binomial weights act as an ${\ell }^1$ convolution, ensuring continuity of ${\mathsf{\Delta }}^{\alpha }$ in the fp topology, and that nabla/delta forms are transferred by the discrete Q–operator. The usefulness of the criteria is illustrated on simple engineering-style examples (e.g., relaxation with memory, damped oscillations with bursts), where the fp-Cesàro decay of difference residuals serves as a practical diagnostic for Cesàro compliance. Beyond illustrative mathematics, we report engineering-style diagnostics where the fuzzy Cesàro residual index correlates with measurable quantities (e.g., vibration amplitude and energy surrogates) under impulsive disturbances and missing data. We also calibrate a global decision threshold ${\tau }_{\mathrm{glob}}$ via sensitivity analysis across $(\alpha ,p,m)$, where $m\in \mathbb{N}$ is the integer difference order, $\alpha >0$ is the fractional order, and $p\ge 1$ is the Cesàro exponent, and provide quantitative baselines (median/M-estimators, ${\ell }_1$ trend filtering, Gaussian Kalman filtering, and an $\alpha$-stable filtering structure) to show complementary gains under bursty regimes. The results are stated for integer m and lifted to fractional orders $\alpha >0$ through the same binomial structure and duality. Full article

Expansive soils present a significant geotechnical challenge due to their pronounced volume changes with moisture variations, leading to substantial infrastructure damage. This study investigates the efficacy of thermal stabilization in mitigating the swell potential and compressibility of a high-plasticity, kaolinite-rich clay from Al Ghat, Saudi Arabia. As well, the changes in basic properties including consistency limits, specific gravity, and compaction characteristics were studied and highlighted. Microstructural studies using X-ray diffraction (XRD), Scanning electron microscopy (SEM), and Energy-dispersive X-ray spectroscopic (EDX) were performed to trace the structural changes and interpret the achieved improvement. Soil specimens were subjected to heat treatment at levels of 200 °C, 400 °C, and 600 °C for two hours, after which their geotechnical and microstructural properties were comprehensively evaluated. The results demonstrate a direct correlation between increasing temperature and the reduction in expansive behavior. Treatment at 600 °C caused a substantial decrease in the plasticity index from 27.00 to 2.94. Correspondingly, oedometer tests showed that the free swell was reduced from 6% to nearly zero, and the swelling pressure was eliminated, dropping from 250 kPa to 0 kPa. XRD analysis confirmed kaolinite decomposition through dehydroxylation, producing metakaolin with diminished water absorption capacity. SEM further revealed significant particle aggregation and the formation of a coarser soil fabric. The findings confirm that heat treatment at temperatures of 400 °C and above is a highly effective method for permanently stabilizing kaolinitic expansive soils, rendering them suitable for construction applications. Full article

Schlieren imaging is a powerful optical sensing technique that captures flow-induced refractive index gradients, offering valuable visual data for analyzing complex fluid dynamics. However, the large volume and structural complexity of the data generated by this sensor pose significant challenges for extracting key physical insights and performing efficient reconstruction and temporal prediction. In this study, we propose a Spatial-Frequency-Scale variational autoencoder (SFS-VAE), a deep learning framework designed for the unsupervised feature decomposition of Schlieren sensor data. To address the limitations of traditional $\beta$-variational autoencoder ($\beta$-VAE) in capturing complex flow regions, the Progressive Frequency-enhanced Spatial Multi-scale Module (PFSM) is designed, which enhances the structures of different frequency bands through Fourier transform and multi-scale convolution; the Feature-Spatial Enhancement Module (FSEM) employs a gradient-driven spatial attention mechanism to extract key regional features. Experiments on flat plate film-cooled jet schlieren data show that SFS-VAE can effectively preserve the information of the mainstream region and more accurately capture the high-gradient features of the jet region, reducing the Root Mean Square Error (RMSE) by approximately 16.9% and increasing the Peak Signal-to-Noise Ratio (PSNR) by approximately 1.6 dB. Furthermore, when integrated with a Transformer for temporal prediction, the model exhibits significantly improved stability and accuracy in forecasting flow field evolution. Overall, the model’s physical interpretability and generalization ability make it a powerful new tool for advanced flow diagnostics through the robust analysis of Schlieren sensor data. Full article

Given the inadequacies in the management of reactive power compensation equipment in distribution networks and insufficient power data mining, existing studies pay little attention to asymmetric reactive power compensation equipment and face pain points such as difficult quantification of nonlinear relationships and challenging evaluation of mechanical switches. First, this paper proposes a data mining-based diagnostic method for the operating status of asymmetric reactive power compensation equipment: it preprocesses data via singular value decomposition and matrix approximation. Second, it classifies load types with K-means clustering, defines “health degree” by introducing mutual information and a reliability coefficient, constructs dual switching criteria, and defines the switching qualification rate. Third, the TOPSIS method is employed for dual-index comprehensive evaluation, and equipment status levels are classified with statistical analysis. Finally, the case analysis demonstrates that the proposed method is accurate, applicable, and easy to implement, which can serve as a basis for equipment troubleshooting and maintenance, thereby filling the relevant research gap. Full article

Exploring homogeneous dense groups is one of the important issues in social network structure measurement. k-core decomposition and core member filtering are common methods to uncover homogeneous dense groups in a network. However, existing methods of k-core decomposition struggle to support in-depth exploration of homogeneous dense groups. To address this issue, we store social networks in a graph database, taking advantage of its characteristics such as property indexes and batch queries. Based on this storage, we propose a k-core decomposition algorithm to improve the efficiency of homogeneous dense group detection. Subsequently, we introduce a core member filtering algorithm for identifying core members, a key exploration goal of this study. In experiments, we verify the efficiency of the k-core decomposition algorithm. Finally, we conduct an in-depth analysis of the characteristics of k-cores and their core members, yielding several important conclusions. For example, the relationship between the core number and the number of nodes obeys the power law distribution. In addition, we find that despite the strong connection of the core members, they do not play an important role in the information spreading of social networks. Full article

Research on Mesoscale Convective Systems (MCSs) in Ecuador has focused on regional studies. However, it lacks a thorough and general examination of their relationship with the nation’s diverse orography and large-scale phenomena. This study conducts a climatological analysis of MCS occurrence throughout Ecuador’s natural regions. We perform this study using Sen’s Slope and the Mann–Kendall test. Teleconnections from the Pacific and Atlantic Oceans are studied through wavelet decomposition between time series and Pacific and Atlantic oceanic indices. The main factors that control MCS formation depend on the region. The Intertropical Convergence Zone (ITCZ) at the large scale affects the entire territory. In western Ecuador, MCS formation is mostly related to the El Niño current and the Chocó Low-Level Jet (CLLJ). The Orinoco Low-Level Jet (OLLJ) and evapotranspiration and nocturnal convection display the largest roles in the east. A progressive intensification of activity from Highlands-North in SON is detected (0.143 MCSs per year). MCSs contribute 26% of total precipitation on average, with regional variations from Coast-South (16.41%) to Amazon-North (44.13%). The research confirms existing knowledge about El Niño’s strong relationship (ρ = 0.7) with MCS occurrence in coastal areas while uncovering new complex patterns. The Trans-Nino Index (TNI) functions as a critical two-sided modulator that conventional analysis methods fail to detect. It produces null correlations over conventional time series of MCS occurrence yet emerges as a primary driver of low-frequency variability in the proposed six natural zones of Ecuador. Wavelet decomposition reveals contrasting TNI responses: Amazon-North shows positive correlation (0.73) while Amazon-South exhibits negative correlation (−0.70) at low frequencies. This affects Walker circulations dynamics over the Pacific Ocean. This research establishes fundamental knowledge about MCSs in Ecuador. It builds on a database with strong methodology as a backbone. The research provides essential information about the factors leading to convection in the country. This will help improve seasonal forecast accuracy and risk management effectiveness. Full article

We aimed to investigate the utility of peritumoral edema-derived radiomic features from magnetic resonance imaging (MRI) image weights and fused MRI sequences for enhancing the performance of machine learning-based glioma grading. The present study utilized the Multimodal Brain Tumor Segmentation Challenge 2023 (BraTS 2023) dataset. Laplacian Re-decomposition (LRD) was employed to fuse multimodal MRI sequences. The fused image quality was evaluated using the Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. A comprehensive set of radiomic features was subsequently extracted from peritumoral edema regions using PyRadiomics. The Boruta algorithm was applied for feature selection, and an optimized classification pipeline was developed using the Tree-based Pipeline Optimization Tool (TPOT). Model performance for glioma grade classification was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC) parameters. Analysis of fused image quality metrics confirmed that the LRD method produces high-quality fused images. From 851 radiomic features extracted from peritumoral edema regions, the Boruta algorithm selected different sets of informative features in both standard MRI and fused images. Subsequent TPOT automated machine learning optimization analysis identified a fine-tuned Stochastic Gradient Descent (SGD) classifier, trained on features from T₁Gd+FLAIR fused images, as the top-performing model. This model achieved superior performance in glioma grade classification (Accuracy = 0.96, Precision = 1.0, Recall = 0.94, F1-Score = 0.96, AUC = 1.0). Radiomic features derived from peritumoral edema in fused MRI images using the LRD method demonstrated distinct, grade-specific patterns and can be utilized as a non-invasive, accurate, and rapid glioma grade classification method. Full article

Analysis of global mean sea-level (GMSL) variations provides insights into their spatial and temporal characteristics. To analyze the sea-level cycle and its correlation with the El Niño–Southern Oscillation (ENSO, represented by the Oceanic Niño Index), this study proposes an enhanced analytical framework integrating Local Mean Decomposition with an improved wavelet thresholding technique and wavelet transform. The GMSL time series (January 1993 to July 2020) underwent multi-scale decomposition and noise reduction using Local Mean Decomposition combined with improved wavelet thresholding. Subsequently, the Morlet continuous wavelet transform was applied to analyze the signal characteristics of both GMSL and the Oceanic Niño Index. Finally, cross-wavelet transform and wavelet coherence analyses were employed to investigate their correlation and phase relationships. Key findings include the following: (1) Persistent intra-annual variability (8–16-month cycles) dominates the GMSL signal, superimposed by interannual fluctuations (4–8-month cycles) related to climatic and seasonal forcing. (2) Phase analysis reveals that GMSL generally leads the Oceanic Niño Index during El Niño events but lags during La Niña events. (3) Strong El Niño episodes (May 1997 to May 1998 and October 2014 to April 2016) resulted in substantial net GMSL increases (+7 mm and +6 mm) and significant peak anomalies (+8 mm and +10 mm). (4) Pronounced negative peak anomalies occur during La Niña events, though prolonged events are often masked by the long-term sea-level rise trend, whereas shorter events exhibit clearly discernible and rapid GMSL decline. The results demonstrate that the proposed framework effectively elucidates the multi-scale coupling between ENSO and sea-level variations, underscoring its value for refining the understanding and prediction of climate-driven sea-level changes. Full article

The transportation sector is crucial for achieving China’s “dual carbon” strategic goals, yet its emission drivers and decoupling mechanisms exhibit significant provincial heterogeneity that remains underexplored. Existing studies predominantly rely on the LMDI method, which suffers from limitations in handling multiple absolute indicators, and rarely quantify the policy-driven decoupling effort. To address these gaps, this study employs the generalized Divisia index method to decompose transportation carbon emissions across thirty Chinese provinces from 2005 to 2022. Furthermore, we innovatively integrate the Tapio decoupling model with a novel decoupling effort model to assess both the decoupling state and the effectiveness of emission reduction policies. Our key findings reveal that: (1) economic output scale was the primary driver of emission growth, while output carbon intensity was the dominant mitigation factor; (2) driving mechanisms varied considerably across provinces, with 83% of provinces primarily driven by economic scale expansion; (3) the national decoupling state improved from weak to strong decoupling, with 53% of provinces achieving decoupling advancement; and (4) intensity effects were the core driver enabling decoupling efforts, while scale effects represented the primary inhibiting factor. This study provides a robust analytical framework and empirical evidence for formulating differentiated decarbonization strategies across Chinese provinces. Full article

This study proposes a four-module “decomposition–forecasting–ensemble–correction” framework to improve the accuracy of complex coal price forecasts. The framework combines Variational Mode Decomposition (VMD), adaptive Autoregressive Integrated Moving Average (ARIMA) and Gated Recurrent Unit (GRU)-Attention forecasting models, a data-driven weighted ensemble strategy, and an innovative error correction mechanism. Empirical analysis using the Bohai-Rim Steam–Coal Price Index (BSPI) shows that the framework significantly outperforms benchmark models, as validated by the Diebold–Mariano test. It reduces the Mean Absolute Percentage Error (MAPE) by 30.8% compared to a standalone GRU-Attention model, with the error correction module alone contributing a 25.1% MAPE reduction. This modular and transferable framework provides a promising approach for improving forecasting accuracy in complex and volatile economic time series. Full article

In light of the increasing focus on global climate change and environmental issues, countries around the world are collaboratively working towards the establishment of a low-carbon economy (LCE). As the most populous developing nation, China is proactively advocating for low-carbon economic development as a means to achieve sustainable growth. Nevertheless, the efficiency of the low-carbon economy (LCEE) exhibits considerable variation across different regions within China. This article seeks to explore the regional disparities in LCEE throughout the country and to identify the factors that contribute to these variations. Firstly, this paper examines the advancements in LCEE research, concentrating on an analysis of 30 Chinese provinces. Employing the Multi-directional Efficiency Analysis (MEA) framework alongside the global Malmquist (GM) index, this study evaluates the efficiency of the low-carbon economy across the 30 provinces from 2010 to 2021. Secondly, by integrating spatial autocorrelation analysis techniques, the research encompasses a multifaceted examination, including spatiotemporal analysis, regional disparities, driving factors, and potential for improvement. The findings indicate significant discrepancies in LCEE among various provinces in China. Notably, LCEE tends to be higher in the eastern coastal regions, attributed to their advanced economic development, whereas the western inland areas generally exhibit lower efficiency levels due to comparatively limited economic progress. Thirdly, LCEE exhibits significant spatial heterogeneity, with clear high–high and low–low clustering patterns, revealing systemic coordination gaps between eastern coastal and central/western regions. Fourthly, from the decomposition results of the global Malmquist index, it can be seen that efficiency change (EC) is less than 1 and technology change (TC) is greater than 1, which promotes the improvement of LCEE. Technical efficiency is the main factor affecting the improvement of LCEE. Full article

The chlorinated polyvinyl chloride (CPVC)/acrylonitrile–butadiene–styrene (ABS) composite represents an important class of engineering thermoplastics, offering a strong balance of flame retardancy, chemical resistance, mechanical properties, processability, and cost efficiency. Despite its widespread application, the flame-retardant mechanism in the CPVC/ABS system remains poorly understood. This work systematically investigated the non-monotonic flame-retardant behavior of CPVC/ABS composites through comprehensive characterization. The combustion performance, as determined by limiting oxygen index (LOI), UL-94 vertical burning tests, and cone calorimeter tests (CCTs), showed an unexpected pattern of flame retardancy initially improving then decreasing with reduced ABS content, which contradicted conventional expectations. The optimal composition at a CPVC/ABS ratio of 2:3 demonstrated good performance, achieving a UL-94 5VA rating and 47.3% reduction in total heat release (THR) relative to CPVC. A more stable and compact structure was observed from the morphology analysis of the residual char, and the thermogravimetric analysis further revealed a synergistic effect in carbonization behavior. The above flame-retardant mechanism could be interpreted by the combined effects of accelerated char formation during the early decomposition stage and significantly enhanced char crosslinking degree. These findings provided fundamental insights for designing high-performance flame-retardant polymer composites and facilitating their industrial implementation. Full article

The rapid expansion of the meat duck industry in China has intensified environmental challenges, particularly those related to managing high-moisture duck manure. Fermentation bed systems, utilizing rice husks as a primary substrate, offer a sustainable solution by promoting waste decomposition and improving animal welfare. This study investigated microbial diversity in rice husk-based fermentation bed materials across different usage durations to assess their ecological feasibility. Samples were collected from a duck farm in Linyi, China, after one, three, five and seven batches of duck rearing (21 days per batch). Microbial communities were analyzed using polymerase chain reaction–denaturing gradient gel electrophoresis (PCR-DGGE), followed by cluster analysis, principal component analysis (PCA) and sequencing of recovered DGGE bands. The results revealed significant shifts in microbial composition, with low similarity (18% overall) and distinct abundance patterns among groups. Bacteroidetes abundance increased with prolonged usage, while Staphylococcus aureus was only detected in the first batch. A total of 32 sequenced bands identified dominant phyla, including Actinobacteria, Proteobacteria, Firmicutes and Bacteroidetes. Group 4 (seven batches) exhibited the highest microbial diversity and richness (Shannon index: 2.68; mean abundance: 16.33 bands), which was attributed to organic matter accumulation and nutrient release during fermentation. These findings demonstrate that rice husk-based fermentation beds maintain robust microbial diversity over time, effectively supporting waste degradation and duck health. We conclude that rice husks are a viable, eco-friendly substrate for waterfowl fermentation bed systems, with periodic microbial supplementation recommended to enhance long-term efficacy. This work provides critical insights for optimizing sustainable livestock farming practices. Full article

The study aimed to optimize an oil-in-water emulsion loaded with the antioxidant extract of Randia monantha using Coccoloba uvifera seed protein (CUSP) as emulsifier and ultrasound-assisted processing. Response surface methodology (RSM) was employed to evaluate the effects of protein concentration (2, 3, and 4%), oil amount (5, 15, and 25%), and ultrasound duration (3, 5, and 7 min) on the polydispersity index (PDI) and droplet size. A total of 21 mg of extract was added to each formulation. The optimal conditions were a 3% protein concentration, 20% oil content, and 7 min of ultrasound. Under these conditions, the emulsion showed low PDI (1.88), D_[3,2] (1.11 µm), and D_[4,3] (1.60 µm). It remained stable at 4 °C for 15 days within a pH range of 6−10, with NaCl concentrations < 200 mM and at temperatures between 25 and 50 °C. Thermal analysis of the emulsion revealed endothermic transitions and decomposition events at higher temperatures, achieving 100% entrapment efficiency and ~83% photoprotection for the extract. This plant protein stabilizes the extract at the oil/water interface, enhancing thermal stability and protecting against photodamage. These qualities are vital in the food industry for preserving thermolabile compounds. The emulsion can enhance antioxidant properties in semi-solid foods or be spray-dried into a powder for functional formulations. Full article

This study evaluates the eco-efficiency and eco-productivity of 141 countries using data-driven analytical frameworks over the period 2018–2023, covering the pre-COVID, COVID, and post-COVID phases. We employ an input-oriented Slack-Based Measure Data Envelopment Analysis (SBM-DEA) under variable returns to scale (VRS), combined with the Malmquist Productivity Index (MPI), to assess both static and dynamic performance. The analysis incorporates three inputs—labor force, gross fixed capital formation, and energy consumption—one desirable output (gross domestic product, GDP), and one undesirable output (CO₂ emissions). Eco-efficiency (the joint performance of energy and carbon efficiency) and eco-productivity (labor and capital efficiency) are evaluated to capture complementary dimensions of sustainable performance. The results reveal significant but temporary gains in eco-efficiency during the peak pandemic years (2020–2021), followed by widespread post-crisis reversals, particularly in labor productivity, energy efficiency, and CO₂ emission efficiency. These reversals were often linked to institutional and structural barriers, such as rigid labor markets and outdated infrastructure, which limited the translation of technological progress into operational efficiency. The MPI decomposition indicates that, while technological change improved in many countries, efficiency change declined, leading to overall stagnation or regression in eco-productivity for most economies. Regression analysis shows that targeted policy stringency in 2022 was positively associated with eco-productivity, whereas broader restrictions in 2020–2021 were less effective. We conclude with differentiated policy recommendations, emphasizing green technology transfer and institutional capacity building for lower-income countries, and the integration of carbon pricing and innovation incentives for high-income economies. Full article