Search Results (22)

Driven by sea-level rise and frequent compound coastal flooding, accurate inundation simulation is essential for disaster mitigation and urban planning. To address the topologically disconnected overestimation errors inherent in the traditional bathtub model, this study proposes a dynamic coastal inundation simulation framework based on an 8-neighbor seed-spread algorithm. Within this framework, a digital elevation model (DEM) is resampled to a 10 m spatial resolution, and a high frequency tidal sequence with a 5-min temporal resolution is reconstructed from typical spring tides. The vertical datums of both the topography and tidal water levels are strictly unified to the Mean Sea Level (MSL) to maintain physical consistency. Comparative experiments across multiple water level scenarios reveal a distinct threshold effect and non-linear expansion characteristics in inundation responses under complex geomorphological conditions. Because the traditional bathtub model fails to account for the blocking effects of inland physical barriers, its overestimation increases significantly once the water level exceeds critical flood protection thresholds. By generating high resolution Time of Arrival (ToA) maps, the proposed framework provides a robust spatial–temporal basis for precise coastal risk assessment, evacuation planning, and defense resource allocation. Full article

Reversible data hiding (RDH) enables the reversible embedding of additional data into cover media, allowing the cover media to be perfectly recovered after extracting the embedded data. RDH relies on accurate prediction of pixels to generate sharply distributed prediction error histograms, thereby maximizing embedding capacity and minimizing visual distortion. While convolutional neural network (CNN)-based predictors excel in smooth regions of cover images by leveraging local correlation, they often fail to produce accurate predictions in the textured regions. To address the limitation of CNN predictors, we propose a novel attention fusion-based CNN predictor (AFCNNP) that adaptively combines the CNN predictor with a non-local means (NLM) predictor. The proposed fusion framework learns spatial weight maps to favor CNN predictions in smooth regions and NLM predictions in textured regions. The experimental results show that the proposed framework outperforms other state-of-the-art CNN predictors by significantly lowering the mean absolute error, mean squared error, and variance of prediction errors, leading to more accurate pixel predictions. With the proposed fusion framework, the embedding and visual performance of prediction error expansion (PEE)-based RDH is improved compared to typical CNN-based RDH methods. Full article

Balancing food production, water conservation, and carbon emissions (CEs) is critical in Northeast China (NEC), yet food–carbon–water (FCW) interactions remain poorly quantified at pixel scale. Conceptually, we move beyond administrative-unit nexus assessments by providing a crop-explicit, grid-based FCW diagnosis that identifies where crop-specific bottlenecks emerge and supports zoning-oriented interventions. We fused multi-source datasets with process models to estimate CEs, water use efficiency (WUE), and yield for maize, rice, and soybean at 500 m resolution during 2001–2020 and evaluated synergies/trade-offs based on Sen’s slope trends and nexus performance using coupling coordination degree (CCD). Annual mean CE (230.8–37,300 kg CO₂-eq ha⁻¹), yield (0–10,031 kg ha⁻¹), and WUE (0–6 kg C m⁻³) exhibited pronounced spatial heterogeneity. Higher CEs and yield concentrated in the central–southern plains, whereas WUE showed a patchier pattern with localized high values. Temporally, CEs increased for all crops, with rice consistently exhibiting the highest CEs. Soybean showed the most pronounced WUE improvement, reaching >2.0 kg C m⁻³ after the early 2010s. Pixel-wise correlations revealed a robust CE–WUE antagonism for all crops (r = −0.33 to −0.60), while CE–yield coupling was crop-dependent (soybean positive, maize weakly negative, rice non-significant). Trend-based coupling further showed that synchronized CE and yield increases dominated 45.7% of croplands, whereas trade-offs were more common when WUE was involved (CE–WUE: 38.0%; WUE–yield: 41.8%), peaking in rice systems (61.8% and 54.0%, respectively). CCD mapping indicated widespread basic coordination but strong crop contrasts. Rice had the lowest coordination (mean CCD = 0.36 ± 0.17) and the largest shares of moderate-to-severe imbalance, identifying rice as the primary FCW bottleneck, whereas maize and soybean more frequently achieved good-to-high coordination. These results support a zoned strategy that consolidates coordinated maize/soybean areas, prioritizes paddy water-saving and low-emission upgrades, and limits further rice expansion in water-constrained zones. Full article

To host the 2022 Winter Olympics, Beijing and Zhangjiakou implemented extensive ecological restoration projects, improving the ecological quality of the region. However, detailed evidence of long-term spatiotemporal dynamics in vegetation productivity remains limited. This study employed the Carnegie–Ames–Stanford Approach (CASA) to estimate the vegetation Net Primary Productivity (NPP) in the Beijing–Zhangjiakou region from 2004 to 2023, utilizing 250 m monthly NDVI data. The 30 m resolution China Land Cover Dataset (CLCD) was incorporated to mask non-vegetated pixels and refine the vegetation mask, reducing mixed-pixel effects. Spatiotemporal variations, seasonal change-point detection, interannual stability, and trend persistence were analyzed across administrative regions and land cover types. Results indicate pronounced spatial heterogeneity in NPP, with persistently high values in forest-dominated western and northern Beijing and northeastern Zhangjiakou, and lower values concentrated in Beijing’s built-up and cropland-dominated southeastern plain. Pixel-level boxplots suggest stronger intra-regional variability in Beijing than in Zhangjiakou. Across landcover types, forests generally maintain the highest NPP, while grasslands are relatively lower. Boxplots further show that shrubs exhibit the highest variability, with all types showing right-skewed distributions. Annual mean NPP increased significantly for the entire region, Beijing, and Zhangjiakou, with interannual increase rates of 3.57, 1.56, and 4.53 gC·m⁻²·yr⁻², respectively; the lowest values occurred in 2007 and the highest in 2022. Trend maps and category statistics consistently suggest that positive trends dominate most of the region and expanded slightly during 2014–2023. BEAST analysis suggests a stable seasonal NPP cycle with no significant seasonal change points. CV-based assessment indicates generally high to extremely high stability, whereas low-stability zones are mainly associated with urban expansion areas, surrounding croplands, and parts of Zhangjiakou grasslands. Hurst results suggest that persistently increasing trends cover more than 90% of the study area, while persistently decreasing trends account for about 5.25% and are primarily linked to Beijing’s expansion zones. Full article

Maximum power point tracking (MPPT) under partial shading is a nonconvex, rapidly varying control problem that challenges multi-agent policies deployed on photovoltaic modules. We present Fuzzy–MAT3D, a fuzzy-augmented multi-agent TD3 (Twin-Delayed Deep Deterministic Policy Gradient) controller trained under centralized training/decentralized execution (CTDE). On the theory side, we prove that differentiable fuzzy partitions of unity endow the actor–critic maps with global Lipschitz regularity, reduce temporal-difference target variance, enlarge the input-to-state stability (ISS) margin, and yield a global $L_{\infty }$$\gamma$-contraction of fixed-policy evaluation (hence, non-expansive with $\kappa =\gamma <1$). We further state a two-time-scale convergence theorem for CTDE-TD3 with fuzzy features; a PL/last-layer-linear corollary implies point convergence and uniqueness of critics. We bound the projected Bellman residual with the correct contraction factor (for $L^{\infty }$ and $L^2\left(\rho \right)$ under measure invariance) and quantified the negative bias induced by $min\{Q_1,Q_2\}$; an N-agent extension is provided. Empirically, a balanced common-random-numbers design across seven scenarios and 20 seeds, analyzed by ANOVA and CRN-paired tests, shows that Fuzzy–MAT3D attains the highest mean MPPT efficiency (92.0% ± 4.0%), outperforming MAT3D and Multi-Agent Deep Deterministic Policy Gradient controller (MADDPG). Overall, fuzzy regularization yields higher efficiency, suppresses steady-state oscillations, and stabilizes learning dynamics, supporting the use of structured, physics-compatible features in multi-agent MPPT controllers. At the level of PV plants, such gains under partial shading translate into higher effective capacity factors and smoother renewable generation without additional hardware. Full article

This study aims to propose a practical and realistic synthetic data generation method for object recognition in hazardous and data-scarce environments, such as construction sites. Artificial intelligence (AI) applications in such dynamic domains require domain-specific datasets, yet collecting real-world data can be challenging due to safety concerns, logistical constraints, and high labor costs. To address these limitations, we introduce object range expansion synthesis (ORES), a lightweight and non-generative method for generating synthetic image data by inserting real object masks into varied background scenes using open datasets. ORES synthesizes new scenes, while preserving scale and ground alignment, enabling controllable and realistic data augmentation. A dataset of 30,000 synthetic images was created using the proposed method and used to train an object recognition model. When tested on real-world construction site images, the model achieved a mean average precision at IoU 0.50 (mAP50) of 98.74% and a recall of 54.55%. While recall indicates room for improvement, the high precision highlights the practical value of synthetic data in enhancing model performance without requiring extensive field data collection. This research contributes a scalable approach to data generation in safety-critical and data-deficient environments, reducing dependence on direct data acquisition, while maintaining model efficacy. It provides a foundation for accelerating the deployment of AI technologies in high-risk industries by overcoming data bottlenecks and supporting real-world applications through practical synthetic augmentation. Full article

This article addresses the convergence of iteration sequences in Cesàro means for asymptotically nonexpansive mappings. Specifically, this study explores the behavior of Kirk iteration in the Cesàro means in the context of uniformly convex and reflexive Banach spaces equipped with uniformly Gâteaux differentiable norms. The focus is to determine the conditions under which the Kirk iteration sequence converges strongly or weakly to a fixed point. Finally, some examples are given in this article to demonstrate the advantages of the preferred iteration method and to verify the results obtained. Full article

The Patagonia Icefield, the largest ice mass in the Southern Hemisphere outside Antarctica, has experienced significant growth and expansion of ice-contact lakes in recent decades, with lake surface water temperature (LSWT) being one of the key influencing factors. LSWT affects glacier melting at the waterline and accelerates glacier mass loss. However, the observations of ice-contact LSWT are often limited to short-term, site-based field measurements, which hinders long-term, whole-lake monitoring. This study examines LSWT for the three largest ice-contact lakes in the Patagonia Icefield—Lake Argentino, Lake Viedma, and Lake O’Higgins, each exceeding 1000 km²—and the three largest nearby non-ice-contact lakes for comparison using MODIS data between 2002 and 2022. In 2022, the mean LSWTs for Lake Argentino, Lake Viedma, and Lake O’Higgins were 7.2, 7.0, and 6.4 °C, respectively. In summer, ice-contact lakes exhibited wider LSWT ranges and more pronounced cooling near glacier termini and warming farther away compared to other seasons, demonstrating glacier melt cooling and its seasonal variability. Over the past 20 years, both Lake Viedma and Lake O’Higgins showed a warming rate of +0.20 °C dec⁻¹, p > 0.1, with slower warming near the glacier, reflecting glacier contact suppression on the LSWT trend. Conversely, Lake Argentino displayed a significant warming rate of +0.43 °C dec⁻¹ (p < 0.05), with faster rates near the glacier terminus, possibly linked to a prolonged and large (>64 km²) iceberg accumulation event from March 2010 to October 2011 in Glacier Upsala’s fjord. Iceberg mapping shows that larger events caused more pronounced short-term (24 days) LSWT cooling in Lake Argentino’s ice-proximal region. This study highlights the role of glacier–lake interactions including calving events in regulating ice-contact lake water temperature. Full article

This paper focuses on one-parameter semigroups of $\rho$-nonexpansive mappings $T_t:C\to C$, where C is a subset of a modular space $X_{\rho }$, the parameter t ranges over $[0,+\infty )$, and $\rho$ is a convex modular with the Fatou property. The common fixed points of such semigroups can be interpreted as stationary points of a dynamic system defined by the semigroup, meaning they remain unchanged during the transformation $T_t$ at any given time t. We demonstrate that, under specific conditions, the sequence $\left\{x_k\right\}$ generated by the implicit iterative process $x_{k+1}=c_k{T}_{t_{k+1}}\left(x_{k+1}\right)+(1-c_k)x_k$ is $\rho$-convergent to a common fixed point of the semigroup. Our findings extend existing convergence results for semigroups of operators, from Banach spaces to a broader class of regular modular spaces. Full article

With the fast expansion of intermittent renewable energy sources in the upcoming smart grids, simple and accurate day-ahead systems for residual load forecasts are urgently needed. Machine learning strategies can facilitate towards drastic cost minimizations in terms of operating-reserves avoidance to compensate the mismatches between the actual and forecasted values. In this study, a multi-input/multi-output model is developed based on artificial neural networks to map the relationship between different predictor inputs, including time indices, weather variables, human activity parameters, and energy price indicators, and target outputs such as wind and photovoltaic generation. While the information flows in only one direction (from the predictor nodes through the hidden layers to the target node), benchmark training algorithms are employed and assessed under different case studies. The model is evaluated under both parametric and non-parametric formulations, namely neural networks and Gaussian process regression. Essential improvements are achieved by enhancing the number of embedded predictors, while superior performance is observed by using Bayesian regularization mechanisms. In terms of mean-error indices and determination coefficient, this opens the pathway towards minimization via Bayesian inference-based approaches in the presence of increased and highly stochastic renewable inputs. Full article

Let C denote a convex subset within the vector space ${𝓁}_{p(·)}$, and let T represent a mapping from C onto itself. Assume $\alpha =({\alpha }_1,\cdots ,{\alpha }_n)$ is a multi-index in ${[0,1]}^n$ such that ${\displaystyle \sum _{i=1}^n}{\alpha }_i=1$, where ${\alpha }_1>0$ and ${\alpha }_n>0$. We define $T_{\alpha }:C\to C$ as $T_{\alpha }={\displaystyle \sum _{i=1}^n}{\alpha }_i{T}^i$, known as the mean average of the mapping T. While every fixed point of T remains fixed for $T_{\alpha }$, the reverse is not always true. This paper examines necessary and sufficient conditions for the existence of fixed points for T, relating them to the existence of fixed points for $T_{\alpha }$ and the behavior of T-orbits of points in T’s domain. The primary approach involves a detailed analysis of recurrent sequences in $\mathbb{R}$. Our focus then shifts to variable exponent modular vector spaces ${𝓁}_{p(·)}$, where we explore the essential conditions that guarantee the existence of fixed points for these mappings. This investigation marks the first instance of such results in this framework. Full article

Picard iteration is on the basis of a great number of numerical methods and applications of mathematics. However, it has been known since the 1950s that this method of fixed-point approximation may not converge in the case of nonexpansive mappings. In this paper, an extension of the concept of nonexpansiveness is presented in the first place. Unlike the classical case, the new maps may be discontinuous, adding an element of generality to the model. Some properties of the set of fixed points of the new maps are studied. Afterwards, two iterative methods of fixed-point approximation are analyzed, in the frameworks of b-metric and Hilbert spaces. In the latter case, it is proved that the symmetrically averaged iterative procedures perform well in the sense of convergence with the least number of operations at each step. As an application, the second part of the article is devoted to the study of fractal mappings on Hilbert spaces defined by means of nonexpansive operators. The paper considers fractal mappings coming from $\phi$-contractions as well. In particular, the new operators are useful for the definition of an extension of the concept of $\alpha$-fractal function, enlarging its scope to more abstract spaces and procedures. The fractal maps studied here have quasi-symmetry, in the sense that their graphs are composed of transformed copies of itself. Full article

Background: The prediction of fluid responsiveness in critical patients helps clinicians in decision making to avoid either under- or overloading of fluid. This study was designed to determine whether lung recruitment maneuver (LRM) would have an effect on the predictability of fluid responsiveness by the changes of hemodynamic parameters in pediatric patients who were receiving lung-protective ventilation and one-lung ventilation (OLV). Methods: A total of 34 children, aged 1–6 years old, scheduled for heart surgeries via right thoracotomy were enrolled. Patients were anesthetized and OLV with lung-protection ventilation settings was established, and then, positioned on left lateral decubitus. LRM and volume expansion (VE) were performed in sequence. Heart rate (HR), systolic arterial pressure (SAP), mean arterial pressure (MAP) diastolic arterial pressure (DAP), stroke volume (SV), stroke volume variation (SVV), and pulse pressure variation (PPV) were recorded via an A-line based monitor system at the following time points: before and after LRM (T1 and T2) and before and after VE (T3 and T4). An increase in stroke volume (SV) or mean arterial pressure (MAP) of ≥10% following fluid loading identified fluid responders. The predictability of fluid responsiveness by the changes of SV (ΔSV_LRM) and MAP (ΔMAP_LRM) after LRM and VE were statistically evaluated by receiver operating characteristic curves [area under the curves (AUC)]. Results: SVs in all patients were significantly decreased after LRM (p < 0.01) and then, increased and returned to baseline after VE (p < 0.01). In total, 16 out of 34 patients who were fluid responders had significantly lower SV after LRM compared to that in fluid non-responders. The area under the receiver operating characteristic curves for ΔSV_LRM was 0.828 (95% confidence interval [CI], 0.660 to 0.935; p < 0.001) and it indicated that ΔSV_LRM was able to predict the fluid responsiveness of pediatric patients. MAPs in all patients were also decreased significantly after LRM, and 12 of them fell into the category of fluid responders after VE. Statistically, ΔMAP_LRM did not predict fluid responsiveness when LRM was considered as an influential factor (p = 0.07). Conclusions: ΔSV_LRM, but not ΔMAP_LRM, showed great reliability in the prediction of the fluid responsiveness following VE in children during one-lung ventilation with lung-protective settings. Trial registration: ChiCTR2300070690. Full article

Deforestation remains one of the key concerning activities around the world due to commodity-driven extraction, agricultural land expansion, and urbanization. The effective and efficient monitoring of national forests using remote sensing technology is important for the early detection and mitigation of deforestation activities. Deep learning techniques have been vastly researched and applied to various remote sensing tasks, whereby fully convolutional neural networks have been commonly studied with various input band combinations for satellite imagery applications, but very little research has focused on deep networks with high-resolution representations, such as HRNet. In this study, an optimal semantic segmentation architecture based on high-resolution feature maps and an attention mechanism is proposed to label each pixel of the satellite imagery input for forest identification. The selected study areas are located in Malaysian rainforests, sampled from 2016, 2018, and 2020, downloaded using Google Earth Pro. Only a two-class problem is considered for this study, which is to classify each pixel either as forest or non-forest. HRNet is chosen as the baseline architecture, in which the hyperparameters are optimized before being embedded with an attention mechanism to help the model to focus on more critical features that are related to the forest. Several variants of the proposed methods are validated on 6120 sliced images, whereby the best performance reaches 85.58% for the mean intersection over union and 92.24% for accuracy. The benchmarking analysis also reveals that the attention-embedded high-resolution architecture outperforms U-Net, SegNet, and FC-DenseNet for both performance metrics. A qualitative analysis between the baseline and attention-based models also shows that fewer false classifications and cleaner prediction outputs can be observed in identifying the forest areas. Full article

Underwater vision-based detection plays an important role in marine resources exploration, marine ecological protection and other fields. Due to the restricted carrier movement and the clustering effect of some marine organisms, the size of some marine organisms in the underwater image is very small, and the samples in the dataset are very unbalanced, which aggravate the difficulty of vision detection of marine organisms. To solve these problems, this study proposes a marine organism detection framework with a dataset augmentation strategy and Convolutional Neural Networks (CNN)-Vision Transformer (ViT) fusion model. The proposed framework adopts two data augmentation methods, namely, random expansion of small objects and non-overlapping filling of scarce samples, to significantly improve the data quality of the dataset. At the same time, the framework takes YOLOv5 as the baseline model, introduces ViT, deformable convolution and trident block in the feature extraction network, and extracts richer features of marine organisms through multi-scale receptive fields with the help of the fusion of CNN and ViT. The experimental results show that, compared with various one-stage detection models, the mean average precision (mAP) of the proposed framework can be improved by 27%. At the same time, it gives consideration to both performance and real-time, so as to achieve high-precision real-time detection of the marine organisms on the underwater mobile platform. Full article