Search Results (275)

This study provides the first comprehensive structural and diversity assessment of the Stanului Forest ecosystem since the last taxonomic survey conducted in 1987. The paper analyses the structural indices of biocoenosis and vegetation diversity in the Stanului Forest ecosystem (Gemărtălui Valley), in the hilly area of Oltenia (Romania), in Dolj County. Primary data were obtained using randomised sampling with frame squares, which ensured the random selection of sampling units and standardisation of the data collection process. The number of samples was equal (10) across all vegetation categories studied. The sampling area was 1 m² for herbaceous species and 100 m² for woody species. Structural indices (frequency, constancy, dominance index, index of relative significance, etc.), diversity indices (Shannon–Wiener, Gleason, Simpson), and statistical analyses were used to interpret the data. The results showed that from a taxonomic perspective, 5 families of woody species and 15 families of herbaceous species were identified. The presence of the invasive species (Robinia pseudoacacia) can negatively influence the ecological functions of the existing plant community. The species Quercus frainetto and Carpinus betulus accounted for 55.16% of the woody layer dominance. In the herbaceous layer, Carex sylvatica and Schedonorus giganteus recorded the highest dominance values. The Shannon–Wiener diversity index was 0.53 in the woody layer and 0.50 in the herbaceous layer. Full article

In this work, we consider a simple bistable motif constituted by a self-enhancing Transcription Factor (TF) and its mRNA with non-instantaneous dynamics. In particular, we mainly numerically investigated the impact of bounded stochastic perturbations of Sine–Wiener type affecting the degradation rate/binding rate constant of the TF on the phase-like transitions of the system. We show that the intrinsic exponential delay in the TF positive feedback, due to the presence of a mRNA with slow dynamics, deeply affects the above-mentioned transitions for long but finite times. We also show that, in the case of more complex delays in the feedback and/or in the translation process, the impact of the extrinsic stochasticity is further amplified. We also briefly investigate the power-law behavior (PLB) of the averaged energy spectrum of the TF by showing that, in some cases, the PLB is simply due to the filtering nature of the motif. A similar analysis can also be applied to biological models having a qualitatively similar structure, such as the well-known Capasso and Paveri–Fontana model of cholera spreading. Full article

Environmental changes and anthropogenic pressures significantly influence both the tree layer and natural regeneration within forest ecosystems. Protected areas represent essential territories for the maintenance and conservation of species within forest communities. In this context, the present study aims to develop a methodological framework for the integrated application of diversity, evenness, and dominance indices in the study of forest plant communities. Analyses were conducted at both α- and β-diversity levels, providing a methodological basis for characterizing local diversity and community differentiation. Species diversity was estimated using the Shannon–Wiener (H′) and Simpson (D) indices, while evenness and dominance were assessed using the Pielou (J′) and Berger–Parker (d) indices. Differences among communities were quantified using the Bray–Curtis dissimilarity index and its components, turnover and nestedness, and structural convergence of forest communities was analyzed through the ICF. The results indicate that α-diversity, estimated by H′, ranges from low to moderate, suggesting a relatively uniform distribution of species abundance. In certain microhabitats, processes of diversification and oligodominance are observed. At the β-diversity level, the analyzed communities are characterized by high dissimilarity, mainly driven by species turnover and, to a lesser extent, by nestedness associated with species loss. The ICF highlights that these forest communities exhibit relatively high structural uniformity, characteristic of mature stands in ecological equilibrium. Full article

Water resource monitoring can provide beneficial information supporting water management; however, present operational systems are small and provide only a subset of the information needed. Primary advancements consist of the clear explanation of water redistribution and water use from groundwater and river schemes, achieving better spatial detail and increased precision as evaluated against hydrometric observation. In such cases, Earth Observation (EO) satellite systems are persistently creating extensive data, which is now essential for applications in different fields. With readily available open-source satellite imagery, aerial remote sensing is progressively becoming a quick and efficient tool for monitoring land and water resource development actions, demonstrating time and cost savings. At present, the deep learning (DL) model will be beneficial for monitoring water resources and EO utilizing remote sensing. In this paper, a Deep Neural Network-Based Object Detection for Water Resource Monitoring and Earth Observation (DNNOD-WRMEO) model is introduced. The main intention is to develop an effective monitoring and analysis framework for water resources and Earth surface observations using aerial remote sensing images. Initially, the Wiener filter (WF) model was used for image pre-processing. For object detection, the Yolov12 method was used for identifying, locating, and classifying objects within an image, followed by the DNNOD-WRMEO methodology, which implements the ResNet-CapsNet model for the backbone feature extraction method. Finally, the temporal convolutional network (TCN) model was implemented for the classification of water resources. The comparison analysis of the DNNOD-WRMEO methodology exhibited a superior accuracy value of 98.61% compared with existing models under the AIWR dataset. Full article

Montane forests, characterized by complex terrain and diverse climates, serve as critical global biodiversity hotspots, particularly for birds and mammals. However, the patterns and underlying processes of bird and mammal diversity remain insufficiently studied in the montane forests of eastern China. This study employed infrared-triggered camera trapping to conduct a four-year field monitoring of birds and mammals, analyzing the effects of plant diversity and seasonal variations on the diversity of habitat-associated animals. Our results revealed that species-level habitat visit frequency in ground-dwelling birds exhibited a significant phylogenetic signal, particularly in spring and summer. Plant diversity metrics demonstrated significant positive correlations with corresponding bird metrics of species richness (SR), phylogenetic diversity (PD), and the standardized effect size of PD (Phylo SES PD). In contrast, for mammals, plant diversity metrics were significantly positively correlated with corresponding SR, mean pairwise phylogenetic distance (Phylo MPD), and mean nearest phylogenetic taxon distance (Phylo MNTD), as well as community structure metrics, including the net relatedness index (Phylo NRI) and nearest taxon index (Phylo NTI). Furthermore, the plant Shannon–Wiener index showed significant positive correlations with both bird and mammal metrics of SR, PD, and Phylo SES PD but significant negative correlations with Phylo MNTD. Seasonal variations triggered the mean altitudinal migration in ground-dwelling birds and mammals. There were significant differences in the diversity and community structure metrics of birds (Shannon–Wiener, Funct FNND, and PD) and mammals (Shannon–Wiener, Funct MPD, Funct FNND, PD, Phylo MPD, Phylo MNTD, and Phylo SES PD), which varied across different seasons. These findings emphasize that plant diversity and seasonal changes are closely related to the diversity and community structure of birds and mammals. They provide theoretical support for the role of habitat vegetation and seasonal dynamics in maintaining the stability and functioning of montane animal ecosystems, offering important insights for addressing habitat fragmentation and species migratory behavior. Full article

Our new Theoretically Dynamic Regression (TDR) modeling methodology was recently applied in three types of real data modeling cases using physically based dynamic model structures with low-order linear regression static functions. Two of the modeling cases achieved the validation set modeling goal of $r_{fit,val} \ge 0.9$. However, the third case, consisting of eleven (11) type one (1) sensor glucose data sets, and thus, eleven individual models, all fail considerably short of this modeling goal and the average $r_{fit,val}$, ${\overline{r}}_{fit,val}$ = 0.68. For this case, the dynamic forms are highly complex 60 min forecast, second-order-plus-dead-time-plus-lead (SOPDTPL) structures, and the static form is a twelve (12) input first-order linear regression structure. Using these dynamic structure results, the objective is to significantly increase $r_{fit}$ for each of the eleven (11) modeling cases using the recently developed Wiener-Physically-Informed-Neural-Network (W-PINN) approach as the static modeling structure. Two W-PINN stage-two static structures are evaluated–one developed using the JMP® Pro Version 16, Artificial Neural Network (ANN) toolbox and the other developed using a novel ANN methodology coded in Python version, 3.12.3. The JMP ${\overline{r}}_{fit,val}$ = 0.74 with a maximum of 0.84. The Python ${\overline{r}}_{fit,val}$ = 0.82 with a maximum of 0.93. Incorporating bias correction, using current and past SGC residuals, the Python estimator improved the average ${\overline{r}}_{fit,val}$ from 0.82 to 0.87 with the maximum still 0.93. Full article

Drivetrain systems operate under varying operating conditions (OCs), which often obscure early-stage fault signatures and hinder robust condition monitoring (CM). This work introduces an AI digital platform developed during the EEDRIVEN project, featuring a holistic CM framework that integrates statistical time series methods—using Generalized AutoRegressive (GAR) models in a multiple model fault diagnosis scheme—with deep learning approaches, including autoencoders and convolutional neural networks, enhanced through a dedicated decision fusion methodology. The platform addresses all key CM tasks, including fault detection, fault type identification, fault severity characterization, and remaining useful life (RUL) estimation, which is performed using a dynamics-informed health indicator derived from GAR parameters and a simple linear Wiener process model. Training for the platform relies on a limited set of experimental vibration signals from the physical drivetrain, augmented with high-fidelity multibody dynamics simulations and surrogate-model realizations to ensure coverage of the full space of OCs and fault scenarios. Its performance is validated on hundreds of inspection experiments using confusion matrices, ROC curves, and metric-based plots, while the decision fusion scheme significantly strengthens diagnostic reliability across the CM stages. The results demonstrate near-perfect fault detection (99.8%), 97.8% accuracy in fault type identification, and over 96% in severity characterization. Moreover, the method yields reliable early-stage RUL estimates for the outer gear of the drivetrain, with normalized errors < 20% and consistently narrow confidence bounds, which confirms the platform’s robustness and practicality for real-world drivetrain systems monitoring. Full article

Crabapples (Malus spp.) are important ornamental trees in northern temperate regions. However, their phenotypic diversity and ornamental values remain poorly characterized, due to a lack of systematic comparison between introduced and domestically bred cultivars/lines. This knowledge gap limits the effective utilization of their germplasm. In this study, 111 floral, foliar, fruit, and tree architectural traits were measured across 93 introduced (North American) and 118 domestically bred (Chinese) cultivars/lines. Comparative analyses using Shannon–Wiener (H′) and Pielou’s evenness (J) indices revealed that floral traits exhibited the highest phenotypic diversity, followed by fruits, leaves, and tree architecture. Among these, 51 key traits (e.g., budlet color, leaf area, and fruit shape) showed above-average diversity, while others (e.g., flower type, leaf cracking, and exocarp color) were less uniform, indicating rare phenotypes. Domestically bred cultivars showed significant improvements in flower color and type, mature leaf shape and size, and fruit characteristics, including novel budlet, bud and petal colors, increased stamen numbers, semi-double or double flowers, and diverse fruit colors. A multi-dimensional ornamental evaluation (Analytic Hierarchy Process) identified 26 superior genotypes and several organ-specific selections for flower- (26), fruit- (25), foliage- (21), and tree architecture-viewing (14) purposes. These findings provide a theoretical basis for updating Malus distinctness, uniformity, and stability (DUS) guidelines, targeted breeding, and strategic landscape applications, highlighting the potential of both introduced and domestic germplasm for ornamental improvement. Full article

In this paper, we investigate the Cauchy problem for the coupled generalized Korteweg–de Vries system driven by a cylindrical Wiener process. We prove local well-posedness for data in $H^s\times H^s,$ with $s>{\displaystyle \frac{1}{2}}$. The key methods that we used in this paper are multilinear estimates in Bourgain spaces, the Itô formula, and a fixed-point argument. Full article

We consider a Student process based on independent copies of a random variable X. If X is in the domain of attraction of the normal law (DAN), a weighted version of the Student process is known to follow a functional Central Limit Theorem (FCLT). Accordingly, appropriate functionals of such a process converge in distribution to the same functionals of the similarly weighted standard Wiener process. We use such a convergence for an integral functional and derive asymptotic confidence intervals (CIs) for the mean of X. For right-skewed distributions of X in DAN, we show that the obtained CIs have higher finite-sample coverage probabilities than, and may be preferred over, a CI $I_1$ of the same asymptotic confidence level $1-\alpha$ that is based on the CLT for the Student t-statistic, since the finite-sample coverage probabilities of the latter CI may be lower than $1-\alpha$. Moreover, for such distributions, the finite-sample coverage probabilities of our best two CIs are also higher than those of their respective equal-expected-length $I_1$ counterparts. Full article

Lithium-ion batteries serve as critical energy storage units for electric vehicles, unmanned aerial vehicles, and other emerging transportation systems. Numerous real-world incidents have demonstrated that thermal runaway (TR) remains a predominant cause of spontaneous combustion in these applications. Concerns over TR risks have significantly hindered broader adoption of lithium-ion batteries. While existing research predominantly focuses on battery heat generation mechanisms, TR initiation processes, and advanced materials with enhanced safety, limited attention has been paid to TR risk evolution induced by cycle-induced performance degradation. To address this gap, this study proposes a data-driven prognostic framework for quantifying TR risks under battery aging scenarios. Leveraging the Open Access XJTU Battery Dataset, we first identify eight degradation-sensitive parameters (including mean current, current standard deviation, and charging time, etc.) by analyzing temporal degradation patterns within characteristic segments of charging curves. These parameters are then fused into a composite degradation index through Physics-Informed Neural Networks (PINNs). Recognizing the stochastic nature of both degradation trajectories and TR-triggering stresses, a Wiener process-based random failure threshold model is developed to probabilistically predict TR risks under time-varying operational conditions. The proposed methodology enables quantitative risk assessment throughout battery service life, offering a novel perspective for aging-aware battery safety management. Full article

This paper aims to improve the accuracy of reliability estimates and the failure time prediction for products exhibiting nonlinear degradation behavior under constant-stress accelerated degradation test (CSADT). To achieve this, a novel degradation model and a life prediction method are proposed, which are based on a generalized Wiener process. Some models assume that the drift coefficients are related to accelerated stress. However, in certain applications, the diffusion coefficients are also affected by accelerated stress. The relationship between the drift parameter and accelerated stress variables can be derived by the accelerated model, and so is the relationship between the diffusion parameter and stress variables based on the principle of invariance of the acceleration factor. To account for individual variability among products, random effects are introduced. Model parameters are estimated using a combination of maximum likelihood estimation (MLE) and the expectation-maximization (EM) algorithm. Furthermore, the probability density function (PDF) of the remaining useful life under normal stress conditions is derived using the law of total probability. The effectiveness and applicability of the proposed approach are validated using simulated constant stress accelerated degradation data and stress relaxation data. The results demonstrate that the model not only fits the degradation process well but also modestly improves the accuracy of the failure time prediction, providing valuable guidance for engineering maintenance and reliability management. Full article

Extreme droughts caused by current climate changes affect the diversity, composition and function of fish communities in lake ecosystems. Poyang Lake is the largest freshwater lake in China, and it is home to many important avian, fish and aquatic mammals. In 2022, Poyang Lake experienced one of the most severe droughts in recorded history. Understanding how fish communities responded to this event can offer key knowledge in developing strategies for coping with future climatic extremes, particularly given that the local government has been actively posting several middle- to long-term policies on managing the fish diversity of Poyang Lake, including fishery resource supplements and the well-known “ten-year fishing ban”. To understand how the fish diversity of Poyang Lake has been altered by climate change and human interventions, here, we analyzed the α- and β-taxonomic diversity (TD) and functional diversity (FD) of fish species using environmental DNA (eDNA), and we compared the fish diversity and community changes before and after the 2022 drought. In total, 77 native fish species and 4 invasive species were detected. The species richness and Shannon–Wiener index decreased significantly, and Simpson’s index had no significant difference post-drought. Rao’s Quadratic Entropy (Rao’sQE) index increased significantly, and the Functional Evenness (FEve) index decreased significantly. The differences in α- and β-TD and FD in the north part and south part of Poyang Lake also reflect the impact of drought. When calculating biodiversity contribution rates of the different species, we found that small-sized species were dominant pre-drought, while medium- and large-sized species were predominant post-drought. These patterns indicate that the fish community of Poyang Lake is undergoing a reestablishing process after the extreme drought. This fish community reestablishment post-drought does not correspond to the natural process of community recovery; instead, it is the result of human intervention while being affected by drought brought about by climate change. Full article

In consideration of the characteristics of two-stage (stable and degraded), nonlinearity and non-stationary randomness in the full life-cycle evolution process of the rolling bearing health indicator (HI), a novel remaining useful life (RUL) prediction method for rolling bearings is proposed based on long short-term memory network–Mahalanobis distance (LSTM-MD) and an incremental unscented Kalman filter (IUKF). First, an LSTM-MD hybrid algorithm is developed to precisely identify the critical change point (CP) between stable operation and incipient degradation in bearing HI trajectories, effectively mitigating the susceptibility of conventional threshold-based methods to HI fluctuations. Second, during the degradation stage, a degradation analysis model based on the nonlinear Wiener process is constructed. Simultaneously, an IUKF-based RUL prediction method for bearings is proposed, which overcomes the implicit assumption of the traditional UKF method that one-step prediction can replace state prediction, particularly in scenarios with significant HI fluctuations, thereby significantly reducing prediction errors. Finally, the proposed method is validated through comparisons with traditional methods using both the XJTU-SY public dataset and a self-built bearing test dataset. The results demonstrate that compared to traditional methods, the accuracy of initial degradation change point identification is improved by 32.6%, and the root mean square error (MSE) of RUL prediction is decreased by 41.8%. Full article

We study stochastic variants of the Kairat-II and Kairat-X equations in (3 + 1) dimensions, two canonical models in soliton theory. Random fluctuations are incorporated through a Wiener process, yielding a multiplicative stochastic embedding of the wave fields. By combining the enhanced direct algebraic technique with the new projective Riccati equation approach, we obtain closed-form stochastic soliton solutions and analyze how noise modulates their amplitude and localization. The solutions are illustrated with consistent 3D surface plots (mean field vs. sample paths) and 2D time traces to highlight wave geometry and variability. In addition, we employ the energy balance approach to separate kinetic and potential contributions and to verify an energy balance relation for the derived solutions, thereby clarifying their physical plausibility and stability under noise. The results provide exact, easily verifiable benchmarks for stochastic nonlinear wave models and a practical template for incorporating randomness into nonlinear dispersive systems. Full article