Understanding how species traits evolve in response to environmental pressures remains a central problem in evolutionary biology. We present a computational framework that combines deterministic and stochastic modeling, nonlinear regression, and Bayesian inference to examine trait–environment dynamics in marine and terrestrial taxa. The framework comprises (i) a single toolkit that compares and combines mechanistic (ODE/SDE), signal-processing (Fourier/wavelet), and machine-learning (GPR, NN) approaches within one workflow; (ii) a GP-based surrogate to accelerate ABC while preserving uncertainty quantification; and (iii) a simulation-to-inference pipeline that reports parameter stability across taxa sizes and links phylogenetic structure to trait dynamics. Using ordinary and stochastic differential equations, Gaussian process regression (GPR), and approximate Bayesian computation (ABC), we analyzed empirical data sets from nektonic and carnivorous species. The fitted analyses showed nonlinear relationships. In nekton, longevity and fecundity followed an inverted U shape, with a peak near log fecundity [Formula: see text]. In carnivores, separate polynomial fits placed the peak in trophic niche breadth near log body mass 8 and 8.5, respectively, indicating an optimum at an intermediate trait value. Polynomial regression explained up to 67% of the variance in nekton longevity, compared with 57% for the linear model. GPR produced a fluctuating fit, with prediction uncertainty spanning niche-breadth values from [Formula: see text] to 3.5. Simulations based on Ornstein–Uhlenbeck and Brownian motion processes captured mean-reverting and unbounded dynamics; the trait trajectories included stationary, oscillatory, and divergent behavior. Neural network predictions ranged from [Formula: see text] to 70 despite observed responses near zero, indicating overfitting in these noisy ecological data. ABC and GPR produced similar parameter estimates across taxon sample sizes; for example, [Formula: see text]–[Formula: see text] with a 95% CI of 0.05–0.22. [Formula: see text]Prior studies typically analyze trait–environment relationships with isolated statistical models or single-process dynamics, which limits cross-ecosystem comparison and the treatment of uncertainty. The proposed workflow compares model classes, quantifies predictive and inferential uncertainty, and applies the same analytical structure to disparate taxa. In this way, it connects theoretical models with empirical ecological data and potential conservation applications.
Traffic congestion is a major challenge in urban areas, primarily driven by industrialization, urbanization, and population growth. Governments and transportation agencies worldwide are actively seeking effective solutions to mitigate this issue. This study addresses traffic prediction under mixed traffic conditions by categorizing vehicles, which is essential for understanding and managing heterogeneous traffic flows. We propose a predictive model that forecasts traffic in terms of categorized vehicle speed and volume. The model integrates a Convolutional Long Short-Term Memory (ConvLSTM) neural network with an LSTM-based attention mechanism to enhance spatiotemporal learning capabilities. It is trained on seven multivariate features, including categorized vehicular speeds, time of day, and day of the week, using the same set of features as input for forecasting. The performance of the proposed model is evaluated against various baseline neural network architectures, including Dense, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and ConvLSTM. Experimental results demonstrate that the proposed model outperforms these baselines in terms of prediction accuracy and stability. In particular, compared with the ConvLSTM model, the proposed approach achieves improvements of 48.06% in mean squared error (MSE), 14.40% in mean absolute error (MAE), and 14.51% in mean pinball loss (MPL), demonstrating the effectiveness of incorporating the attention mechanism for improved traffic forecasting under mixed traffic conditions.
A significant problem for modern communication systems is accurate and real-time intrusion detection, which is especially important given the rapid expansion of network infrastructures and the rising sophistication of cyberattacks. A Network-based Intrusion Detection System (NIDS) that can adapt to changing and unbalanced traffic conditions is the focus of this research. In order to improve the performance of intrusion detection, a voting-based ensemble machine learning, the VEML framework that uses the ensemble machine learning mechanism and Brown Bear Optimization (BBO), is used for optimization. The suggested VEML uses AdaBoost and XGBoost and Incremental Support Vector Machine (ISVM) in an ensemble way for classification. After implementing a uniform preprocessing pipeline that includes normalizing the Z-score and Min–Max, we use SMOTE to mitigate class imbalance. In order to decrease dimensionality and increase computing efficiency, feature selection is carried out using an ensemble-based approach that combines DT, RF, and LR. For hyperparameter tweaking that is classifier-specific, BBO is used to provide steady convergence and better generalization. Four benchmark data sets, CIC-IDS2017, CSE-CIC-IDS2018, NSL-KDD, and UNSW-NB15, are used to test the framework. With an F1-score of 99.04% and an accuracy of 99.6% on CIC-IDS2017, the suggested model also manages a precision of 99.0% and a recall of 99.01%. The outcomes prove that the VEML-BBO architecture is an effective, efficient, and practical way to identify intrusions in current networks.
This paper introduces an Artificial Intelligence (AI)-powered, web-based platform designed to enhance health accessibility and facilitate early disease detection, addressing individuals’ reluctance to seek timely medical attention. This system offers quick health insights by analyzing user symptoms and providing predictive feedback, while also assisting medical professionals through automated analysis of medical reports via machine learning models for specific diseases. Built on a modern MERN stack with Python-based AI libraries, the platform utilizes public medical data sets from sources like National Institutes of Health for model training. This paper highlights the transformative potential of integrating AI into digital healthcare, aiming to streamline symptom analysis, improve accessibility, and reduce the burden on medical professionals.
Many nonlinear devices are linked to the power grid as the fast development of the power electronics sector causes serious harmonic pollution of the grid voltage. Active power filter (APF), which can detect the harmonics at the installation location, is the tool used for the harmonic voltage treatment. While considering the surplus of APF active devices to guarantee effective and stable operation of APF, the harmonic sources at each node will change with time and need the injection of fresh harmonic compensation commands. This work intends to offer a dynamic adjustment approach of distributed harmonic compensation based on the computation model of active device margin of APF. First, for certain harmonic combinations, the APF active device loss and injection temperature model is developed to compute in real time whether the harmonic injection command satisfies the active device margin. Furthermore, the adaptive energy valley optimization method is applied to derive new harmonic injection commands for the power grid with harmonic source changes, which on one hand lowers the total voltage harmonic distortion rate of the power grid, and on the other hand satisfies the active device margin to guarantee stable operation of APF.
