The scheme of water resources management is necessary for reducing water scarcity in arid areas and improving water availability in general. However, water leak detection and irrigation scheduling traditional AI models are often computationally intensive and require complex hyperparameter tuning, making them less scalable. This study presents an artificial intelligence-based optimization framework that improves forecasting accuracy, computational speed, and real-time adaptability. The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. Moreover, ontology-based feature structuring enhances interpretability, while hyperparameter tuning (GridSearchCV, Bayesian Optimization) and model compression techniques (pruning, quantization, knowledge distillation) ensure computational efficiency. A large number of experiments on real-world IoT sensor data testify to the effectiveness of the framework. It achieves 0.992 AUC-ROC scores for leak detection, an RMSE of 0.227 hours for irrigation scheduling, and an overall accuracy of 94.8%. Additional performance measures comprise precision (89.0%), recall (95.2%), F1-score (0.92), and inference speed (0.003 ms/sample). Although quantization has reduced the computational overhead, we still see a 13.02% increase in the model size as seen in Experiment 6, leading to a trade-off that needs to be optimized further. This study offers a deployable AI-based model for sustainable water management by tackling the issues of scalability, computational cost, and limitations in benchmark evaluation. By virtue of the empirical validation and comparative analysis of the framework, it has been shown to perform better than the regular methods, proving that the methodology can act as a step forward in the field of real-time, AI-assisted irrigation and leak detection systems.

Mathematical modeling of epidemics is a cornerstone in the study and response to the spread of diseases and related processes across various domains. However, classical models generally do not describe such memory effects properly and are computationally inefficient, which restricts their applicability or predictive accuracy. To address these issues, we introduce a new approach to epidemic modeling using our newly proposed fractional-order differential equations, which are endowed with the Atangana–Baleanu system to describe long-range dependencies and nonlinear characteristics more accurately than the traditional Caputo system. To address this, we develop physics-informed neural networks and Fourier-based artificial intelligence-driven surrogate solvers, which are computationally efficient without compromising accuracy. To actuate intervention policies in a dynamic fashion, we also incorporate a hybrid control mechanism integrating the use of reinforcement learning with classical mathematical optimization to facilitate adaptive policymaking that benefits from data. Unlike existing work, our framework is rigorously evaluated on real-world epidemiological datasets from the World Health Organization and the Centers for Disease Control and Prevention, and tested extensively for out of- the-box adaptability to cybersecurity (cyber malware), social rumor, and financial contagion problems. We also propose a data-free generative model (Fair4Free) that improves fairness, privacy, and utility in synthetic dataset generation, allowing its use even for constrained-data settings. Experimental evidence indicates that our holistic approach enhances the accuracy of predictive performance compared to baselines, with both lower computational cost and cross-domain generalizability to unprecedented settings. Finally, we set a new state-of-the-art for EpiModel by end-to-end training on fair data.

The increasing complexity of disability-related healthcare conditions requires intelligent systems capable of integrating heterogeneous physiological, behavioral, nutritional, demographic, and functional health information under evolving real-world conditions. Existing healthcare artificial intelligence systems frequently rely on centralized black-box architectures that suffer from limited interpretability, unstable generalization under temporal drift, and weak deployment scalability in resource-constrained environments. This study proposes a lightweight organizational healthcare intelligence framework for multidomain disability risk prediction using harmonized National Health and Nutrition Examination Survey (NHANES) data. Instead of monolithic prediction, the proposed framework organizes lightweight specialist learners into cooperative teams representing behavioral, physiological, nutritional–metabolic, demographic–fairness, and functional–disability domains. Adaptive coordination among specialist teams is achieved through Nash-cooperative optimization integrated with swarm-based strategies including Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony Optimization, and Harris Hawks Optimization. The framework additionally incorporates explainable specialist reasoning, lightweight edge-oriented inference, and temporal robustness evaluation under heterogeneous healthcare distributions. Experimental evaluation using pooled NHANES 2011-2018 data achieved a 73.90% classification accuracy, a weighted F1-score of 68.03%, a Matthews correlation coefficient of 0.6228, and a multiclass area under the receiver operating characteristic curve of 0.8905. Explainability analysis demonstrated heterogeneous specialist domain contributions, with physiological reasoning achieving the strongest independent predictive capability while demographic–fairness specialization preserved cooperative organizational stability. Swarm-adaptive coordination further improved balanced optimization behavior while maintaining lightweight inference latency near 0.01 ms/sample. External later-cycle evaluation under temporally shifted NHANES environments demonstrated preservation of higher-level disability risk organization despite substantial distribution drift. The proposed framework provides a scalable, interpretable, lightweight, and temporally adaptive organizational intelligence approach for disability-aware healthcare analytics.

In Wuhan City, China was found a new type of viral infection could be a novel CoV strain (2019-nCoV) which as an acute respiratory syndrome CoV-2 (SARS-CoV-2) and formerly, severe acute respiratory syndrome (SARS). All these emerging infectious diseases are caused great damage to general health.New Coronavirus has diffused very quickly at the world and the World Health Organization has declared it a pandemic. It usually affects people who are immunocompromised. Immunodeficiency often comes from nutritional deficiency and this makes viruses stronger. Therefore, this review focused on the identification of the SARS-CoV-2 virus, its transmission and spread, and the history of Coronavirus. In addition, how to boost immunity by learn about nutrition recommendations and healthy diets during the COVID-19 pandemic.

Variation in gaze behavior due to age is currently a considerable challenge in building reliable eye-tracking systems for Autism Spectrum Disorder (ASD) diagnosis. However, existing strategies often focus on static gaze representation or dataset-based information, which can lead to limited generalization of findings depending on developmental groups and heterogeneous recording conditions. In this paper, we present a temporal transformer-based system for ASD classification using eye-tracking sequences. This allows you to model gaze behavior as a structured temporal process in the context of contextual attention, as well as employing entropy-based modeling for various distributions of variability over time and temporal consistency constraints to capture sequential gaze dynamics related to ASD behavioral patterns. The framework was evaluated using public eye-tracking corpus containing temporally ordered gaze recordings from ASD and TD participants across age groups. Five sequential experiments on baseline classification, class-balancing analysis, cross-age evaluation, ablation analysis, and cross-dataset transfer learning were performed to conduct experiment-based evaluations. Model performed 0.91 in in-domain Area Under the Receiver Operating Characteristic Curve (AUC) and 0.81 in F1-score on the primary eye-tracking dataset. In the cross-dataset assessment stage, the framework presented a relatively stable performance, with an AUC of 0.85 and an average F1-score of 0.74, irrespective of differences in participant distributions and recording conditions. Ablation analysis also revealed that entropy regularization and temporal consistency mechanisms played a significant role in model stability and classification performance. The ablation analysis provides additional insight into the contribution of the proposed framework components beyond the overall classification performance. Removing the entropy-based regularization reduced the model’s ability to represent variability in gaze allocation, whereas removing the temporal-consistency regularization resulted in less stable sequence representations during learning. These observations indicate that the proposed components complement the transformer-based sequence encoder by improving representation stability and preserving diagnostically relevant temporal information. Rather than acting as independent classifiers, the regularization mechanisms serve as supporting constraints that enhance the quality and robustness of the learned temporal representations. The results indicate that temporally structured gaze modeling is more robust, interpretable, and general in comparison to static gaze representations. In summary, the presented framework can represent a scalable and developmentally appropriate approach to gaze-based ASD classification and support the implementation of trusted neurodevelopmental screening systems.

Kiwi fruit (Actinidia deliciosa) and star anise (Illicium verum) are the richest sources of minerals and vitamins. Fractionation of phenolic compounds by HPLC for kiwi fruit showed that the main compounds in the methanol extract were syringic acid, chrysin, and quercetin. Star anise had the highest concentrations of syringic acid, quercetin, and gallic acid, respectively. Mice in biological experiments were divided into six groups. Group I was the negative control, while Group II was the positive control, and they were both injected with EAC. Group III was injected with EAC and the chemical drug cisplatin. Groups IV, V, and VI mice were injected with EAC separately and treated with kiwifruit juice, star anise, and a mixture of kiwifruit juice and star anise. The results showed that cell viability was best in kiwi fruit, followed by the mixture and star anise. In contrast, the treatment with cisplatin as chemotherapy for EAC resulted in lower cell viability compared to the different juices. Mice in Groups IV, V, and VI, as well as those treated with cisplatin, were observed to have an increased expansion of myeloid cells (CD11b+LY6G+). The results from flow cytometry indicated enhanced levels of CD69 and CD62L, in addition to CD4+ and CD8+ T cells in the various experimental mice groups. It can be concluded that kiwi fruit and star anise have high nutritional value and the highest concentrations of phenolic compounds. These characteristics contribute to a high activity of natural antioxidants and medicinal properties.