Goat ghee is currently gaining more interest as an important bioactive food having an impact on human health provided by its fatty acid (FA) composition. The objective of this study was to characterize the FA profile of ghee derived from local Arbia goats and assess the effects of feed supplementation with two indigenous arid plants, Artemisia herba-alba (AHA) and Retama raetam (RR), on its nutritional quality. Arbia goats were fed a control diet (n = 5) or diets supplemented with AHA (n = 5) and RR (n = 5). The FA composition was analyzed by gas chromatography-mass spectrometry (GC-MS), and nutritional indices were calculated. The baseline ghee was characterized by a high content of total saturated fatty acids (SFAs) (77.81% +/- 6.64%). AHA supplementation significantly improved the profile by increasing total monounsaturated fatty acids (MUFAs) (21.60 +/- 4.75, p = 0.016) and yielding the lowest thrombogenicity index (TI). Conversely, RR supplementation worsened the profile, causing a significant increase in total SFAs (82.26% +/- 3.82%, p = 0.010) and the highest TI. These findings emphasize that AHA is a promising, sustainable additive for enhancing the FA profile of small ruminant products, highlighting the critical need for screening indigenous plant materials to avoid adverse nutritional outcomes.
In recent years, energy management strategies for fuel cell electric vehicles have received great attention due to the need for efficient power distribution and better fuel economy. This study proposes a hybrid energy storage system composed of a fuel cell as the main source, supported by a lithium-ion battery and a supercapacitor. The main objective is to improve fuel cell efficiency and maintain the DC Bus voltage stability under different driving conditions. A deep neural network-based power management strategy was developed using MATLAB and trained with a large dataset to optimize power sharing between the components. Simulation results show that the proposed method satisfies load demand accurately, reduces fuel cell power fluctuations, and keeps the DC Bus voltage within ±0.61
This letter proposes a hybrid framework that synergistically combines reinforcement learning with particle swarm optimization to automate the architectural design and control optimization of shunt active power filters (SAPFs). By leveraging PSO to autonomously design the neural network architecture of a twin delayed deep deterministic policy gradient (TD3) RL agent, specifically meta-optimizing the neuron count in its hidden layers of the actor network, this approach eliminates manual tuning and enhances adaptability and efficiency. A multiobjective composite reward function is designed to simultaneously minimize DC voltage error, reduce total harmonic distortion, penalize excessive control actions and enhance overall system stability, thereby addressing key performance challenges in three-phase SAPF operation. The simulation results obtained by implementing the proposed approach demonstrated rapid convergence and superior performance, confirming its practical effectiveness in satisfying the dynamic operational requirements of modern power systems.
Arabic is a linguistically rich and morphologically complex language, yet it remains under-resourced, particularly in terms of annotated datasets for subjectivity analysis. Current methods struggle with the fragmentation of available resources and the methodological homogeneity of existing ensembles, which often rely on architecturally similar models. This makes it difficult to create robust, generalized tools. To address this, the present study proposes Dhati+, a novel cross-architectural framework for subjectivity assessment. To address the scarcity of specialized datasets, we constructed a comprehensive dataset, AraDhati+, by integrating and augmenting existing Arabic datasets and collections, including the Arabic Subjectivity/Sentiment Dataset (ASTD), the Large-scale Arabic Book Reviews dataset (LABR), the Hotel Arabic-Reviews Dataset (HARD), and the SANAD dataset. We then fine-tuned state-of-the-art Arabic language models–Cross-lingual Language Model-RoBERTa (XLM-RoBERTa), Arabic Bidirectional Encoder Representations from Transformers (AraBERT), and Arabian Generative Pre-trained Transformer (ArabianGPT)–on the AraDhati+ dataset. Crucially, we implemented a Heterogeneous Ensemble Strategy that fuses the distinct cognitive paradigms of Bidirectional Discriminative Encoders and a Unidirectional Generative Decoder. This approach mitigates individual inductive biases by balancing contextual semantic extraction with causal reasoning. Our proposed approach achieved an impressive accuracy of 97.88
Regulating the formation of rigid zones in viscoplastic fluids is essential for accurate numerical modeling and practical applications. This paper presents a numerical study on the control and minimization of rigid zones in Herschel-Bulkley fluids during flow at the yield stress threshold within a confined square domain. The Papanastasiou regularization method is employed to ensure a smooth transition between fluid and rigid-like zones. This study systematically analyzes the influence of key rheological parameters and the regularization approach on the suppression and reduction of these zones. The influence of the regularization parameter m on rigid zone formation during flow is investigated, along with the effect of the critical shear rate for different values of this parameter. Additionally, the impact of inlet pressure on the rigid zone area is examined, followed by an in-depth analysis of its effect for various critical shear rate values to explore their combined influence. Furthermore, the study explores the influence of the consistency factor and the power-law index on rigid zones during flow. The findings highlight optimal parameter selection strategies to suppress or minimize rigid zones at the yield stress threshold, ensuring improved numerical accuracy in viscoplastic fluid simulations.
