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The main aim of this investigation is to replicate and enhance a sustainable hybrid energy structure that combines solar photovoltaic, wind turbines, battery storage. The study employs the Homer simulation model to evaluate the scaling, cost, and control strategy of this hybrid power system. This work primarily focuses on determining the most efficient design for a renewable energy generation system architecture for a significant electric vehicle charging station. The hybrid power system is designed to meet an AC base load of 2424.25 kWh/day with peak consumption of 444 kW. The simulation results indicate that the optimized components and the cost of energy are at an optimal level and the optimal design in terms of renewable energy penetration.

Despite recent advancements in Instruct-based Image Editing models for generating high-quality images, they are known as black boxes and a significant barrier to transparency and user trust. To solve this issue, we introduce SMILE (Statistical Model-agnostic Interpretability with Local Explanations), a novel model-agnostic for localized interpretability that provides a visual heatmap to clarify the textual elements' influence on image-generating models. We applied our method to various Instruction-based Image Editing models like Pix2Pix, Image2Image-turbo and Diffusers-Inpaint and showed how our model can improve interpretability and reliability. Also, we use stability, accuracy, fidelity, and consistency metrics to evaluate our method. These findings indicate the exciting potential of model-agnostic interpretability for reliability and trustworthiness in critical applications such as healthcare and autonomous driving while encouraging additional investigation into the significance of interpretability in enhancing dependable image editing models.

Sparse principal component analysis (sPCA) enhances the interpretability of principal components (PCs) by imposing sparsity constraints on loading vectors (LVs). However, when used as a precursor to independent component analysis (ICA) for blind source separation (BSS), sPCA may underperform due to its focus on simplicity, potentially disregarding some statistical information essential for effective ICA. To overcome this limitation, a sophisticated approach is proposed that preserves the interpretability advantages of sPCA while significantly enhancing its source extraction capabilities. This consists of two tailored algorithms, dissociative PCA (DPCA1 and DPCA2), which employ adaptive and firm thresholding alongside gradient and coordinate descent approaches to optimize the proposed model dynamically. These algorithms integrate left and right singular vectors from singular value decomposition (SVD) through dissociation matrices (DMs) that replace traditional singular values, thus capturing latent interdependencies effectively to model complex source relationships. This leads to refined PCs and LVs that more accurately represent the underlying data structure. The proposed approach avoids focusing on individual eigenvectors, instead, it collaboratively combines multiple eigenvectors to disentangle interdependencies within each SVD variate. The superior performance of the proposed DPCA algorithms is demonstrated across four varied imaging applications including functional magnetic resonance imaging (fMRI) source retrieval, foreground-background separation, image reconstruction, and image inpainting. They outperformed traditional methods such as PCA+ICA, PPCA+ICA, SPCA+ICA, PMD, and GPower., Comment: 1. 13 pages with 6 figures, this work has not bee published before. 2. The paper is yet to be peer-reviewed and I am planning to submit it to IEEE Transactions on Image Processing. 3. There is no supplementary material. 4. There is no funding for this work as of now

Inspired by the natural motion of insects, fish, and other animals, flapping airfoils have gained significant importance due to their applications in fields such as ship propulsion, micro aerial vehicles, and autonomous underwater vehicles. Over the past two decades, extensive research has focused on understanding the dynamics of these airfoils, their thrust production capabilities, and methods to enhance this thrust in unsteady flows. This study investigates how the presence of a cylinder oscillating due to incoming flow affects the thrust performance of the flapping airfoil. The results indicate that the flapping airfoil generates increased thrust when placed in the wake of the oscillating cylinder compared to a scenario without the cylinder's wake. A direct relationship has been found between the strouhal number ($St$) of the flapping airfoil and its pitching amplitude, which significantly influences the airfoil's performance. This analysis highlights the potential for optimizing flapping airfoil efficiency through strategic selection of flapping parameters and the placement of an oscillating cylinder.

In this paper, we examine the coupling between odor dynamics and vortex dynamics around undulating bodies, with a focus on bio-inspired propulsion mechanisms. Utilizing computational fluid dynamics (CFD) simulations with an in-house Immersed-Boundary Method (IBM) solver, we investigate how different waveform patterns, specifically carangiform and anguilliform, influence the dispersion of chemical cues in both water and air environments. Our findings reveal that vortex dynamics significantly impact the overall trajectory of odor spots, although the alignment between odor spots and coherent flow structures is not always precise. We also evaluate the relative contributions of diffusion and convection in odor transport, showing that convection dominates in water, driven by higher Schmidt numbers, while diffusion plays a more prominent role alongside convection in air. Additionally, the anguilliform waveform generally produces stronger and farther-reaching chemical cues compared to carangiform swimmers. The critical roles of Strouhal number and Reynolds number in determining the efficiency of odor dispersion are also explained, offering insights that could enhance the design of more efficient, adaptive, and intelligent autonomous underwater vehicles (AUVs) by integrating sensory and hydrodynamic principles inspired by fish locomotion.

We propose a model order reduction approach to speed up the computation of seismograms, i.e. the solution of the seismic wave equation evaluated at a receiver location, for different model parameters. Our approach achieves a reduction of the unknowns by a factor of approximately 1000 for various numerical experiments for a 2D subsurface model of Groningen, the Netherlands, even if the wave speeds of the subsurface are relatively varied. Moreover, using parallel computing, the reduced model can approximate the (time domain) seismogram in a lower wall clock time than an implicit Newmark-beta method. To realize this reduction, we exploit the fact that seismograms are low-pass filtered for the observed seismic events by considering the Laplace-transformed problem in frequency domain. Therefore, we can avoid the high frequencies that would require many reduced basis functions to reach the desired accuracy and generally make the reduced order approximation of wave problems challenging. Instead, we can prove for our ansatz that for a fixed subsurface model the reduced order approximation converges exponentially fast in the frequency range of interest in the Laplace domain. We build the reduced model from solutions of the Laplace-transformed problem via a (Proper Orthogonal Decomposition-)Greedy algorithm targeting the construction of the reduced model to the time domain seismograms; the latter is achieved by using an a posteriori error estimator that does not require computing any time domain counterparts. Finally, we show that we obtain a stable reduced model thus overcoming the challenge that standard model reduction approaches do not necessarily yield a stable reduced model for wave problems., Comment: 26 pages, plus supplementary material

We present the findings of an experimental study whereby we correlate the changes in the morphology of the photoplethysmography (PPG) signal to healthy aging. Under this pretext, we estimate the biological age of a person as well as the age group he/she belongs to, using the PPG data that we collect via a non-invasive low-cost MAX30102 PPG sensor. Specifically, we collect raw infrared PPG data from the finger-tip of 179 apparently healthy subjects, aged 3-65 years. In addition, we record the following metadata of each subject: age, gender, height, weight, family history of cardiac disease, smoking history, vitals (heart rate and SpO2). We pre-process the raw PPG data to remove noise, artifacts, and baseline wander. We then construct 60 features based upon the first four PPG derivatives, the so-called VPG, APG, JPG, and SPG signals, and the demographic features. We then do correlation-based feature-ranking (which retains 26 most important features), followed by Gaussian noise-based data augmentation (which results in 15-fold increase in the size of our dataset). Finally, we feed the feature set to three machine learning classifiers (logistic regression, decision tree, random forest), and two shallow neural networks: a feedforward neural network (FFNN) and a convolutional neural network (CNN). For the age group classification, the shallow FFNN performs the best with 98% accuracy for binary classification (3-15 years vs. 15+ years), and 97% accuracy for three-class classification (3-12 years, 13-30 years, 30+ years). For biological age prediction, the shallow FFNN again performs the best with a mean absolute error (MAE) of 1.64., Comment: 8 pages, 5 figures, 6 tables, submitted to a journal for review

The ability of Deep Learning to process and extract relevant information in complex brain dynamics from raw EEG data has been demonstrated in various recent works. Deep learning models, however, have also been shown to perform best on large corpora of data. When processing EEG, a natural approach is to combine EEG datasets from different experiments to train large deep-learning models. However, most EEG experiments use custom channel montages, requiring the data to be transformed into a common space. Previous methods have used the raw EEG signal to extract features of interest and focused on using a common feature space across EEG datasets. While this is a sensible approach, it underexploits the potential richness of EEG raw data. Here, we explore using spatial attention applied to EEG electrode coordinates to perform channel harmonization of raw EEG data, allowing us to train deep learning on EEG data using different montages. We test this model on a gender classification task. We first show that spatial attention increases model performance. Then, we show that a deep learning model trained on data using different channel montages performs significantly better than deep learning models trained on fixed 23- and 128-channel data montages.

To investigate1,1-Diphenyl-2-picrylhydrazyl (DPPH) and superoxide radical (SOR) 17 scavenging activities of 2-oxo-1,2,3,4-tetrahydropyrimidines derivatives. Free radicals are 18 highly unstable and reactive molecules/atoms. In the body, free radicals form during 19 normal and abnormal metabolism in the body and cause serious damage to other 20 biomolecules through generating oxidative stress (OS). If free radicals induced OS does 21 not neutralized properly, host multiple pathologies including several types of cancers., Comment: 11 pages,1 scheme, 1 table