University of Sharjah

Abstract We present the habitability and stability of the proposed planets around the young stellar system HD 21841 based on the estimated astrophysical parameters of its components. Our study utilizes an orbital analysis of the system, employing Tokovinin's Method and incorporating 12 new speckle interferometric measurements that collectively span approximately 90 degrees of the orbit. Additionally, we use Al-Wardat's method for analyzing binary and multiple stellar systems in conjunction with Gaia DR3 astrometry and spectral data for a comprehensive spectrophotometric assessment of the components. This approach utilizes Kurucz’s model stellar atmospheres and available photometric data to develop synthetic spectral energy distributions (SEDs) for each component and subsequently the entire system. We can identify the physical parameters and the optimal solution by comparing the synthetic SED of the entire system with the observational data from Gaia. The analysis successfully determined the stellar parameters of the system for the first time, revealing the following: $ T_{\rm eff.}=5945\pm80\,K$, $R=1.018\pm0.05\, R_\odot$ , $\mathcal{M}=1.12\pm0.10\, \mathcal{M_\odot}$ for the primary component and $T_{\rm eff.}=5700\pm80\,K$, $R=0.975\pm0.06\, R_\odot$ , $\mathcal{M}=1.05\pm0.09\, \mathcal{M_\odot}$ for the secondary component. Both components are found to have solar metallicity and are approximately 1 billion years old. The combined spectrophotometric and dynamical analysis resulted in precise stellar masses and a new dynamical parallax of $23.756\pm0.001$ mas, consistent with the Hipparcos 1997 measurement ($23.76\pm1.07$) and intermediate between that of Gaia DR2 ($23.51\pm0.28$) and Gaia DR3 ($24.31\pm0.19$). In addition to studying the habitability and stability of the exoplanets within the system, we explored the processes of its formation and evolution.

• Introduced a semi-automated annotation workflow for efficient large-scale UAV labeling. • Proposed a lightweight hybrid segmentation framework integrating Mamba, Transformer, and CNN. • Achieved a superior balance between segmentation accuracy and computational efficiency. • Demonstrated strong performance and generalizability across diverse landscapes. • Enabled scalable regional mapping and monitoring of Acacia tortilis from UAV images. Acacia tortilis (Umbrella thorn acacia), native to the dry and semi-arid zones of Africa and the Middle East, can withstand extreme climatic conditions and provides substantial ecological and socioeconomic benefits. Reliable, regional-scale mapping and monitoring are increasingly critical as populations face mounting environmental and anthropogenic pressures. This study presents an efficient and lightweight deep learning framework for the regional-scale delineation of A. tortilis from ultra-high spatial resolution unmanned aerial vehicle (UAV) images. A comprehensive field campaign was conducted to collect ground-truth samples across diverse urban, agricultural, and mountainous settings. Aiming to expedite the labor-intensive labeling process and generate consistent, reliable training data, a semi-automated annotation workflow was developed, expanding the annotated dataset nearly fourfold to approximately 36,800 A. tortilis instances. The study also introduces a hybrid U-shaped Mamba-transformer architecture, designed to maximize the advantages of Mamba units, Transformers, and convolutional networks for joint modeling of local detail and extensive contextual information. The proposed framework was evaluated against several representative CNN–, Transformer-, and Mamba-based models, as well as alternative decoder designs, to assess segmentation quality, efficiency, and generalizability. Using an independent testing dataset, the hybrid model family (tiny, small, and base variants) demonstrated a strong balance between accuracy and efficiency, achieving mean intersection over union values ranging from 85.30% to 85.44% and mean F-scores between 91.52% and 91.61%, maintaining consistent performance across geographically distinct regions. The lightweight CNN-based decoder offered an optimal balance between accuracy and computational cost, with substantially reduced training time compared to more complex decoders. Across an extensive regional survey extent (∼250 km 2 ), the framework consistently delineated A. tortilis crowns, underscoring robust and scalable performance. Thus, the proposed framework can facilitate systematic monitoring of A. tortilis and support the conservation, restoration, and sustainable management of native tree species across arid and semi-arid ecosystems.

Abstract Green growth integrates economic development with ecological sustainability, emphasizing emissions reduction, biodiversity conservation, and resource efficiency. However, the interplay of renewable energy diversification, education, and globalization in shaping green growth pathways remains understudied, particularly in emerging economies like India. This study aims to examine how solar energy (SOR), biomass energy (BIO), education (EDU), and globalization (GLO) influence green growth (GGW), selecting India as the focus due to its significant investments in biomass energy, solar energy, and education initiatives. This study used the wavelet quantile-based approaches to observed how these factors impact GGW across different quantiles and period. The result from the wavelet quantile regression (WQR) indicates that: (i) SOR exerts a positive and significant influence on GGW in the medium and long term, while in the medium term its effect is quantile-dependent, varying in sign and magnitude; (ii) BIO exhibits no significant effect on GGW across quantiles in the short run, while exerting a positive and statistically significant impact on GGW across most quantiles in the medium and long term; (iii) the role of EDU exhibits heterogeneous short-run effects, and becomes predominantly positive in the medium and long run (iv) GLO exerts a significant and negative effect on GGW at lower quantiles in the short run; in the medium and long term, the positive and significant effect is observed. Moreover, the result from the wavelet quantile correlation analysis gives validation to the result from the WQR method. Furthermore, the result of the Wavelet Quantile Granger Causality method revealed the evidence of causation from the explanatory variables to GGW. The study proposed policies based on these findings.

Brain-computer interfaces (BCIs) were initially created to help individuals with disabilities control devices and communicate without muscle movement. Today, BCIs are used for prosthetic control, cognitive enhancement, and neurological rehabilitation. The BCI system depends on analyzing electroencephalogram (EEG) signals captured from the brain. Decoding these EEG signals is a complex process that combines multiple algorithms to extract meaningful information from these intricate and noisy signals. One of the most popular techniques is the Common Spatial Patterns (CSP), which helps preserve useful and sensitive information. This paper presents an optimized extension of the CSP model for extracting EEG data features in a multiclass setting using Riemannian geometry-based weighting. The use of weighting based on Riemannian geometry enhances the robustness of covariance matrix computation, thereby decreasing the influence of noise that can significantly distort the mean of covariance matrices in the traditional CSP method. The proposed approach is also extended by the integration of a multi-band filter bank, providing a more detailed examination of EEG signals. Three classifiers, Linear Discriminant Analysis (LDA), Random Forest Classifier (RFC), and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery tasks. LDA achieves an accuracy of 80.40%, while MLP and RFC reach 80.02% and 80.90%, respectively. The results obtained using a majority vote combining the decisions of the three classifiers are 81.83% for accuracy and Recall, 82.74% for precision, and 81.87% for F1-score. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications.

Improving the energy efficiency of buildings has become a critical priority, particularly in hot–humid climates where cooling demands are exceptionally high. Hence, due to buildings being considered one of the biggest contributors to energy consumption, utilizing feasible retrofitting strategies is critical. This study assessed the impact of three retrofitting strategies—enhanced glazing, enhanced roof insulation, and reflective cooling paint on exterior walls—on the cooling load, energy consumption, and economic feasibility of a residential townhouse in hot–humid climates in Sharjah. The methodology involved simulating various retrofitting scenarios using DesignBuilder to assess their impact on energy consumption and cooling load, followed by an economic analysis to evaluate feasibility in terms of cost, energy savings, and payback periods. It was found that using reflective paint achieved the highest reductions in energy consumption (8%) and cooling load (13%), while combining reflective paint with glazing upgrades offered up to 10% energy savings and 16% cooling load reduction. These results highlight that reflective paints and their combinations with other strategies provide a cost-effective and energy-efficient solution for retrofitting older buildings in hot–humid climates, making them a sustainable choice for reducing energy demand and promoting thermal comfort. The findings offer practical insights for replicable interventions in similar hot–humid urban contexts and contribute to the regional transition toward nearly net-zero energy buildings.