Robustness assessment of Muscat coastal highway network (CHN) under multi-hazard scenarios focusing on traffic stability and adaptation measures.

This study critically examines the reliability and resilience of the Muscat coastal highway network (CHN) under the compounded effects of earthquakes and floods, representing interacting multi-hazard scenarios. The analysis utilized fragility functions for both earthquake-induced and flood-induced landslides, integrating these with traffic data for selected highway links to estimate bridge damage and assess CHN functionality in post-hazard conditions. Economic sensitivity analysis revealed a significant increase in costs due to flood-induced landslides, emphasizing the impact of dominant intensity measures on network costs and traffic flow.

Photon-phonon destructive-constructive polarization quantization in Sm:BiPO.

We report photon-phonon dressing quantization dependency on polarization. Destructive dressing polarization quantization is exhibited in fluorescence (FL) for narrowband signals, while constructive dominant dressing quantization is exhibited in fluorescence (FL) for broadband signals due to phase perturbation. Furthermore, constructive polarization quantization results due to coexistence of generation and dressing effects in strong and competitive Rabi frequency.

Co-occurrence of Parkinson's disease and Retinitis Pigmentosa: A genetic and in silico analysis.

Introduction: Parkinson's disease (PD) is primarily driven by the protein Alpha Synuclein (A-Syn) accumulation. Synphilin-1 protein, encoded by the SNCAIP gene, which co-localizes with A-Syn is a known risk factor for PD. Retinitis pigmentosa (RP), is a cluster of retinal degenerative disorders, and Cyclic Nucleotide Gated channel subunit Alpha 1 (CNGA1) is one of the initial genes associated with RP.

An innovative artificial neural network model for smart crop prediction using sensory network based soil data.

A thriving agricultural system is the cornerstone of an expanding economy of agricultural countries. Farmers' crop productivity is significantly reduced when they choose the crop without considering environmental factors and soil characteristics. Crop prediction enables farmers to select crops that maximize crop yield and earnings.