University of Science and Technology of Mazandaran

Image enhancement remains a cornerstone in digital image processing, aiming to improve visual clarity through various methods. Spatial domain techniques include integer-order and fractional-order differentiation. Although widely used, traditional integer-order differentiation techniques suffer from limitations such as indiscriminate spatial frequency treatment and noise amplification, leading to degraded image quality. This paper proposes an adaptive fractional-order differentiation approach employing Caputo fractional differential masks to selectively enhance image details. This approach uses image gradient information to determine the appropriate fractional order. By dynamically adjusting the fractional order based on specific image requirements, the method achieves superior contrast improvement while preserving fine details and minimizing noise. Experimental results, evaluated using metrics such as Pratt's Figure of Merit (FOM), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR), demonstrate that this approach outperforms comparable techniques, highlighting its effectiveness in image enhancement.

Summary Dealing with phenomena such as gas hydrate formation, which threatens sustainable production by blocking transmission lines, is a priority during field operations. The presence of laboratory data on gas hydrate thermodynamic equilibria, as well as models that can estimate it with acceptable approximation within the range of the field temperature and pressure profile, seems essential for safe and sustainable production by mitigating the risks associated with hydrate formation. The aim of the present work is to examine the pressure-temperature region of the hydrate zone, at which stable hydrate forms thermodynamically, for the South Pars gas field with natural gas composition including hydrogen sulfide (H2S) in the presence of monoethylene glycol (MEG) and methanol (MeOH), commercially used with the intention of performing thermodynamic inhibition. First, static experimental high-pressure tests were performed at levels of 10 wt%, 20 wt%, and 30 wt%. The presence of two thermodynamic inhibitors shifted the hydrate equilibrium curve (i.e., the border of the hydrate zone) to lower temperatures. The average suppression temperature in the presence of MEG with the concentration of 10 wt%, 20 wt%, and 30 wt% is 1.1°C, 4.9°C, and 9.5°C, respectively. In return for the presence of MeOH, it was observed as 4.0°C, 8.7°C, and 15.2°C, representing a more potent inhibitory effect of MeOH compared with MEG. Then, a thermodynamic model was provided exclusively to anticipate the South Pars gasfield hydrate equilibrium conditions based on the fugacity difference of water in the hydrate and aqueous phases. The model incorporated the van der Waals-Platteeuw (vdW-P) model, Peng-Robinson (PR) equation of state (EOS), and nonrandom two-liquid (NRTL) activity coefficient model related to the hydrate phase, vapor phase, and alcohol/glycol solutions, respectively. The optimized parameters of the model were determined using a genetic algorithm (GA) technique. The prediction of the model showed acceptable accuracy, with average absolute pressure deviations below 7% compared with the experimental results.

A bstract A first study is presented of the cross section for the scattering of same-sign W boson pairs via the detection of a τ lepton. The data from proton-proton collisions at the center-of-mass energy of 13 TeV were collected by the CMS detector at the LHC, and correspond to an integrated luminosity of 138 fb −1 . Events were selected that contain two jets with large pseudorapidity and large invariant mass, one τ lepton, one light lepton (e or μ ), and significant missing transverse momentum. The measured cross section for electroweak same-sign WW scattering is $${1.44}_{-0.56}^{+0.63}$$ times the standard model prediction. In addition, a search is presented for the indirect effects of processes beyond the standard model via the effective field theory framework, in terms of dimension-6 and dimension-8 operators.

Reconstructing and analyzing protein point clouds pose considerable computational challenges due to the intricate three-dimensional structures and variability inherent to proteins. In this study, we adapt the Variational FoldingNet (VF-Net) model, initially developed for dental datasets, to address these challenges in the context of protein point clouds. By implementing a customized data pipeline and introducing architectural enhancements, we optimize the model’s performance for accurate protein structure reconstruction while preserving computational efficiency. Our approach effectively handles the inherent variability in protein shapes and ensures precise reconstruction through a consistent one-to-one mapping between input and output point clouds. Experimental results highlight significant reductions in reconstruction errors, demonstrating the potential of this adapted model for advanced protein structural analysis. This work lays the groundwork for leveraging variational models in other complex 3D datasets, paving the way for future innovations in computational biology and beyond.