This paper introduces an innovative, AI-driven methodology for predicting Ship Resistance using only three fundamental input parameters: Length Between Waterlines (LWL), Beam at Waterline (BWL), and Draft (T). Traditional Resistance prediction techniques such as empirical methods, towing tank experiments, and computational fluid dynamics (CFD) simulations are highly accurate but involve significant time, cost, and complexity. Our approach leverages machine learning algorithms, including XGBoost, CatBoost, and Gradient Boosting, to derive a comprehensive suite of hydrodynamic characteristics from a robust dataset comprising 308 full-scale experiments across 22 different hull shapes. The methodology begins with meticulous data preprocessing and feature engineering, including normalization, outlier analysis, and correlation assessment, to ensure reliability and minimize error propagation. By transforming raw hydrodynamic data into dimensionless groups, our models effectively capture both linear and non-linear relationShips among critical parameters such as displacement, wetted surface area, midShip section area, waterplane area, and the longitudinal center of buoyancy (LCB). Simple linear regression techniques were successfully used to derive parameters with perfect correlations, while more complex non-linear interactions were accurately predicted using advanced ensemble methods. The integration of these AI models into a Django-based web application further enhances the utility of our approach, providing naval architects and marine engineers with a user-friendly, real-time tool for design optimization and performance evaluation. Comparative analysis indicates that our streamlined model delivers predictions of residual and frictional Resistance with accuracy comparable to traditional methods, while offering significant improvements in computational efficiency and cost-effectiveness. Overall, this research bridges the gap between classical hydrodynamic theory and modern artificial intelligence techniques, offering a rapid, reliable, and scalable solution for Ship Resistance prediction that has the potential to significantly enhance early-stage design processes in naval architecture.

In small towing tanks, due to tank and model dimension limitations, tests have been performed at low Reynolds number and laminar flow regimes. In this condition, flow momentum is low and as a result, the probability of flow separation is high. This has been resulted to a difference in flow behavior around the model and prototype. Therefore the scale effect in model testing cannot be avoided, which will be grater in small towing tanks. For correction of results in all towing tanks, the scale effect correction factor has been used. This paper is a result of tests done on a model of a 35000 tons Product Carrier. Tests have been done with and without a turbulent simulator and the scale correction factor for both conditions has been evaluated. Results show that, without a turbulent simulator, this factor approximately has been changed linearly. And, in the case of using a turbulent simulator, the amount of this factor decreased extremely and varied nonlinearly.

There is no doubt that religious knowledges have profound influence on master piece of persian literature considering of great literary men and quality of usage of religious knowledge helped to critisized these masterpieces and literary works. In this article by use of tradition "Safineh" (Ship) the author tried to have another view to great suffis" work Mathnavi.

Nowadays, various numerical methods are developed to extend computational fluid dynamics in engineering applications. One of the most useful methods in free surface modeling is Boundary Element Method (BEM). BEM is used to model inviscid fluid flow such as flow around Ships. BEM solutions employ surface mesh at all of the boundaries. In order to model the linear free surface, BEM can be modified to a simple form called Green Function Method (GFM).In this paper a developed code is applied GFM to several problems including flows around Ships and the results are presented.To verify the accuracy and application of the code, it is applied to flows around an analytically body which is Wigley hull and results are in good agreement with available data. The wave-making properties are also investigated. Variations of the wave pattern versus the speed are studied, and a quantitative investigation of the forces is performed. In addition, the effects of linear solution are studied. Presented results and computer code can be used to optimize the hull forms for decreasing wave making Resistance.

In the current study, dynamics of a twin-hull Ship has been evaluated in regular waves. Numerical solution has been done using the Reynolds-Averaged Navier-Stokes equations and the interDyMFoam solver of openFoam open-source code. The results of the numerical solution in calm water present acceptable agreement with the published experimental data. The maximum speed of the Ship in calm water is equivalent to Froude Number=0.747 and assuming the maximum engine power is constant, the Ship has been able to reach a speed equivalent to the Froude Number=0.274 in waves. Although the presence of the wedge in calm water condition has caused the lift force and the control of longitudinal instabilities, but the same wedge has caused the imbalance of pressure forces at a lower speed and in a state where the Ship is moving in waves. In general, the result of the forces acting on the Ship in the waves has led to an increase in the range of heave and pitch motions (5 and 7 percent respectively) for the body equipped with a wedge, which is a weak point for the Ship equipped with a wedge. Installing the wedge has increased the added Resistance in waves by 4.26%. Although, the addition of the wedge has reduced the longitudinal instability in calm water at design speed, but for this Ship it was proven that the addition of the wedge leads to the deterioration of the Ship dynamics in waves. So, a controllable angle stern wedge could be installed to have the best performance in all conditions.

2Background: Antibiotic Resistance represents a critical global concern within the medical community, posing significant challenges in the treatment of infections caused by drug-resistant pathogens. Over the years, broad-spectrum fluoroquinolones have been extensively used to treat infections caused by both Gram-positive and Gram-negative bacteria, including Pseudomonas aeruginosa. In this study, we decided to assess the prevalence of plasmid-mediated quinolone Resistance mechanisms among clinical isolates of P. aeruginosa in Ardabil hospitals. Methods: We analyzed a total of 200 clinical isolates of P. aeruginosa, collected between June 2019 and May 2023. The antibiotic Resistance profiles of these strains against various fluoroquinolone antibiotics were determined using the disk diffusion method. Additionally, we investigated the presence of qnrA, qnrB, qnrC, qnrD, and qnrS genes through polymerase chain reaction (PCR) analysis. Furthermore, we assessed the expression levels of efflux pump genes and outer membrane porin genes using the quantitative reverse transcription PCR (qRT-PCR) in fluoroquinolone-resistant P. aeruginosa strains. Results: Our findings revealed that 69% of P. aeruginosa strains were resistant to fluoroquinolones. The Resistance rates for different fluoroquinolones were as follows: ciprofloxacin 55.5%, ofloxacin 62%, norfloxacin 53.5%, lomefloxacin 55.3%, and levofloxacin 55.5%. Notably, 78.9% of these strains exhibited multidrug Resistance (MDR). Among the qnr genes, qnrB was the most prevalent (2.9%). No other qnr genes were identified. Interestingly, 75% of P. aeruginosa strains carrying the qnrB gene showed overexpression of efflux pump genes, while 100% exhibited down-regulation of the oprD gene. Conclusion: Given the high prevalence of fluoroquinolone-resistant P. aeruginosa clinical isolates in Ardabil hospitals and the multifactorial nature of Resistance, continuous monitoring of antibiotic Resistance trends and understanding the underlying Resistance mechanisms are crucial for selecting appropriate treatment strategies.