A new two-way nesting technique is presented for a multiple nested-grid ocean modelling system. The new technique uses explicit center finite difference and leapfrog schemes to exchange information between the different subcomponents of the nested-grid system. The performance of the different nesting techniques is compared, using two independent nested-grid modelling systems. In this paper, a new nesting algorithm is described and some preliminary results are demonstrated. The validity of the nesting method is shown in some problems for the depth averaged of 2D linear shallow water equation.

Endophytic bacteria produced analogous secondary metabolites of their hosts. Similarly, the ability to generate antioxidants is not an exception. Dragon scales (Pyrrosia piloselloides), an epiphytic plant of the Polypodiaceae family, are frequently overlooked. This research aims to isolate antioxidant-producing bacteria from dragon-scale fern leaves. The antioxidant activities were tested after the extraction procedure using ethanolic extract. Bacteria were characterized and selected as candidates for antioxidant production by screening for the production of total phenolic compounds. Antioxidant levels were determined utilizing the ABTS, FRAP, and DPPH techniques. The preliminary findings of the entire phenolic compound test rev

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

The research aims to identify the tax policy strategy adopted in Iraq after the change of the tax system in 2003 and beyond, and then make a comparison of the two strategies on corporate data whether they are charged with progressive tax rates and after the change of the system as the tax rates became fixed, and then indicate the changes In the tax proceeds, and knowing the imensions of the approved tax policy, is it a tax reform strategy or a strategy to attract investments.The research started from the problem of exposure of the Iraqi tax system to several changes, as this led to a reflection on the technical organization of taxes, in terms of the tax rate.The descriptive analytical approach was chosen to study the actual reality of th

This research will cover different aspects of estimating process of construction work in a desert area. The inherent difficulties which accompany the cost estimating of the construction works in desert environment in a developing country, will stem from the limited information available, resources scarcity, low level of skilled workers, the prevailing severe weather conditions and many others, which definitely don't provide a fair, reliable and accurate estimation. This study tries to present unit price to estimate the cost in preliminary phase of a project.  Estimations are supported by developing mathematical equations based on the historical data of maintenance, new construction of managerial and school projects.