EGFR-mutant non–small cell lung cancer (NSCLC) initially responds well to EGFR tyrosine kinase inhibitors (EGFR-TKIs), yet resistance is nearly inevitable and responses to immune checkpoint inhibitors (ICIs) remain limited. In a retrospective cohort of 57 patients with advanced EGFR-mutant NSCLC treated with nivolumab or pembrolizumab, the objective response rate was 12.3%, although responses were concentrated in a small subgroup with concurrent high PD-L1 expression and dense intratumoral CD8⁺ immune-cell infiltration. Emerging evidence links these outcomes to chemokine-network dysregulation that alters immune trafficking and spatial organization. Oncogenic EGFR signaling shifts chemokine networks away from effector T-cell recruitment

Bovine theileriosis, caused by Theileria annulate, is disease affecting cattle and buffaloes worldwide. The current study aimed to screen the blood samples of 30 naturally suspected local buffaloes infected with Theileria species. The blood samples were initially examined by light microscopic and then the positive samples were subjected to PCR reactions. All 30 animals indicated clinical symptoms, such as high fever, loss of appetite, the presence of the hard tick, and enlargement of lymph nodes. The amplified products of 18S rRNA were analyzed, along with molecular detection of the drug-binding site alterations and interrelated changes in the cytochrome b (cyto b) gene. Blood smears revealed the presence of infected erythrocytes wi

Abstract 

     The aim of this work is to create a power control system for wind turbines based on fuzzy logic. Three power control loop was considered including: changing the pitch angle of  the blade, changing the length of the blade and turning the nacelle. The stochastic law was given for changes and instant inaccurate assessment of wind conditions changes. Two different algorithms were used for fuzzy inference in the control loop, the Mamdani and Larsen algorithms. These two different algorithms are materialized and developed in this study in Matlab-Fuzzy logic toolbox which has been practically implemented using necessary intelligent control system in electrical engineerin

Machine learning is considered a powerful technique in many applications such as classification, clustering, recognition and prediction. Deep learning is a modern, vital and superior machine learning that gives stunning performance, especially with huge data. Stock market price prediction is the process of determining the future value of a prospect of a financial instrument traded in the market, to gain a great profit a successful prediction must be conducted, in order to achieve that machine learning is used, in this article, two approaches are proposed to predict the stock market prices and movement using two datasets, the first approach employs two machine learning models (J48 & logistic regression) while the second approach based on rec

A field experiment is conducted to study the effect of different levels of peat (0, 25, 50, 75, and 100 Mg ha-1 to uncropped and cropped soil to wheat. Soil samples are taken in different period of time (0, 3, 30, 60, 90, 120, and 180 days after cultivation to determine (NaHCO3-Exteractable P at 3 different depths (0-10, 10-20, and 20-30 cm). Field Experiment is conducted in a randomized complete block design (RCBD) with four replicates. Wheat, Al-Rasheed variety, is cultivated as a testing crop. The entire field is equally dived in two divisions. One of the two divisions is cultivated to wheat and the second is left uncropped. The effect of five levels of peat namely 0, 25, 50, 75, 100 Mg ha-1 is investigated. Soils are fully analyzed

Maximizing the net present value (NPV) of oil field development is heavily dependent on optimizing well placement. The traditional approach entails the use of expert intuition to design well configurations and locations, followed by economic analysis and reservoir simulation to determine the most effective plan. However, this approach often proves inadequate due to the complexity and nonlinearity of reservoirs. In recent years, computational techniques have been developed to optimize well placement by defining decision variables (such as well coordinates), objective functions (such as NPV or cumulative oil production), and constraints. This paper presents a study on the use of genetic algorithms for well placement optimization, a ty

Abstract: Word sense disambiguation (WSD) is a significant field in computational linguistics as it is indispensable for many language understanding applications. Automatic processing of documents is made difficult because of the fact that many of the terms it contain ambiguous. Word Sense Disambiguation (WSD) systems try to solve these ambiguities and find the correct meaning. Genetic algorithms can be active to resolve this problem since they have been effectively applied for many optimization problems. In this paper, genetic algorithms proposed to solve the word sense disambiguation problem that can automatically select the intended meaning of a word in context without any additional resource. The proposed algorithm is evaluated on a col

Storage tanks condition and integrity is maintained by joint application of coating and cathodic protection. Iraq southern region rich in oil and petroleum product refineries need and use plenty of aboveground storage tanks. Iraq went through conflicts over the past thirty five years resulting in holding the oil industry infrastructure behind regarding maintenance and modernization. The primary concern in this work is the design and implementation of cathodic protection systems for the aboveground storage tanks farm in the oil industry.

Storage tank external base area and tank internal surface area are to be protected against corrosion using impressed current and sacrificial anode cathodic protection systems. Int