Heart disease is a significant and impactful health condition that ranks as the leading cause of death in many countries. In order to aid physicians in diagnosing cardiovascular diseases, clinical datasets are available for reference. However, with the rise of big data and medical datasets, it has become increasingly challenging for medical practitioners to accurately predict heart disease due to the abundance of unrelated and redundant features that hinder computational complexity and accuracy. As such, this study aims to identify the most discriminative features within high-dimensional datasets while minimizing complexity and improving accuracy through an Extra Tree feature selection based technique. The work study assesses the efficac

An efficient modification and a novel technique combining the homotopy concept with  Adomian decomposition method (ADM) to obtain an accurate analytical solution for Riccati matrix delay differential equation (RMDDE) is introduced  in this paper  . Both methods are very efficient and effective. The whole integral part of ADM is used instead of the integral part of homotopy technique. The major feature in current technique gives us a large convergence region of iterative approximate solutions .The results acquired by this technique give better approximations for a larger region as well as previously. Finally, the results conducted via suggesting an efficient and easy technique, and may be addressed to other non-linear problems.

Objective: We hypothesized that attacking cancer cells by combining various modes of action can hinder them from taking the chance to evolve resistance to treatment. Incorporation of photodynamic therapy (PDT) with oncolytic virotherapy might be a promising dual approach to cancer treatment. Methods: NDV AMHA1 strain as virotherapy in integration with aminolaevulinic acid (ALA) using low power He-Ne laser as PDT in the existing work was examined against breast cancer cells derived from Iraqi cancer patients named (AMJ13). This combination was evaluated using Chou–Talalay analysis. Results: The results showed an increased killing rate when using both 0.01 and 0.1 Multiplicity of infection (MOI) of the virus when combined with a dose of 617

Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are

This study provides valuable information on secondary microbial infections in H1N1 patients compared to Seasonal Influenza in Iraqi Patients. Nasopharynx  swabs were collected from  (12 ) patients  infected with Seasonal influenza (11  from Baghdad  and 1 Patient from south of Iraq) ,and ( 22 ) samples from patients with 2009 H1N1 ( 20 from Baghdad and  2 from  south of Iraq). The results show that the patients infected with 2009 H1N1 Virus were younger than healthy subjects and those infected with seasonal influenza. And the difference reached to the level of significance     (p< 0.01) compared with healthy subjects.Two cases infected with 2009 H1N1 virus (9.1%) were fro

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

A skip list data structure is really just a simulation of a binary search tree. Skip lists algorithm are simpler, faster and use less space. this data structure conceptually uses parallel sorted linked lists. Searching in a skip list is more difficult than searching in a regular sorted linked list. Because a skip list is a two dimensional data structure, it is implemented using a two dimensional network of nodes with four pointers. the implementation of the search, insert and delete operation taking a time of upto . The skip list could be modified to implement the order statistic operations of RANKand SEARCH BY RANK while maintaining the same expected time. Keywords:skip list , parallel linked list , randomized algorithm , rank.

The work reported in this study focusing on the abrasive wear behavior for three types of pipes used in oil industries (Carbone steel, Alloy steel and Stainless steel) using a wear apparatus for dry and wet tests, manufactured according to ASTM G65. Silica sand with
hardness (1000-1100) HV was used as abrasive material. The abrasive wear of these pipes has been measured experimentally by measuring the wear rate for each case under different sliding speeds, applied loads, and sand conditions (dry or wet). All tests have been conducted using sand of particle size (200-425) µm, ambient temperature of 34.5 °C and humidity 22% (Lab conditions).
The results show that the material loss due to abrasive wear increased monotonically with