The present paper confirmed the presence of Phrynocephalus maculatus longicaudatus Haas, 1957 in Iraq and recorded the first observations of this taxon in Al-Muthanna province southwestern of Iraq. The existence of the species is yet uncertain in Iraq. The habitat and morphological characteristics of this species were reviewed.

The important device in the Wireless Sensor Network (WSN) is the Sink Node (SN). That is used to store, collect and analyze data from every sensor node in the network. Thus the main role of SN in WSN makes it a big target for traffic analysis attack. Therefore, securing the SN position is a substantial issue. This study presents Security for Mobile Sink Node location using Dynamic Routing Protocol called (SMSNDRP), in order to increase complexity for adversary trying to discover mobile SN location. In addition to that, it minimizes network energy consumption. The proposed protocol which is applied on WSN framework consists of 50 nodes with static and mobile SN. The results havw shown in each round a dynamic change in the route to reach mobi

Background: Normal thyroid function is essential for neonatal growth and brain development. In a newborn infant with severe disease, endocrine regulation of hormones can be affected by abnormal metabolism. The assessment of thyroid parameters results in the recognition of a dysfunction and its association with disease severity. Objective: This study aimed to assess thyroid function profiles in critically ill neonates in the neonatal intensive care unit (NICU) compared with healthy controls. Additionally, we aimed to detect the presence of TD and its possible association with critical illness. Methods: A case-control study was performed in 100 neonates, comprising 50 sick neonates and 50 healthy controls. We measured thyroid functio

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Unter Exilliteratur versteht man „die literarische Produktion von Autoren, die wegen politischer, religiöser oder rassistischer Verfolgung gezwungen sind, sich an einem anderen als dem von ihnen gewünschten Lebens- und Arbeitsort aufzuhalten.“[1] Deswegen unterscheidet sich „Migration“ von „Exil“ dadurch, dass sie freiwillige Auswanderung ist. Bereits in der Antike und auch im Mittelalter gab es Schriftsteller, die vor Zensur oder politischer Verfolgung ins Exil geflohen sind, wie zum Beispiel der griechische Satiriker des 

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

Objectives: To assess the qualifications and performance of newly graduated pharmacists and explore factors that affect their performance in public hospitals. Methods: This qualitative study included face-to-face, semi-structured interviews with hospital practitioner pharmacists. It was conducted between March and May 2023 in Kerbala province, Iraq. The participating pharmacists were selected purposefully because they had three or more years of work experience at governmental hospitals. The audio-recording interviews were transcribed. Thematic analyses were used to generate themes and subthemes from the interviews. Results: Twenty-seven hospital pharmacists participated in this study. The study found that there are several factors t

The linear segment with parabolic blend (LSPB) trajectory deviates from the specified waypoints. It is restricted to that the acceleration must be sufficiently high. In this work, it is proposed to engage modified LSPB trajectory with particle swarm optimization (PSO) so as to create through points on the trajectory. The assumption of normal LSPB method that parabolic part is centered in time around waypoints is replaced by proposed coefficients for calculating the time duration of the linear part. These coefficients are functions of velocities between through points. The velocities are obtained by PSO so as to force the LSPB trajectory passing exactly through the specified path points. Also, relations for velocity correction and exact v

To finalize any construction investment project, it would be necessary to identify the most significant problems and obstacles that lead to project reluctance and stalling. Unexpected events and conflicts may have disrupted these strategies and impacted project development. Due to the high initial investment costs of construction projects, crises can have an immediate impact, resulting in significant financial losses. The 2014 financial crisis was one of the most prominent crises that Iraq faced, which prompted the researcher to identify and evaluate those obstacles through this research and questionnaires using Pareto scientific theory to exclude factors that do not contribute to project lag. It was discovered that 28 o