The Indoor Environmental Quality (IEQ) describes an indoor space condition that the wellbeing and comfortability are provided for the users. Many researchers have highlighted the importance of adopting IEQ criteria, although they are not yet well defined in the Kurdistan region. However, environmental quality is not necessary for the contemporary buildings of the Kurdistan Region, and there is no measurement tool in the Region. This research aims to develop an IEQ assessment tool for the Kurdistan region using Mixed method methodology, both qualitative and quantitative. Therefore, a Delphi Technique was used as a method initially developed as systematic, interactive forecasting on a panel of experts. Thirty-five Delphi C

Total dissolved solids are at the top of the parameters list of water quality that requires investigations for planning and management, especially for irrigation and drinking purposes. If the quality of water is sufficiently predictable, then appropriate management is possible. In the current study, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were used as indicators of water quality and for the prediction of Total Dissolved Solids (TDS) along the Tigris River, in Baghdad city. To build these models five water parameters were selected from the intakes of four water treatment plants on the Tigris River, for the period between 2013 and 2017. The selected water parameters were Total Dissolved Solids (TDS

This paper offers a systemic review of the deep learning methods to detect violence on campus, which is a critical issue in intelligent surveillance to improve the student safety and prompt cut off of violent accidents. The review reviews studies published 2018-2025, concentrating on model structure to detect fights, bullying, vandalism, and aggressive behavior on problematic campuses due to occlusion and light variations and complicated human interactions. The research design includes a comparative study of different deep learning networks, such as CNNs, RNNs, 3D CNNs, attention-based networks, transformers, graph neural networks, neuro-fuzzy, and multimodal systems and federated learning methods. The paper also assesses benchmark

The power generation of solar photovoltaic (PV) technology is being implemented in every nation worldwide due to its environmentally clean characteristics. Therefore, PV technology is significantly growing in the present applications and usage of PV power systems. Despite the strength of the PV arrays in power systems, the arrays remain susceptible to certain faults. An effective supply requires economic returns, the security of the equipment and humans, precise fault identification, diagnosis, and interruption tools. Meanwhile, the faults in unidentified arc lead to serious fire hazards to commercial, residential, and utility-scale PV systems. To ensure secure and dependable distribution of electricity, the detection of such ha

Background: Febrile convulsions are the most frequent type of seizures in children under 6 years of age. Significant percentage of these children will later suffer from recurrence of febrile convulsion.Objectives: To identify the main risk factors for recurrent febrile convulsions in children.Methods: we carried out a case control study involving 89 children those who experienced first attack of febrile convulsions and 92 children with recurrent attack of febrile convulsions. The study was conducted in Central Children Teaching Hospital, Baghdad during the period 2006- 2007. Results: Compared to children with first attack of febrile convulsion, children with recurrent seizures were younger at onset (4- 12m) (67% vs. 44%), mainly male (70

Fibroepithelioma of Pinkus (FEP) is a slowly growing, low-grade malignant tumor with very low metastatic potential that is considered a distinct variant of basal cell carcinoma (BCC). It usually manifests as sessile or polypoidal lesions on the trunk of middle-aged patients. However, it may present in younger age groups, even in children. In this case, we present a rare case of FEP atypically presenting as a scaly plaque on the lower back for several years in an elderly female who was eventually diagnosed by excisional biopsy and histopathology.

Developing an efficient algorithm for automated Magnetic Resonance Imaging (MRI) segmentation to characterize tumor abnormalities in an accurate and reproducible manner is ever demanding. This paper presents an overview of the recent development and challenges of the energy minimizing active contour segmentation model called snake for the MRI. This model is successfully used in contour detection for object recognition, computer vision and graphics as well as biomedical image processing including X-ray, MRI and Ultrasound images. Snakes being deformable well-defined curves in the image domain can move under the influence of internal forces and external forces are subsequently derived from the image data. We underscore a critical appraisal