The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is

BACKGROUND: Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is common, yet no curative treatment identified. Cinnamon is a herbal substance, which has many applications in medicine. AIM: The aim of the study was to study the effect of cinnamon on patients with chronic pelvic pain syndrome. METHODS: Sixty patients with documented CP/CPPS randomized into two groups during 2018 and 2019 in Baghdad. The first group received 60 capsules each contained 1 g of cinnamon. The other group received 60 capsules each contained 1 g of sugar powder (placebo). All the patients instructed to take one capsule twice daily for 1 month. National Institutes of Health-Chronic Prostatitis Symptom Index (NIH-CPSI) was reported for bot

The current study aims to develop a teaching design in accordance with cluster thinking strategies and explore the effect of this teaching design on students’ achievement in science. To this end, the null hypothesis was adopted: there is no statistically significant difference at the level of (0, 05) between experimental group who adopted the teaching design in learning science and control group who follow the traditional method in learning the same subject. To test the null hypothesis, total of (74) students from Al-Alaama Hussain Mahfooth intermediate school were selected intentionally for the academic year 2016-2017. The sample divided into two equal groups when all the variables (age, prior achievement of science,

Background: Osteoporosis is a systemic skeletal disorder that has an impact on general health, dental health and salivary composition. The mineralization of teeth happens simultaneously with that of the skeleton, but if mineral metabolism is disrupted, tooth failures will resemble those that affect bone tissue. Vitamin D plays a key role in bone and tooth mineralization.

Objective: to evaluate the impact of osteoporosis on teeth decay in relation to salivary vitamin D among menopause in Baghdad city.

Subjects and Methods: This study was cross sectional study. The study group consists of

Background: During pregnancy many physiological, anatomical and biochemical changes take place that affect almost all body systems. In the oral pregnant women have serious changes such as more sever dental caries. This study was conducted to measure dental caries severity and selected salivary variables (salivary flow rate, PH and viscosity)and to find the relation of dental caries with these salivary variables. Subjects, materials and methods: The study group consisted of 60 pregnant women that were divided into three equal groups according to trimester (20 pregnant women in each trimester).They were selected randomly from the Maternal and Child Health Care Centers in Baghdad city, the age range was 20-25 years. In addition to 20 unmarried

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to

This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining