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: 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

Release of industrial effluents comprising dyes in water bodies is one of the foremost causes of water pollution. Therefore, the proper and proficient treatment of these dyes contaminated left-over material before their release is crucial. Herein, an eco-friendly biological macromolecule Gum-Acacia (GA) integrated Fe3O4 nanoparticles composite hydrogel was manufactured via co-precipitation technique for effective adsorption of Congo red (CR) dye existing in water bodies. The as-prepared magnetic GA/Fe3O4 composite hydrogel was characterized by FTIR, XRD, EDX, VSM, SEM, and BET techniques. These studies discovered the fruitful fabrication of biodegradable magnetic GA/Fe3O4 composite hydrogel possessing porous structure with large surface are

This study presents, for the first time, an innovative Jet Plasma-assisted technique for the green synthesis of TiO₂@Ag core–shell nanoparticles using chard leaf extract as a natural reducing and stabilizing agent. The Jet Plasma provides a highly energetic environment that accelerates nucleation and core–shell formation at low temperatures without toxic precursors. The synthesized nanoparticles exhibited uniform and stable structures, as confirmed by comprehensive characterization techniques including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), ultraviolet–visible (UV–Vis) spectroscopy, transmission electron microscopy (TEM), and zeta potential analysis. XRD patterns confirmed the crystalline anatase

University campuses in Iraq are substantial energy consumers, with consumption increasing significantly during periods of high temperatures, underscoring the necessity to enhance their energy performance. Energy simulation tools offer valuable insights into evaluating and improving the energy efficiency of buildings. This study focuses on simulating passive architectural design for three selected buildings at Al-Khwarizmi College of Engineering (AKCOE) to examine the effectiveness of their cooling systems. DesignBuilder software was employed, and climatic data for a year in Baghdad was collected to assess the influence of passive architectural strategies on the thermal performance of the targeted buildings. The simulations revealed that the

Background: A worldwide health epidemic, type 2 diabetes mellitus was significantly influenced by chronic inflammation, which led to increased insulin resistance (IR). The most widely practiced form of therapy used to control musculoskeletal pain in people with diabetes is non-steroidal anti-inflammatory drugs (NSAIDs), which provide their action by inhibiting cyclooxygenase enzyme (COX). COX1, COX2, and COX3 are distinct isoforms of the cyclooxygenase enzyme. The potential anti-inflammatory benefits of cyclooxygenase-2 (COX-2) inhibitors, both selective and non-selective, have been investigated concerning the management of type 2 diabetes patients. Objective: the purpose of this research is to explore the impact of highly selective