Inefficient wastewater disposal and wastewater discharge problems in water bodies have led to increasing pollution in water bodies. Pollutants in the river contribute to increasing the biological oxygen demand (BOD), total suspended solids (SS), total dissolved solids (TDS), chemical oxygen demand (COD), and toxic metals render this water unsuitable for consumption and even pose a significant risk to human health. Over the last few years, water conservation has been the subject of growing awareness and concern throughout the world, so this research focused on review studies of researches that studied the importance of water quality of wastewater treated disposal in water bodies and modern technology to management wastewater disposals.
This paper deals with modelling and control of Euler-Bernoulli smart beam interacting with a fluid medium. Several distributed piezo-patches (actuators and/or sensors) are bonded on the surface of the target beam. To model the vibrating beam properly, the effect of the piezo-patches and the hydrodynamic loads should be taken into account carefully. The partial differential equation PDE for the target oscillating beam is derived considering the piezo-actuators as input controls. Fluid forces are decomposed into two components: 1) hydrodynamic forces due to the beam oscillations, and 2) external (disturbance) hydrodynamic loads independent of beam motion. Then the PDE is discretized usi
Background:Periodontal diseases are infectious diseases in which periodontalpathogens trigger chronic inflammatory and immune responses. Interleukine-6 is a multifunctional cytokine playing a central role in inflammation and tissue injury.The aim of the study IS to determine the level of Interleukin-6(IL-6) in saliva of patients with chronic periodontitis compared to healthy subjects. Materials and Methods:The total subjects of the present study is 60, divided into 3 groups; 20 patients with chronic periodontitis with pocket depth(PD ≥4 mm)(group I), 20 patients with pocket depth(PD <4 mm) with clinical attachment loss (group II), and 20 healthy controls with pocket probing depth (PPD ≤ 3 mm) without clinical attachment loss (g
... Show MoreThe technology of subsurface soil water retention (SWRT) uses a polyethylene trough that is fixed under the root zone of the plant. It is a modern technology to increase the values of water use efficiency, plant productivity and saving irrigation water by applying as little irrigation water as possible. This study work aims at improving the crop yield and water use efficiency of a cucumber plant with less applied irrigation water by installing membrane trough below the soil surface. The field experiment was conducted in the Hawr Rajab District of Baghdad Governorate in Winter 2018 for testing various trickle irrigation systems. Two agricultural treatment plots were utilized in a greenhouse for the comparison. Plot T1 has used a subsurface t
... Show MoreThe technology of subsurface soil water retention (SWRT) uses a polyethylene trough that is fixed under the root zone of the plant. It is a modern technology to increase the values of water use efficiency, plant productivity and saving irrigation water by applying as little irrigation water as possible. This study work aims at improving the crop yield and water use efficiency of a cucumber plant with less applied irrigation water by installing membrane trough below the soil surface. The field experiment was conducted in the Hawr Rajab District of Baghdad Governorate in Winter 2018 for testing various trickle irrigation systems. Two agricultural treatment plots were utilized in a greenhouse for the compa
... Show MoreThis study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K
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