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

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

 Finding a path solution in a dynamic environment represents a challenge for the robotics researchers, furthermore, it is the main issue for autonomous robots and manipulators since nowadays the world is looking forward to this challenge. The collision free path for robot in an environment with moving obstacles such as different objects, humans, animals or other robots is considered as an actual problem that needs to be solved.  In addition, the local minima and sharp edges are the most common problems in all path planning algorithms. The main objective of this work is to overcome these problems by demonstrating the robot path planning and obstacle avoidance using D star (D*) algorithm based on Particle Swarm Optimization (PSO)

 Humanity is confronted with a growing array of environmental challenges that demand immediate attention and cannot be disregarded. One of the issues the world faces is air pollution, which presents a significant risk to both the environment and human well-being. The capitalist system has a great impact on the exacerbation of air pollution and environmental deterioration. This impact is reflected in Caryl Churchill’s post-apocalyptic play Not Not Not Not Not Enough Oxygen (1971). The play presents a futuristic scenario in which humanity faces grave consequences due to the polluting practices of capitalism and the unsustainable exploitation of natural resources. It depicts a future in which environmental degradation drives people

Objectives: The study aim to evaluate nursing performance during nasogastric tube feeding in neonatal intensive care unit. Methodology: A descriptive study was carried out in Neonatal Intensive Care Unit at al–Batool Teaching Hospital, for the purpose of evaluate of quality of nursing performance for premature baby during nasogastric tube feeding in neonatal intensive care unit. The study consumed the period from 4th of December 2017 to the 24nd of April 2018, Non-probability purposive sample of (25) nurses working in the neonatal intensive care unit. The data were collected through the use of Observational instrument which consist of socio-demographic characteristics, quality of nursing care. Results: The study shows that the majority