A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co

      Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.

Exploitation of mature oil fields around the world has forced researchers to develop new ways to optimize reservoir performance from such reservoirs. To achieve that, drilling horizontal wells is an effective method. The effectiveness of this kind of wells is to increase oil withdrawal. The objective of this study is to optimize the location, design, and completion of a new horizontal well as an oil producer to improve oil recovery in a real field located in Iraq. “A” is an oil and gas condensate field located in the Northeast of Iraq. From field production history, it is realized the difficulty to control gas and water production in this kind of complex carbonate reservoir with vertical producer wells. In this study, a horizont

The exponential growth of audio data shared over the internet and communication channels has raised significant concerns about the security and privacy of transmitted information. Due to high processing requirements, traditional encryption algorithms demand considerable computational effort for real-time audio encryption. To address these challenges, this paper presents a permutation for secure audio encryption using a combination of Tent and 1D logistic maps. The audio data is first shuffled using Tent map for the random permutation. The high random secret key with a length equal to the size of the audio data is then generated using a 1D logistic map. Finally, the Exclusive OR (XOR) operation is applied between the generated key and the sh

In the present work the preparation of Cu2S alloy was done throughout mixing the sulphur and copper elements according to the proper atomic weight and then put in an evacuated quartz ampoule which then sealed and heated at 1273 K for five hours and left to cool. Thin films from Cu2S powder thicknesses of ~300nm were prepared by thermal evaporation technique on a glass substrate and under vacuum of 10 -5 mbar with rate of deposition 25nm/sec. The prepared thin films subjected to heat treatment at different temperatures(300, 373 , 473 and 573 K)for half an hour. The structures of Cu2S powder and films have been studied by X–ray diffraction technique. The result reveals that the preparedalloy has cubic structure a

These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about t

As a well-known oral and intravenous antifungal, voriconazole (VRN) has an extensive history of usage in the medical field. Solid lipid nanoparticles (SLNs) have been produced to treat ocular fungal keratitis in the eye. A 32Box-behnken design was used to produce a variety of new formulas for hot-melt extrusion. The SLNs were evaluated by entrapment efficiency (EE percent), particle size (PS), polydispersity index (PDI), and zeta potential (ZP). A series of in-vitro and in-vivo studies were carried out on the new formula. The produced vesicles’ EE, PS, PDI, and ZP values were all good. SLNs eye drops were numerically adjusted to include carbopol, a stabilizer, lipids, and a surfactant, among other substances. ZP of -36.5 ± 0.20 m

TiO2 thin films have been deposited at different concentration of
CdO of (x= 0.0, 0.05, 0.1, 0.15 and 0.2) Wt. % onto glass substrates
by pulsed laser deposition technique (PLD) using Nd-YAG laser
with λ=1064nm, energy=800mJ and number of shots=500. The
thickness of the film was 200nm. The films were annealed to
different annealing (423 and 523) k. The effect of annealing
temperatures and concentration of CdO on the structural and
photoluminescence (PL) properties were investigated. X-ray
diffraction (XRD) results reveals that the deposited TiO2(1-x)CdOx
thin films were polycrystalline with tetragonal structure and many
peaks were appeared at (110), (101), (111) and (211) planes with
preferred orientatio