The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

Massive multiple-input multiple-output (MaMi) systems have attracted much research attention during the last few years. This is because MaMi systems are able to achieve a remarkable improvement in data rate and thus meet the immensely ongoing traffic demands required by the future wireless networks. To date, the downlink training sequence (DTS) for the frequency division duplex (FDD) MaMi communications systems have been designed based on the idealistic assumption of white noise environments. However, it is essential and more practical to consider the colored noise environments when designing an efficient DTS for channel estimation. To this end, this paper proposes a new DTS design by exploring the joint use of spatial channel and n

Nanofluids are proven to be efficient agents for wettability alteration in subsurface applications including enhanced oil recovery (EOR). Nanofluids can also be used for CO2-storage applications where the CO2-wet rocks can be rendered strongly water-wet, however no attention has been given to this aspect in the past. Thus in this work we presents contact angle (θ) measurements for CO2/brine/calcite system as function of pressure (0.1 MPa, 5 MPa, 10 MPa, 15 MPa, and 20 MPa), temperature (23 °C, 50 °C and 70 °C), and salinity (0, 5, 10, 15, and 20% NaCl) before and after nano-treatment to address the wettability alteration efficiency. Moreover, the effect of treatment pressure and temperature, treatment fluid concentration (SiO2 wt%) and

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

Reverse Osmosis (RO) has already proved its worth as an efficient treatment method in chemical and environmental engineering applications. Various successful RO attempts for the rejection of organic and highly toxic pollutants from wastewater can be found in the literature over the last decade. Dimethylphenol is classified as a high-toxic organic compound found ubiquitously in wastewater. It poses a real threat to humans and the environment even at low concentration. In this paper, a model based framework was developed for the simulation and optimisation of RO process for the removal of dimethylphenol from wastewater. We incorporated our earlier developed and validated process model into the Species Conserving Genetic Algorithm (SCG

Engineering equipment is essential part in the construction project and usually manufactured with long lead times, large costs and special engineering requirements. Construction manager targets that equipment to be delivered in the site need date with the right quantity, appropriate cost and required quality, and this entails an efficient supplier can satisfy these targets. Selection of engineering equipment supplier is a crucial managerial process .it requires evaluation of multiple suppliers according to multiple criteria. This process is usually performed manually and based on just limited evaluation criteria, so better alternatives may be neglected. Three stages of survey comprised number of public a

Modified optical fiber sensors received increasing attention because of their superior properties over electrical sensors. These properties include their immunity towards electromagnetic interference and the ability to be deployed in corrosive and volatile environment. Several optical fiber platforms have been developed for chemical sensing applications based on modifying optical fiber cladding layer such as etched, tapered, D-shaped and etched-tapered. The modifications purpose is to extend the evanescent wave propagating out of the core physical dimensions. Thus, evanescent wave interaction with analyte is enhanced. Modified optical transducing platforms are integrated in gas sensing applications, such as ammonia. Modified optical