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

Toxic substances have been released into water supplies in recent decades because of fast industrialization and population growth. Fenton electrochemical process has been addressed to treat wastewater which is very popular because of its high efficiency and straightforward design. One of the advanced oxidation processes (AOPs) is electro-Fenton (EF) process, and electrode material significantly affects its performance. Nickel foam was chosen as the source of electro-generated hydrogen peroxide (H₂O₂) due to its good characteristics. In the present study, the main goals were to explore the effects of operation parameters (FeSO₄ concentration, current density, and electrolysis time) on the catalytic perform

Abstract  

All central air conditioning systems contain piping system with various components, sizes, material, and layouts. If such systems in operating mode, the flow in piping system and its component such as valves can produce severe vibration due to some flow phenomenon’s. In this research, experimental measurements and numerical simulation are used to study the flow-induced vibration in valves. Computational fluid dynamics (CFD) concepts are included with one-way and two-way fluid-structure interaction concepts by using finite element software Package (ANSYS 14.57). Detection analysis is performed on flow characteristics under operation conditions and relations with structural vibration. Most of

The addition of new reactive sites on the surface area of the inert sand, which are represented by layered double hydroxide nanoparticles, is the primary goal of this work, which aims to transform the sand into a reactive material. Cetyltrimethylammonium bromide (CTAB) surfactant is used in the reaction of calcium extracted from solid waste-chicken eggshells with aluminum prepared from the cheapest coagulant-alum. By separating amoxicillin from wastewater, the performance of coated sand named as "sand coated with (Ca/Al-CTAB)-LDH" was evaluated. Measurements demonstrated that pH of 12 from 8, 9, 10, 11, and 12, CTAB dosage of 0.05 g from 0, 0.03, 0.05, and 0.1 g, ratio of Ca/Al of 2 from 1, 2, 3, and 4, and mass of sand of 1 g/50 mL from

This study proposes a pioneering Ethical Artificial Intelligence (EAI) framework for advancing sustainable development in Iraq by integrating eight multidimensional sustainability indicators—administrative, technological, economic, environmental, social, legal, security, and governance. Utilizing data from 60 completed development projects, the framework combines SPSS statistical analysis, the SMART-AI model, and Artificial Neural Networks (ANN) to identify key determinants of project success and failure. Results reveal a 37% project failure rate, with administrative and technological deficiencies emerging as the most influential predictors. The SMART-AI model achieved an accuracy of 91.3% using stratified k-fold cross-validation. A bilin

This paper presents a numerical analysis using ANSYS finite element program to simulate the reinforced concrete slabs with spherical voids. Six full-scale one way bubbled slabs of (3000mm) length with rectangular cross-sectional area of (460mm) width and (150mm) depth are tested as simply supported under two-concentrated load. The results of the finite element model are presented and compared with the experimental data of the tested slabs. Material nonlinearities due to cracking and crushing of concrete and yielding of reinforcement are considered. The general behavior of the finite element models represented by the load-deflection curves at midspan, crack pattern, ultimate load, load-concrete strain curves and failure m

Nanofluids, liquid suspensions of nanoparticles (Np), are an effective agent to alter the wettability of oil-wet reservoirs to water-wet thus promoting hydrocarbon recovery. It can also have an application to more efficient carbon storage. We present a series of contact angle (θ) investigations on initially oil-wet calcite surfaces to quantify the performance of hydrophilic silica nanoparticles for wettability alteration. These tests are conducted at typical in-situ high pressure (CO2), temperature and salinity conditions. A high pressure–temperature (P/T) optical cell with a regulated tilted surface was used to measure the advancing and receding contact angles at the desired conditions. The results showed that silica nanofluids can alte

Web testing is very important method for users and developers because it gives the ability to detect errors in applications and check their quality to perform services to users performance abilities, user interface, security and other different types of web testing that may occur in web application. This paper focuses on a major branch of the performance testing, which is called the load testing. Load testing depends on an important elements called request time and response time. From these elements, it can be decided if the performance time of a web application is good or not. In the experimental results, the load testing applied on the website (http://ihcoedu.uobaghdad.edu.iq) the main home page and all the science departments pages. In t

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we