The precipitation of calcite induced via microorganisms (MICP) is a technique that has been developed as an innovative sustainable ground improvement method utilizing ureolytic bacteria to soil strengthening and stabilization. Locally isolated Bacillus Sonorensis from Iraqi soil samples were found to have high abilities in producing urease. This study aims to use the MICP technique in improving the undrained shear strength of soft clay soil using two native urease producing bacteria that help in the precipitation of calcite to increase the cementation between soil particles. Three concentrations of each of the locally prepared Bacillus sonorensis are used in this study for cementation reagent (0.25M, 0.5M, and 1M) during

Nowadays power systems are huge networks that consist of electrical energy sources, static and lumped load components, connected over long distances by A.C. transmission lines. Voltage improvement is an important aspect of the power system. If the issue is not dealt with properly, may lead to voltage collapse.  In this paper, HVDC links/bipolar connections were inserted in a power system in order to improve the voltage profile. The load flow was simulated by Electrical Transient Analyzer Program (ETAP.16) program in which Newton- Raphson method is used. The load flow simulation studies show a significant enhancement of the power system performance after applying HVDC links on Kurdistan power systems. Th

Abstract

    This work deals with a numerical investigation to evaluate the utilization of a water pipe buried inside a roof to reduce the heat gain and minimize the transmission of heat energy inside the conditioning space in summer season.     The numerical results of this paper showed that the reduction in heat gain and energy saving could be occurred with specific values of parameters, like the number of pipes per square meter, the ratio of pipe diameter to the roof thickness, and the pipe inlet water temperature. Comparing with a normal roof (without pipes), the results indicated a significant reduction in energy heat gain which is about 37.8% when the number of pipes per m

In this work, a magnetic switch was prepared using two typesof ferrofluid materials, the pure ferrofluid and ferrofluid doped with copper nanoparticles (10 nm). The critical magnetic field (Hc) and the state of magnetic saturation (Hs) were studied using three types of laser sources. The main parameters of the magnetic switch measured using pure ferrofluid and He-Ne Laser source were Hc(0.5 mv, 0.4 G), Hs (8.5 mv, 3 G). For the ferrofluid doped with copper nanoparticles were Hc (1 mv, 4 G), Hs (15 mv, 9.6 G), Using green semiconductor laser for the Pure ferrofluid were Hc (0.5 mv, 0.3 G) Hs (15 mv, 2.9 G). While the ferrofluid doped with copper nanoparticles were Hc (0.5 mv, 1 G), Hs (12 mv, 2.8 G) and by using the violet semiconductor l

Students’ feedback is crucial for educational institutions to assess the performance of their teachers, most opinions are expressed in their native language, especially for people in south Asian regions. In Pakistan, people use Roman Urdu to express their reviews, and this applied in the education domain where students used Roman Urdu to express their feedback. It is very time-consuming and labor-intensive process to handle qualitative opinions manually. Additionally, it can be difficult to determine sentence semantics in a text that is written in a colloquial style like Roman Urdu. This study proposes an enhanced word embedding technique and investigates the neural word Embedding (Word2Vec and Glove) to determine which perfo

The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

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

This paper is concerned with the numerical solutions of the vorticity transport equation (VTE) in two-dimensional space with homogenous Dirichlet boundary conditions. Namely, for this problem, the Crank-Nicolson finite difference equation is derived.  In addition, the consistency and stability of the Crank-Nicolson method are studied. Moreover, a numerical experiment is considered to study the convergence of the Crank-Nicolson scheme and to visualize the discrete graphs for the vorticity and stream functions. The analytical result shows that the proposed scheme is consistent, whereas the numerical results show that the solutions are stable with small space-steps and at any time levels.

Renewable energy technology is growing fast especially photovoltaic (PV) system to move the conventional electricity generation and distribution towards smart grid. However, similar to monthly electricity bill, the PV energy producers can only monitor their energy PV generation once a month. Any malfuntion in PV system components may reduce the performance of the system without notice. Thus, developing a real-time monitoring system of PV production is very crucial for early detection. In addition, electricity consumption is also important to be monitored more frequently to increase energy savings awareness among consumers. Hardware based Internet-of-Thing (IoT) monitoring and control system is widely used. However, the implementation of

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D