Expansive soils exist in large areas around the world, especially in the arid and semi-arid regions. In different areas around Kingdom of Saudi Arabia, this problematic soil exists in form of layers especially in the newly developed regions of the country. Construction activities on such soil requires considering precautionary measures in order to avoid and/or minimize the risks associated with this problematic soil. Different research studies proposed measures to improve construction conditions where expansive soil exists. These measures utilize one of two techniques, either the full replacement of expansive soil layers or mitigating the expansive soil with a wide range of additives. In the current study, a new mitigating agent is introduced to be mixed with expansive soil aiming at improving its geotechnical properties; which shall be reflected in its engineering behavior under construction loads. The mitigating agent is the form of Palm Fronds Ash [PFA]. Palm Fronds [PF] is an agricultural waste that is being hugely produced every year in Saudi Arabia as a byproduct of cultivating millions of palm trees. The typical process of disposing such a waste is through open air burning, which causes serious environmental hazards to Saudi communities. In the current study, the PFA is utilized as a stabilizing agent through mixing it with the expansive soil. Remolded samples of expansive soil samples mixed with 4%, 8% and 12% PFA are prepared and tested in order to assess the improvement, if any, in its geotechnical properties. The results of the experimental program conducted on this soil mixture showed improvement of the geotechnical properties of the expansive soil; Atterberg limits, maximum dry density (MDD), unconfined compressive strength (UCS), and the swelling pressure (SP). This study showed that at a 10% content, by weight, of PFA in the expansive soil samples, the soil Liquid Limit is reduced by almost 40%. The MDD increased by almost 5% and the UCS increased by an average value of almost 26% while the swelling pressure decreased by almost 88%.  Moreover, a preliminary empirical equation is presented to estimate the percentage of reduction in swelling pressure as function of the percentage of PFA mixed with the expansive soil.

This paper presents an experimental investigation of the effect of silica fume (SF) in addition to the possibility of using local natural pozzolan (LNP) as fine aggregate replacement in cement mortars on the mechanical properties of cement mortars. The availability of local natural pozzolan in volcanic areas in Al-Madinah Al-Munawarah, KSA stimulated studying the possibility of utilizing LNP as a partial substitution for sand to reduce the density of the concrete and to improve the properties of eco-friendly concrete. The experimental work was carried out on seventy-five cement mortar cubes of 50 mm, nine cubes as control specimens, twelve cubes were made by replacing sand by volume with local natural pozzolan at replacement levels of 10, 20, 30 and 40% without addition of SF, fifteen cubes were made by replacing sand by volume with LNP at replacement levels of 10, 20, 30 and 40% and by replacing 5% of cement with silica fume, fifteen cubes were made by replacing sand by volume with LNP at levels of replacement of 10, 20, 30 and 40% and by replacing 10% of cement with silica fume. Twenty-four cubes were made without sand replacement and with cement replacement by weight with SF at replacement levels of 10, 20, 30 and 40%. The specimens were tested after 7, 14 and 28 days. The addition of SF showed a marked influence on the properties of the produced cement mortar cubes.  

Abstract. Artificial intelligence is a branch of computer science capable of analyzing complex medical data. Their potential to exploit a meaningful relationship within a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. This paper compares Artificial Neural Networks (ANN) applications like Mammogram, ultrasound, Thermal Imaging, MRI.  It also shows the accuracy of Artificial Neural Networks (ANN) to detect breast cancer using the WEKA tool.

Electromagnetic wave propagation of personal communication devices at home has been a source of concern at the current research. In particular, there are a considerable number of peer-reviewed publications which show that mobile phone and Wi-Fi exposed a body affects and case some chronic problems to the human body. The main objective of this project is to design and analyze the effect of electromagnetic exposure on human body based on the trigger rate of 100 via automatic device assessment and warning strategy. The result shows that, a significant value of the electromagnetic wave exposure based on the success rate of 72% throughout 25 trial times of test. However, the system has less accuracy by 28% due to the basic antenna used with integrated Arduino controller to detect the electromagnetic wave. In conclusion, the electromagnetic radiation has a direct effect; especially in long-term may lead to the changes in physiological indices and body immune function. The consequences of this investigation likewise showed that joined impacts of Wi-Fi in addition to cell phone presentation have a higher potential to cause a lot of hurtful impacts.