King Fahd University of Petroleum and Minerals

About: King Fahd University of Petroleum and Minerals is a education organization based out in Dhahran, Saudi Arabia. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 7603 authors who have published 24030 publications receiving 443803 citations. The organization is also known as: College of Petroleum and Minerals.

Watermelon waste products as green corrosion inhibitors for mild steel in HCl solution

Abstract: Three agro-waste products namely; watermelon rind extract (WMRE), watermelon seed extract (WMSE) and watermelon peel extract (WMPE) were investigated for their corrosion inhibition potentials for mild steel in HCl solution using electrochemical techniques at 25 °C. All the extracts inhibited acid-induced corrosion of mild steel in the order WMSE > WMRE > WMPE. Corrosion inhibition effect was found to be dependent on extracts’ concentrations. Inhibitive action of the extracts could be attributed to adsorption of the extracts’ components on mild steel surface by physisorption mechanism according to Langmuir adsorption isotherm model and functions as mixed-typed inhibitors as revealed by potentiodynamic polarization studies.

Null steering in phased arrays by controlling the element positions

Abstract: Null steering methods usually involve costly and complicated amplitude and/or phase control systems. A technique is presented for null steering based on the element position perturbations. The technique frees the phase shifters to be used solely for steering the main beam toward the direction of the desired signal. It also removes the limitations of the other techniques by independently steering the main beam and the nulls to arbitrary independent directions. This technique is also capable of obtaining sidelobe cancellation and wideband signal rejection. >

Discovering knowledge from fuzzy concept lattice

Abstract: Since its inception, association rule mining has become one of the core data mining tasks, and has attracted tremendous interest among researchers and practitioners. Many efficient algorithms have been proposed in the literature, e.g., Apriori, Partition, DIC, for mining association rules in the context of marketbasket analysis. They are all based on apriori methods, i.e., pruning the itemset lattice, and requires multiple database accesses. However, research so far has mainly focused on mining over binary data, i.e., either an item is present in a transaction or not. Little attention was paid to mining over data where the quantity of items is considered. In this paper, we propose to address the problem of mining fuzzy association rules, by considering the quantity of items in the transactions. After the fuzzification of the transaction database, we apply a new efficient algorithm, called FARD (Fuzzy Association Rule Discovery), for mining fuzzy association rules. FARD is based on the pruning of the fuzzy concept lattice, and can be applied equally to classical or fuzzy databases, by scanning the database only once.