This paper aims at discovering the real implication of deduction in the Arabic culture
with concentration on its applications in Arabic grammar, logic, and fundamentals of Islamic
legislations. Some light has been shed on deduction in the Arabic culture but most of recent
works did not analyze deduction according to the pragmatic analysis. This paper will answer
the following questions:
to what extent deduction in Arabic grammar could comprehend with deduction l logic and
fundamentals of thinking in Islamic thought?
how can we find the deduction thinking in the Qur’anic surah if Ghafir?
Can we find parts of deduction in the surah?
The methodology in this paper is descriptive analytical. This methodology has been
chosen according to the pragmatic thinking which concentrates on description and analysis
simultaneously.
The following results have been ended up with:
A comprehensive thinking collection between grammar, fundamental research, and logic in
the applications of the Arabic culture.
This collection seems to be clear in their understanding in the Qur’anic text including the
referred to surah.
The following part of deduction is clearly understood from the surah of Ghafir: the gradual,
backward, complex, and pronominal deductions.
We aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measure
Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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