Chemical Adsorption Data’s, Temperature Effect and Structural Properties of Artemether-Lumefantrine Corrosion Inhibition Properties on Structural Steel in 0.62M NaCl

Chemical and marine components are faced with corrosion resulting from salty media in an application, which ends up in serious catastrophe. In an attempt to provide and curtail this challenge of toxic use of inorganic inhibitors, properties of Artemether/Lumefantrine was examined as a corrosion inhibitor for mild steel in 0.625M sodium chloride (NaCl) medium. The corrosion propagation under different inhibitor concentrations between 0-20 ml and the temperature difference of 298-323K was examined using linear potentiodynamic polarisation and open circuit potential. The structural pitting evolution was done using a scanning electron microscope (SEM) and energy-dispersive x-ray spectroscopy (EDS) analyses. From the data, the activation energy of the corrosion reaction increased with increasing inhibitor concentration, suggesting that it inhibits corrosion by increasing the energy required to initiate the corrosion reaction. The Gibbs free energies of adsorption fall within the range of-10.08 and-13.87 kJ/mol, which is greater than-20 kJ/mol; thus, the artemether-lumefantrine inhibitor exhibited physical type adsorption. The values of the free energy of adsorption were all negative. The Langmuir Isotherm seems to perform exceptionally well with a correlation efficiency of 0.975 against all other isotherm fits.

A Hybrid Alarm Association Method Based on AP Clustering and Causality

Internet of Things (IoT) brought great convenience to people’s daily lives. Meanwhile, the IoT devices are facing severe attacks from hackers and malicious attackers. Hackers and malicious attackers use various methods to invade the Internet of Things system, causing the Internet of Things to face a large number of targeted, concealed, and penetrating potential threats, which makes the privacy problem of the Internet of Things suffers serious challenges. But the existing methods and technologies cannot fully identify the attacker’s attack process and protect the privacy of the Internet of Things. Alarm correlation method can construct a complete attack scenario and identify the attacker’s intention by alarming the alarm data which provides an effective protection for user privacy. However, the existing alarm correlation methods still have the disadvantages of low correlation accuracy, poor correlation efficiency, and strong dependence on the knowledge base. To address these issues, we propose an alarm correlation method based on Affinity Propagation (AP) clustering algorithm and causal relationship. Our method considers that the alarm data triggered by the same attack process has high similarity characteristics, adopts the AP algorithm to improve the correlation efficiency, and at the same time constructs a complete attack process based on the causal correlation idea. The new alarm correlation method has a high correlation effect and builds a complete attack process to help managers identify attack intentions and prevent attacks.