A comparative study was carried out to evaluate alkaloid antibacterial activity which was extracted from the root bark Punica granatum L. by liquid membrane techniques (SA) and organic solvent traditional techniques (SB). The screening of the antimicrobial activity was conducted by agar well diffusion method against Staphylococcus aureus, Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae, and Proteus mirabilis at three concentration levels (5, 10 and 15 mg/ml). Alkaloid extracts were analyzed by a high performance liquid chromatography (HPLC) method. Among the tested extractions, SB showed the highest antibacterial activity against all five bacterial strains, especially at 15 mg/ml concentration. However, all the B type solution

Copula modeling is widely used in modern statistics. The boundary bias problem is one of the problems faced when estimating by nonparametric methods, as kernel estimators are the most common in nonparametric estimation. In this paper, the copula density function was estimated using the probit transformation nonparametric method in order to get rid of the boundary bias problem that the kernel estimators suffer from. Using simulation for three nonparametric methods to estimate the copula density function and we proposed a new method that is better than the rest of the methods by five types of copulas with different sample sizes and different levels of correlation between the copula variables and the different parameters for the function. The

The development of Web 2.0 has improved people's ability to share their opinions. These opinions serve as an important piece of knowledge for other reviewers. To figure out what the opinions is all about, an automatic system of analysis is needed. Aspect-based sentiment analysis is the most important research topic conducted to extract reviewers-opinions about certain attribute, for instance opinion-target (aspect). In aspect-based tasks, the identification of the implicit aspect such as aspects implicitly implied in a review, is the most challenging task to accomplish. However, this paper strives to identify the implicit aspects based on hierarchical algorithm incorporated with common-sense knowledge by means of dimensionality reduction.

This paper proposes a collaborative system called Recycle Rewarding System (RRS), and focuses on the aspect of using information communication technology (ICT) as a tool to promote greening. The idea behind RRS is to encourage recycling collectors by paying them for earning points. In doing so, both the industries and individuals reap the economical benefits of such system. Finally, and more importantly, the system intends to achieve a green environment for the Earth. This paper discusses the design and implementation of the RRS, involves: the architectural design, selection of components, and implementation issues. Five modules are used to construct the system, namely: database, data entry, points collecting and recording, points reward

The physical substance at high energy level with specific circumstances; tend to behave harsh and complicated, meanwhile, sustaining equilibrium or non-equilibrium thermodynamic of the system. Measurement of the temperature by ordinary techniques in these cases is not applicable at all. Likewise, there is a need to apply mathematical models in numerous critical applications to measure the temperature accurately at an atomic level of the matter. Those mathematical models follow statistical rules with different distribution approaches of quantities energy of the system. However, these approaches have functional effects at microscopic and macroscopic levels of that system. Therefore, this research study represents an innovative of a wi

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

Background: Many types of instruments and techniques are used in the instrumentation of the root canal system. These instruments and techniques may extrude debris beyond the apical foramen and may cause post-instrumentation complications. The aim of this study was to evaluate the amount of apically extruded debris resulted by using 4 types of nickel-titanium instruments (WaveOne, TRUShape 3D conforming files, Hyflex CM, and One Shape files) during endodontic instrumentation. Materials and methods: Forty freshly extracted human mandibular second premolar with straight canals and a single apex were collected for this study. All teeth were cut to similar lengths. Pre-weighted glass vials were used as collecting containers. Samples were randoml

Background. Endodontic infections caused by remaining biofilm following disinfection with chemical fluids encourage secondary bacterial infection; hence, employing laser pulses to activate the fluids is advised to improve microbial biofilm clearance. This study investigated the performance of Er,Cr:YSGG laser in photon-induced photoacoustic streaming (PIPS) agitation of 5.25% sodium hypochlorite (NaOCl) to enhance the removal of mature Enterococcus faecalis (E. faecalis) biofilms in complex root canal systems. Methods. The mesial roots of the lower first and second molars were separated and inoculated with E. faecalis bacteria for 30 days. The roots were irrigated with 5.25% NaOCl, some of them were agitated with passive ultrasonic