Doxycycline hyclate is an antibiotic drug with a broad‐spectrum activity against a variety of gram‐positive and gram‐negative bacteria and is frequently used as a pharmacological agent and as an effector molecule in an inducible gene expression system. A sensitive, reliable and fast spectrophotometric method for the determination of doxycycline hyclate in pure and pharmaceutical formulations has been developed using flow injection analysis (FIA) and batch procedures. The proposed method is based on the reaction between the chromogenic reagent (V4+) and doxycycline hyclate in a neutral medium, resulting in the formation of a yellow compound that shows maximum absorbance at 396 nm. In a batch procedure, the proposed method was validated over the concentration range of 1.0–80 μg mL−1 with a sampling frequency of 30/h, and commercial pharmaceutical samples were successfully determined. The proposed method was successfully adapted with an FIA system where the peak heights are proportionally connected to doxycycline hyclate over the concentration range of 25–400 μg mL−1 with a sampling frequency of 50/h. The limits of detection (LOD) and quantification (LOQ) were 0.9 and 10.44 μg mL−1 and 3.01 and 34.81 μg mL−1 for batch and FIA respectively. The samples were submitted to an HPLC analysis, and the outcomes demonstrated excellent agreement with the suggested procedures. The adopted FIA procedure allows fast monitoring of doxycycline hyclate in pharmaceutical formulations and it can be used for quality control purposes during the production processes of doxycycline hyclate.
Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
... Show MoreGas hydrate formation is considered one of the major problems facing the oil and gas industry as it poses a significant threat to the production, transportation and processing of natural gas. These solid structures can nucleate and agglomerate gradually so that a large cluster of hydrate is formed, which can clog flow lines, chokes, valves, and other production facilities. Thus, an accurate predictive model is necessary for designing natural gas production systems at safe operating conditions and mitigating the issues induced by the formation of hydrates. In this context, a thermodynamic model for gas hydrate equilibrium conditions and cage occupancies of N2 + CH4 and N2 + CO4 gas mix
Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD
... Show MoreThe rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com
... Show More Sixteen new complexes with the general formula [M(L)2(H2O)2] were prepared resulting from the reaction of the two new Schiff base ligands, which are: - L1= (E)-5-((2-hydroxybenzylidene)amino)-2-phenyl-2,4-dihydro-3H-pyrazol-3-one) L2 = (E)-5-((2-hydroxy-3-methoxybenzylidene)amino)-2-phenylpyrazolidin-3-one) With divalent metal ions (manganese, cobalt, nickel, copper, zinc, cadmium, mercury) and (tetravalent platinum). Ligands was derived from the reaction of the amine (5-amino-2-phenyl-2,4-dihydro-3H-pyrazol-3-one) with Salicylaldehyde and ortho-vanillin, which is linked to the metal ions via the nitrogen atoms are the isomethene group and the oxygen is the hydroxide group of t
... Show MoreTexture synthesis using genetic algorithms is one way; proposed in the previous research, to synthesis texture in a fast and easy way. In genetic texture synthesis algorithms ,the chromosome consist of random blocks selected manually by the user .However ,this method of selection is highly dependent on the experience of user .Hence, wrong selection of blocks will greatly affect the synthesized texture result. In this paper a new method is suggested for selecting the blocks automatically without the participation of user .The results show that this method of selection eliminates some blending caused from the previous manual method of selection.
Decision making is vital and important activity in field operations research ,engineering ,administration science and economic science with any industrial or service company or organization because the core of management process as well as improve him performance . The research includes decision making process when the objective function is fraction function and solve models fraction programming by using some fraction programming methods and using goal programming method aid programming ( win QSB )and the results explain the effect use the goal programming method in decision making process when the objective function is
fraction .
in this paper the collocation method will be solve ordinary differential equations of retarted arguments also some examples are presented in order to illustrate this approach
This paper proposes a novel method for generating True Random Numbers (TRNs) using electromechanical switches. The proposed generator is implemented using an FPGA board. The system utilizes the phenomenon of electromechanical switch bounce to produce a randomly fluctuated signal that is used to trigger a counter to generate a binary random number. Compared to other true random number generation methods, the proposed approach features a high degree of randomness using a simple circuit that can be easily built using off-the-shelf components. The proposed system is implemented using a commercial relay circuit connected to an FPGA board that is used to process and record the generated random sequences. Applying statistical testing on th
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