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Highlight: A new optimization algorithm inspired by how duelist improve their skill in duel  In duelist algorithm, different treatment is given to each duelist based on the duel result  Duelist algorithm provided good result compared to the other optimization algorithms such as genetic algorithm, particle swarm optimization algorithm and imperialist competitive algorithm

A Neural Network Internal Model Control (NN-IMC) strategy is investigated, by establishing inverse and forward model based neural network (NN). Further for developing the model has been selected suitable adaptive filter. Two types of NN-based inverse model (i.e. with and without disturbance input) were accurately simulated. The results indicated that the neural networks are capable to establish forward and inverse model rapidly from the couple of input-output open loop data of single distillation column binary system with a good root mean square error (RMSE). The simulation results revealed that NN-IMC with appropriate learning rate -momentum is capable to pursue the set-point changes and to reject the disturbance changes without steady state error or oscillations. NN-IMC with inverse model which contains disturbance input (modified NN-IMC) offer better performance than without it (conventional NN-IMC).

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Highlight: A new optimization algorithm inspired by how duelist improve their skill in duel  In duelist algorithm, different treatment is given to each duelist based on the duel result  Duelist algorithm provided good result compared to the other optimization algorithms such as genetic algorithm, particle swarm optimization algorithm and imperialist competitive algorithm Abstract-This paper proposes an optimization algorithm based on how human fight and learn from each duelist. Since this algorithm is based on population, the proposed algorithm starts with an initial set of duelists. The duel is to determine the winner and loser. The loser learns from the winner, while the winner try their new skill or technique that may improve their fighting capabilities. A few duelists with highest fighting capabilities are called as champion. The champion train a new duelists such as their capabilities. The new duelist will join the tournament as a representative of each champion. All duelist are re-evaluated, and the duelists with worst fighting capabilities is eliminated to maintain the amount of duelists. Two optimization problem is applied for the proposed algorithm, together with genetic algorithm, particle swarm optimization and imperialist competitive algorithm. The results show that the proposed algorithm is able to find the better global optimum and faster iteration.

The aim of the study was to evaluate antimicrobial activity of extract of Jengkol (Pithecellobium lobatum Benth.) and Petai (Parkia spesioca Hassk) peel as natural antimicrobial to inhibit Listeria monocytogenes. By using Minimum Inhibitory Concentration (MIC), the antimicrobial activity of Jengkol peel and Petai peel extract was 0.78 % and 0.39 %, respectively. By using Inhibition zone, the antimicrobial activity of Jengkol peel and petai peel extract was 1,08 ± 0,07 and 3,13 ± 0,13 mm. Phytochemical compounds of jengkol and petai were apigenin, coumaric acid, gallic acid, kaempferol, hesperidin, luteolin, naringenin, quercetin. Myricetin was found on jengkol but it was not found in petai peel extract. Antimicrobial activity of petai peel extract was more effective to inhibit Listeria monocytogenes than jengkol peel extract.

A total of 43 Salmonella enterica isolates belonging to different serovars (Salmonella Albany, Salmonella Agona, Salmonella Corvallis, Salmonella Stanley, Salmonella Typhimurium, Salmonella Mikawasima, and Salmonella Bovismorbificans) were isolated from catfish (Clarias gariepinus) and tilapia (Tilapia mossambica) obtained from nine wet markets and eight ponds in Penang, Malaysia. Thirteen, 19, and 11 isolates were isolated from 9 of 32 catfish, 14 of 32 tilapia, and 11 of 44 water samples, respectively. Fish reared in ponds were fed chicken offal, spoiled eggs, and commercial fish feed. The genetic relatedness of these Salmonella isolates was determined by random amplified polymorphic DNA PCR (RAPD-PCR) using primer OPC2, repetitive extragenic palindromic PCR (REP-PCR), and pulsed-field gel electrophoresis (PFGE). Composite analysis of the RAPD-PCR, REP-PCR, and PFGE results showed that the Salmonella serovars could be differentiated into six clusters and 15 singletons. RAPD-PCR differentiated the Salmonella isolates into 11 clusters and 10 singletons, while REP-PCR differentiated them into 4 clusters and 1 singleton. PFGE differentiated the Salmonella isolates into seven clusters and seven singletons. The close genetic relationship of Salmonella isolates from catfish or tilapia obtained from different ponds, irrespective of the type of feed given, may be caused by several factors, such as the quality of the water, density of fish, and size of ponds.

The tools to predict the growth of bacteria over the time is essential to maintain the process stability in bio processes. Currently, not all tools have been fully used to fulfil these interests which can be applied in industry and laboratory. In this paper, a mathematical modelling approach based on the type of multi layer perceptron artificial neural network created by Finite Impulse Response (FIR) is proposed. The neural network model was developed using data collected from laboratory work. A total of 75% the growth of bacteria (S. Aureus, B. Cereus and S. Typhimurium) which is inhibited by lemon basil waste extract, over the time data are used to train Artificial Neural Network (ANN), and the rest of the data are used to validate the model. ANN has been model the growth of S. Aureus, B. Cereus and S. Typhimurium which is inhibited by lemon basil waste extract over the time. Mean Square Error (MSE) results during training and validation obtained from this modeling were 0.087 and 0.147, respectively. It means the mathematical modeling approach used in this study is suitable for capturing nonlinear characteristics of bacterial growth that is inhibited by lemon basil waste extract.