Global optimization methods for calibration and optimization of the hydrologic tank model's parameters

The tank model, a lumped conceptual hydrological model, is well known due to its simplicity of concept, simplicity in computation while achieving forecasting accuracy comparable with more sophisticated models. However, the calibration of the hydrologic tank model required much time and effort to obtain better results through trial and error method. With the development of artificial intelligence, three probabilistic Global Optimization methods namely Genetic Algorithm (GA), Shuffle Complex Evolution (SCE) and Particle Swarm Optimization (PSO) were adopted for model calibration. The objective of the study is to find the best type of Global Optimization Methods and the best configuration to calibrate tank model that will produce the best fit between the observed and simulated runoff. The selected study area is Bedup Basin, located at Samarahan Division, Sarawak. Input data used for model calibration is a single storm event. The optimal parameters obtained will then be validated with 11 other single storm events. The performance of the optimization techniques is measured using Coefficient of Correlation (R) and Nash-Sutcliffe coefficient (E 2 ). Results show that all three probabilitic GOMs are able to obtain optimal value for 10 parameters of tank model. However, the best GOMs for hourly runoff simulation is PSO. SCE appeard to be the second best performance GOMs and the least performed is GA technique

Evacuation routing optimizer (EROP) / Azlinah Mohamed … [et_al.]

This report presents the solution to the two of the most critical processes in planning for flash Hood evacuation: the evacuation vehicle assignment problem (EVAP) and the evacuation vehicle routing problem (EVRP). With these solutions, the evacuation routing optimizer (EROP) is constructed. The EVAP is firstly solved, followed by the EVRP. For EVAP, discrete particle position is proposed to support the implementation of discrete particle swarm optimization called myDPSOVAP-A. Particle positions are initially calculated based on the average passenger capacity of each evacuation vehicle. We experiment with different numbers of the potential flooded areas (PFA) using two types of sequences for vehicle capacity; random and sort ascending order. Both of these sequences are tested with different inertia weights, constriction coefficients (CF), and acceleration coefficients. We analyse the performance of each vehicle allocation in four experiment categories: myDPSOVAP-A using inertia weight with random vehicle capacity, myDPSOVAP-A using inertia weight with sort ascending order of vehicle capacity; myDPSOVAP-A using CF with random vehicle capacity, and myDPSOVAP-A using CF with sort ascending of vehicle capacity. Flash flood evacuation datasets from Malaysia are used in the experiment. myDPSOVAP-A using inertia weight with random capacity was found to give the best results for both random and sort ascending order of vehicle capacity. Solutions reached by analyses with CF random and inertia weight sorted in ascending order were shown to be competitive with those obtained using inertia weight with random capacity. Overall, myDPSOVAP-A outperformed both a genetic algorithm with random vehicle capacity and a genetic algorithm with sort ascending order of vehicle capacity in solving the EVAP. Consequently EVRP, myDPSOVRPl is modified and named as myDPSO_VRP_2, adopts a new solution mapping which incorporates a graph decomposition and random selection of priority value. The purpose of this mapping is to reduce the searching space of the particles, leading to a better solution. Computational experiments involve EVRP dataset from road network for flash flood evacuation in Johor State, Malaysia. The myDPSOVRPl and myDPSO_VRP_2 are respectively compared with a genetic algorithm (GA) using solution mapping for EVRP. The results indicate that the proposed myDPSO_VRP_2 are highly competitive and show good performance in both fitness value and processing time. Overall, DPSOVRP2 and myDPSOVAP-A which are the main component in the EROP gave good performance in maximizing the number of people to vehicles and minimizing the total travelling time from vehicle location to PFA. EROP was embedded with the DPSOVRP2 and retrieved the generated capacitated vehicles from the myDPSOVAP-A. EROP is also accommodated with the routing of vehicles from PFA to relief centres to support the whole processes of the evacuation route planning

Spectroscopic and Thermal Studies of Palladium (II) Complex of N- (5-methylpyridin-2-ylcarbamothiol) Cinnamamide Ligand

Abstract N-(5-methylpyridin-2-ylcarbamothiol) cinnamamide ligand (L1) and dichlor

Spectroscopic and thermal studies of palladium (II) Complex of N-(5-methylpyridin-2-ylcarbamothiol) cinnamamide ligand

N-(5-methylpyridin-2-ylcarbamothiol) cinnamamide ligand (L1) and dichloro (N-(5-methylpyridine-2-yl-carbamothiol)cinnamamide –к2O,S) palladium (II) (ML1) were successfully synthesised and characterized by several typicalspectroscopic and analytical techniques namely Infra-Red (IR) Spectroscopy,1H and13C Nuclear Magnetic Resonance andThermogravimetric Analysis (TGA). The Infrared spectrum for L1 shows four bands of interest namelyν(N-H),ν(C=O),ν(C-N),ν(C=N) andν(C=S) which can be observed at 3247cm-1, 1682cm-1, 1473cm-1, 1541cm-1and 764cm-1respectivelywhile for the designated metal complex, ML1 the values fall at 3227cm-1, 1689cm-1, 1492cm-1, 1542cm-1and 774cm-1respectively. In1H NMR spectra for the compound L1 and ML1 show protons for N-H which can be observed atδH10.11ppm, 13.02ppm andδH8.71ppm, 8.99ppm while the13C NMR spectra for these compounds, the signal of C=O and C=Scan be observed atδC177ppm, 164ppm andδC173ppm,166ppm. Whilst, in thermogravimetric analysis, compounds L1 andML1 started to degrade at temperature 162.14°C (80%weight of sample) and 186.15°C (74 % weight of sample)respectively

Understanding of antibiotic use and resistance among final-year pharmacy and medical students: a pilot study

Introduction: This study is aimed to investigate the understanding of antibiotic use and antibiotic resistance and its correlate factors among final-year medical and pharmacy students at International Islamic University Malaysia (IIUM). Methodology: This was a cross-sectional study. The study instrument was developed by extensive literature search and was subjected to face validity and content validity to medical and pharmacy academics. A pilot study was conducted to ascertain the reliability coefficient. Data was entered to SPSS version 17 and descriptive and inferential statistics were applied. Results: A total of 123 questionnaires were included in the study. Out of 123 respondents, 58.5% (n = 72) were final-year medical students, while 41.5% (n = 51) were final-year pharmacy students. The majority of the respondents showed adequate knowledge regarding the course contents related to antibiotics (n = 116; 94.3%). Almost all the respondents correctly reported the difference between bactericidal and bacteriostatic antibiotics. Only 15.4% (n = 19) and 27.6% (n = 34) of students were able to recognize Streptococcus pyogenes as non-pencillin resistant bacterium and Enterococcus as vancomycin-resistant bacterium, respectively. Conclusions: The students showed good understanding regarding antibiotic resistance. In comparison to medical students, pharmacy students showed better understanding and more adequate knowledge, as the mean value for each domain was slightly higher for pharmacy students. Extensively improving the curriculum and educating healthcare professionals, especially physicians and pharmacists, right from the time of their educational training can inculcate a moral responsibility toward the judicious use of antibiotics, which can serve to eradicate antibiotic resistance