COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.

Organic pollutants cause many environmental problems to our environment because of their toxicity, non-degradation and ability to long-range transport. The most common organic pollutants are known as persistent organic pollutants (POPs) and are known as hydrocarbons. Effective techniques for the removal of hydrocarbons and heavy metals from soil have drawn great attention. Remediation techniques represent one of the most important of these techniques because of their gentle impact on the environment. The study highlights numerous methods for Physical and chemical remediation techniques with explanation of the ability of some plants and agricultural wastes for remediation.

Nowadays, companies are recognizing their primordial roles and responsibilities towards the protection of the environment and save the natural resources. They are focusing on some contemporary activities such as Reverse Logistics which is economically and environmentally viable. However, the integration of such an initiative needs flows restructuring and supply chain management in order to increase sustainability and maximize profits. Under this background, this paper addresses an inventory control model for a reverse logistics system that deals with two separated types of demand, for new products and remanufactured products, with different selling prices. The model consists of a single shared machine between production and remanufacturing operations, while the machine is subject to random failures and repairs. Three stock points respectively for returns, new products and remanufactured products are investigated. Meanwhile, in this paper, a modeling of the problem with Discrete-Event simulation using Arena® was conducted. Regarding the purpose of finding, a near-optimal inventory control policy that minimizes the total cost, an optimization of the model based on Tabu Search and Genetic Algorithms was established. Computational examples and sensitivity analysis were performed in order to compare the results and the robustness of each proposed algorithm. Then the results of the two methods were compared with those of OptQuest® optimization tool.

Many of mimic analogues synthesized before depending on the change in the structure of aromatic rings. In this work, the carbonyl group in the structure of compounds 1-4 converted to thiocarbonyl group, and then studying the toxicological activity due to chemical change in the active center of mimic analogues was performed for compounds N-tert-butyl-2,4-dichloro-N'-(2,4-dichlorobenzoyl)benzohydrazide (2) and N-tert-butyl-2,4-dichloro-N'-[(2,4-dichlorophenyl)carbonothioyl]benzenecarbothiohydrazide (6). The toxicological study was done by using 2nd and 4th instar larvae of the cotton leaf worm, Spodoptera littoralis (Boisd.). Five concentration levels (600, 300, 150, 75 and 37.5 ppm) of compounds (2) and (6) were applied on the fresh plant food to the newly grown (2^nd and 4^th) instar larvae.

Red jasmine rice is recognized as a healthy food with high phenolic compounds. These compounds present antibacterial and anti-free radical properties. Moreover, colored rice exhibits a biological activity against anticancer. Objectives of this study are 1) exploring a biological screening and cell viability of 70% ethanol and aqueous extracts of red jasmine rice, 2) investigating cytotoxicity to fibroblast NIH3T3 (IC80) that is one hundred cells were found cell viability 80 cells. Red jasmine rice extracts were dried and transformed into a powder using the freeze-drying method. The extracts were treated with fibroblast NIH3T3 for MTT. The highest of IC50 of red jasmine rice extract to scavenge the DPPH and ABTS radicals was found in ethanol extract (53.20±7.37 and 64.17±5.76, respectively). The experiment showed that the ethanol and aqueous extract of red rice did not show cytotoxicity to fibroblast NIH3T3 (IC80). The extracts of red rice show the biological screening of anti-oxidation with total phenolic compounds and flavonoid contents. Moreover, it does not modify the physical properties of the cream formula. It can be concluded that the red rice extract is highly promising for the value addition.

The power-law memory effect is taken into consideration in a generalisation of the economic model of natural growth. The memory effect refers to a process's reliance on its current state and its history of previous changes. However, the study that focuses on natural growth in economics considering the memory effect with fractional order-linear differential equation model is still limited. The current investigation seeks to solve the natural growth with memory effect in the economics model and decide the best model using fractional differential equation (FDE), namely Adomian Decomposition and Variational Iteration Methods. Also, this study assumes the level of consumer loss memory during a certain time interval denoted by a parameter (α). This study showed the model of loss memory effect with 0 < α ≤ 1 given a slowdown in output growth compared to a model without memory effect. Besides that, this study also found that output Y(t) is growing faster with the Variational Iteration method compared to the Adomian decomposition method. Also, using graphical simulation, this study found the output Y(t) is closer to the exact solution with α=0.4 and α=0.9. In conclusion, this study successfully solved natural growth with memory effect in economics and decided the best model between FDE, namely Adomian decomposition and Variational iterative methods using numerical analysis.

A series of some new pyrazole-substituted 7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazines was synthesized in this study. The structures of target substances were confirmed by using 1H and 13С NMR spectroscopy, mass spectrometry and elemental analysis. The synthesized compounds have been evaluated for antimicrobial activity against five bacterial strains (Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Staphylococcus aureus) and two fungal strains (Candida albicans and Cryptococcus neoformans). The antimicrobial screening studies of synthesized substances established that 2 of 12 compounds show pronounced antibacterial activity against the strain Staphylococcus aureus.

The Indonesian Navy is the spearhead in maintaining maritime security in Indonesian waters. In carrying out its main tasks, the Indonesian Navy has components of an Integrated Fleet Weapon System in which there are elements of Ships and Naval Bases. To ensure the effectiveness of carrying out operations by ship elements, ship operations are supported by the Naval Base as the organizer of the support function. Naval Base's carrying capacity consists of 5 (five) support functions, including: (1) support for anchoring facilities; (2) support for supply facilities; (3) support for maintenance and repair facilities; (4) support facility maintenance personnel; and (5) support for base development facilities. Naval Base does not yet have its dock to support anchoring facilities for ship operations. In addition to cooperation in the use of the Naval Base anchorage facility, there is also cooperation in port security, both in terms of land and port water aspects. As the number of ship visits at Naval Base Harbor increases, the dock utility increases. The increase in dock utility resulted in a decrease in port services which also resulted in a decrease in the Naval Base Carrying Capacity. To improve port services, Pelindo III implements the port development program contained in the Naval Base Port Master Plan in Permen KP number 792 of 2017. In this study, an analysis of the impact of the Naval Base Port development policy on the carrying capacity of the Naval Base was carried out. The data analysis uses System Dynamics modeling with a simulation period of 30 years in 3 development scenarios, namely short-term scenarios, medium-term scenarios, and long-term scenarios. From the simulation results, it is found that the construction of the Naval Base port affects the Naval Base Carrying Capacity with an average increase of 1.8% in each policy scenario. The increase in Naval Base Carrying Capacity has an effect on increasing Ship Operations by an average of 1.8% and also increasing the Security of Naval Base Harbor by an average of 0.14%. The results of the analysis of this study can be used as consideration for policymaking by the Navy.

A series of azo dyes incorporated acridine chromophore labelled as 8 (a-d), 10 (a, b), 12 and 14 were prepared in very good yields starting from 9-chloroacridine 1 followed by amination, diazotization and coupling either with rhodanine analogues 6 (a, b) or other coupling partners 9 (a, b), 11 and 13. FT-IR, ¹H NMR, and mass spectroscopic analysis were used to establish the structures of the produced azo dispersed dyes. Moreover, the synthesized azo dyes were used to prepare pastes that were used to print polyester fabric using classic silk-screen printing techniques. The dyes were tested for color strength and fastness properties, and they showed good fastness resistance to washing, rubbing, and perspiration, as well as fastness to sublimation and light. The dyes were further screened for their in vitro antibacterial activity against both Gram (+) and Gram (-) bacterial species. Most of them showed promising activities against these tested organisms.

There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.