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Fulltext Effects of acids pre-treatment on the microbial fermentation process for bioethanol production from microalgae

Phwan CK, Chew KW, Sebayang AH, Ong HC, Ling TC, Malek MA, et al.

Biotechnol Biofuels, 2019;12:191.
PMID: 31384298 DOI: 10.1186/s13068-019-1533-5

Background: Microalgae are one of the promising feedstock that consists of high carbohydrate content which can be converted into bioethanol. Pre-treatment is one of the critical steps required to release fermentable sugars to be used in the microbial fermentation process. In this study, the reducing sugar concentration of Chlorella species was investigated by pre-treating the biomass with dilute sulfuric acid and acetic acid at different concentrations 1%, 3%, 5%, 7%, and 9% (v/v).
Results: 3,5-Dinitrosalicylic acid (DNS) method, FTIR, and GC-FID were employed to evaluate the reducing sugar concentration, functional groups of alcohol bonds and concentration of bioethanol, respectively. The two-way ANOVA results (p
Fulltext Relationship between High Temperature and Formation of Chalkiness and Their Effects on Quality of Rice

Nevame AYM, Emon RM, Malek MA, Hasan MM, Alam MA, Muharam FM, et al.

Biomed Res Int, 2018;2018:1653721.
PMID: 30065932 DOI: 10.1155/2018/1653721

Occurrence of chalkiness in rice is attributed to genetic and environmental factors, especially high temperature (HT). The HT induces heat stress, which in turn compromises many grain qualities, especially transparency. Chalkiness in rice is commonly studied together with other quality traits such as amylose content, gel consistency, and protein storage. In addition to the fundamental QTLs, some other QTLs have been identified which accelerate chalkiness occurrence under HT condition. In this review, some of the relatively stable chalkiness, amylose content, and gel consistency related QTLs have been presented well. Genetically, HT effect on chalkiness is explained by the location of certain chalkiness gene in the vicinity of high-temperature-responsive genes. With regard to stable QTL distribution and availability of potential material resources, there is still feasibility to find out novel stable QTLs related to chalkiness under HT condition. A better understanding of those achievements is essential to develop new rice varieties with a reduced chalky grain percentage. Therefore, we propose the pyramiding of relatively stable and nonallelic QTLs controlling low chalkiness endosperm into adaptable rice varieties as pragmatic approach to mitigate HT effect.
Fulltext An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration

Ehteram M, Singh VP, Ferdowsi A, Mousavi SF, Farzin S, Karami H, et al.

PLoS One, 2019;14(5):e0217499.
PMID: 31150443 DOI: 10.1371/journal.pone.0217499

Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5-15% and 5-17% compared with the GP model, 12-21% and 10-22% compared with the M5T model, and 7-15% and 5-18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models.
Fulltext Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting

Afan HA, Allawi MF, El-Shafie A, Yaseen ZM, Ahmed AN, Malek MA, et al.

Sci Rep, 2020 03 13;10(1):4684.
PMID: 32170078 DOI: 10.1038/s41598-020-61355-x

In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.