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  1. A systematic literature review of deep learning neural network for time series air quality forecasting
    Zaini N, Ean LW, Ahmed AN, Malek MA
    Environ Sci Pollut Res Int, 2022 Jan;29(4):4958-4990.
    PMID: 34807385 DOI: 10.1007/s11356-021-17442-1
    Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.
  2. Fulltext Increased yield performance of mutation induced Soybean genotypes at varied agro-ecological conditions
    Bhuiyan MSH, Malek MA, Emon RM, Khatun MK, Khandaker MM, Alam MA
    Braz J Biol, 2022;84:e255235.
    PMID: 35019108 DOI: 10.1590/1519-6984.255235
    In soybean breeding program, continuous selection pressure on traits response to yield created a genetic bottleneck for improvements of soybean through hybridization breeding technique. Therefore an initiative was taken to developed high yielding soybean variety applying mutation breeding techniques at Plant Breeding Division, Bangladesh Institute of Nuclear Agriculture (BINA), Bangladesh. Locally available popular cultivar BARI Soybean-5 was used as a parent material and subjected to five different doses of Gamma ray using Co60. In respect to seed yield and yield attributing characters, twelve true breed mutants were selected from M4 generation. High values of heritability and genetic advance with high genotypic coefficient of variance (GCV) for plant height, branch number and pod number were considered as favorable attributes for soybean improvement that ensure expected yield. The mutant SBM-18 obtained from 250Gy provided stable yield performance at diversified environments. It provided maximum seed yield of 3056 kg ha-1 with highest number of pods plant-1 (56). The National Seed Board of Bangladesh (NSB) eventually approved SBM-18 and registered it as a new soybean variety named 'Binasoybean-5' for large-scale planting because of its superior stability in various agro-ecological zones and consistent yield performance.
  3. Assessing changes of carbon stock in dipterocarp forest due to hydro-electric dam construction in Malaysia
    Dullah H, Malek MA, Omar H, Mangi SA, Hanafiah MM
    Environ Sci Pollut Res Int, 2021 Aug;28(32):44264-44276.
    PMID: 33847888 DOI: 10.1007/s11356-021-13833-6
    Deforestation and forest degradation are among the leading global concerns, as they could reduce the carbon sink and sequestration potential of the forest. The impoundment of Kenyir River, Hulu Terengganu, Malaysia, in 1985 due to the development of hydropower station has created a large area of water bodies following clearance of forested land. This study assessed the loss of forest carbon due to these activities within the period of 37 years, between 1972 and 2019. The study area consisted of Kenyir Lake catchment area, which consisted mainly of forests and the great Kenyir Lake. Remote sensing datasets have been used in this analysis. Satellite images from Landsat 1-5 MSS and Landsat 8 OLI/TRIS that were acquired between the years 1972 and 2019 were used to classify land uses in the entire landscape of Kenyir Lake catchment. Support vector machine (SVM) was adapted to generate the land-use classification map in the study area. The results show that the total study area includes 278,179 ha and forest covers dominated the area for before and after the impoundment of Kenyir Lake. The assessed loss of carbon between the years 1972 and 2019 was around 8.6 million Mg C with an annual rate of 0.36%. The main single cause attributing to the forest loss was due to clearing of forest for hydro-electric dam construction. However, the remaining forests surrounding the study area are still able to sequester carbon at a considerable rate and thus balance the carbon dynamics within the landscapes. The results highlight that carbon sequestration scenario in Kenyir Lake catchment area shows the potential of the carbon sink in the study area are acceptable with only 17% reduction of sequestration ability. The landscape of the study area is considered as highly vegetated area despite changes due to dam construction.
  4. Tsunami inundation maps for the northwest of Peninsular Malaysia and demarcation of affected electrical assets
    Naim NNN, Mardi NH, Malek MA, Teh SY, Wil MA, Shuja AH, et al.
    Environ Monit Assess, 2021 Jun 10;193(7):405.
    PMID: 34110509 DOI: 10.1007/s10661-021-09179-8
    The massive destruction and loss caused by the 2004 Sumatra-Andaman tsunami were attributed to the lack of knowledge on tsunami and low regional detection and communication systems for early warning in that region. This study aimed to identify locations at risk of impending tsunami from Andaman Sea for the safety of community and proper development planning at the coastal areas by providing an updated and revised inundation maps. The last study on this area was conducted several years ago which open the possibility to new findings. Generated by tsunami simulation models, the maps illustrate the extent and level of inundation to which the coastal community and infrastructure would be subjected. As a result of coastal changes and availability of better topographic data, the existing inundation maps for the coastal areas of northwest Peninsular Malaysia at risk to impending tsunami from the Andaman Sea are revised. This paper documented the computational setup leading to the generation of the revised inundation maps. The tsunami simulation model TUNA was used to simulate the generation, propagation, and subsequent run-up and inundation of tsunamis triggered by earthquakes of moment magnitudes (Mw) 8.5, 9.0, and 9.25 along the Sunda Trench. From the simulations, it was found that at Mw 9.25, Balik Pulau, Pulau Pinang would be subjected to inundation of as far as 3.47 km with 5.40-m-deep inundation at the highest section.
  5. 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.
  6. Adsorption kinetics of colour removal from palm oil mill effluent using wastewater sludge carbon in column studies
    Kutty SRM, Almahbashi NMY, Nazrin AAM, Malek MA, Noor A, Baloo L, et al.
    Heliyon, 2019 Oct;5(10):e02439.
    PMID: 31667371 DOI: 10.1016/j.heliyon.2019.e02439
    Treated palm oil mill effluents (POME) is of great concern as it still has colour from its dissolved organics which may pollute receiving water bodies. In this study, the removal of colour from treated palm oil mill effluent were investigated through adsorption studies using carbon derived from wastewater sludge (WSC). Sludge from activated sludge plants were dried and processed to produce WSC. In this study, three different bed depths of WSC were used: 5 cm, 10 cm, and 15 cm. For each bed depth, the flowrate was varied at three different values: 100 mL/hr, 50 mL/hr and 25 mL/hr. It was found that at bed depth of 5 cm, the breakthrough curves were occurred at 360 min, 150 min and 15 min for flowrates of 25, 50 and 100 mL/hr respectively. It was observed that at a particular depth the exhaustion time for column reduced as flow rate increases. Kinetic models, Adams-Bohart and Yoon-Nelson were used to analyze the performance of the adsorption. It was found that rate constant for Adams Bohart model decreased with the increase in bed depth. Adsorption capacity obtained from Adams-Bohart model ranged from 2676.19 mg/L up to 8938.78 mg/L. The maximum adsorption capacity increases with smaller bed depth. For Yoon-Nelson model, the rate constant decreases with increase in bed depth. The required time for 50% breakthrough obtained from the models ranged from 17.01 to 104.17 minutes for all three bed depths. The reduction of colour was found to be effective at all bed depths. The experimental data was best described by both models as with higher values of correlation coefficient (R2).
  7. Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model
    Tukimat NNA, Ahmad Syukri NA, Malek MA
    Heliyon, 2019 Sep;5(9):e02456.
    PMID: 31687558 DOI: 10.1016/j.heliyon.2019.e02456
    An accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. The SDSM was used as a climate agent to predict the changes of the climate trend in Δ2030s by gauged and ungauged stations. There were five predictors set have been selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was controlled to produce reliable validated results with lesser %MAE (<23%) and higher R. The projected rainfall was suspected to decrease 14% in Δ2030s. All the RCPs agreed the long term rainfall pattern was consistent to the historical with lower annual rainfall intensity. The RCP8.5 shows the least rainfall changes. These findings then used to compare the accuracy of monthly rainfall at control station (Stn 2). The GIS-Kriging method being as an interpolation agent was successfully to produce similar rainfall trend with the control station. The accuracy was estimated to reach 84%. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSM-GIS model can producing a reliable long-term rainfall generation at ungauged station.
  8. 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.
  9. 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 

  10. Hybrid liquid biphasic system for cell disruption and simultaneous lipid extraction from microalgae Chlorella sorokiniana CY-1 for biofuel production
    Yew GY, Chew KW, Malek MA, Ho YC, Chen WH, Ling TC, et al.
    Biotechnol Biofuels, 2019;12:252.
    PMID: 31666807 DOI: 10.1186/s13068-019-1591-8
    Background: The extraction of lipids from microalgae requires a pretreatment process to break the cell wall and subsequent extraction processes to obtain the lipids for biofuels production. The multistep operation tends to incur high costs and are energy intensive due to longer process operations. This research work applies the combination of radicals from hydrogen peroxide with an organic solvent as a chemical pretreatment method for disrupting the cell wall of microalgae and simultaneously extracting lipids from the biomass in a one-step biphasic solution.

    Result: Several parameters which can affect the biphasic system were analyzed: contact time, volume of solvent, volume ratio, type of organic solvent, biomass amount and concentration of solvents, to extract the highest amount of lipids from microalgae. The results were optimized and up to 83.5% of lipid recovery yield and 94.6% of enhancement was successfully achieved. The results obtain from GC-FID were similar to the analysis of triglyceride lipid standard.

    Conclusion: The profound hybrid biphasic system shows great potential to radically disrupt the cell wall of microalgae and instantaneously extract lipids in a single-step approach. The lipids extracted were tested to for its comparability to biodiesel performance.

  11. 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.
  12. Introgression of blast resistance genes into the elite rice variety MR263 through marker-assisted backcrossing
    Hasan MM, Rafii MY, Ismail MR, Mahmood M, Alam MA, Abdul Rahim H, et al.
    J Sci Food Agric, 2016 Mar 15;96(4):1297-305.
    PMID: 25892666 DOI: 10.1002/jsfa.7222
    Blast caused by the fungus Magnaporthe oryzae is a significant disease threat to rice across the world and is especially prevalent in Malaysia. An elite, early-maturing, high-yielding Malaysian rice variety, MR263, is susceptible to blast and was used as the recurrent parent in this study. To improve MR263 disease resistance, the Pongsu Seribu 1 rice variety was used as donor of the blast resistance Pi-7(t), Pi-d(t)1 and Pir2-3(t) genes and qLN2 quantitative trait locus (QTL). The objective was to introgress these blast resistance genes into the background of MR263 using marker-assisted backcrossing with both foreground and background selection.
  13. Marker-assisted backcrossing: a useful method for rice improvement
    Hasan MM, Rafii MY, Ismail MR, Mahmood M, Rahim HA, Alam MA, et al.
    Biotechnology, biotechnological equipment, 2015 Mar 04;29(2):237-254.
    PMID: 26019637
    The world's population is increasing very rapidly, reducing the cultivable land of rice, decreasing table water, emerging new diseases and pests, and the climate changes are major issues that must be addressed to researchers to develop sustainable crop varieties with resistance to biotic and abiotic stresses. However, recent scientific discoveries and advances particularly in genetics, genomics and crop physiology have opened up new opportunities to reduce the impact of these stresses which would have been difficult if not impossible as recently as the turn of the century. Marker assisted backcrossing (MABC) is one of the most promising approaches is the use of molecular markers to identify and select genes controlling resistance to those factors. Regarding this, MABC can contribute to develop resistant or high-yielding or quality rice varieties by incorporating a gene of interest into an elite variety which is already well adapted by the farmers. MABC is newly developed efficient tool by which using large population sizes (400 or more plants) for the backcross F1 generations, it is possible to recover the recurrent parent genotype using only two or three backcrosses. So far, many high yielding, biotic and abiotic stresses tolerance, quality and fragrance rice varieties have been developed in rice growing countries through MABC within the shortest timeframe. Nowadays, MABC is being used widely in plant breeding programmes to develop new variety/lines especially in rice. This paper reviews recent literature on some examples of variety/ line development using MABC strategy.
  14. A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
    Chun TS, Malek MA, Ismail AR
    Water Sci Technol, 2015;71(4):524-8.
    PMID: 25746643 DOI: 10.2166/wst.2014.451
    The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.
  15. Prediction analysis of effluent removal in a septic sludge treatment plant: a biomimetics engineering approach
    Chun TS, Malek MA, Ismail AR
    Environ Sci Process Impacts, 2014 Sep 20;16(9):2208-14.
    PMID: 25005632 DOI: 10.1039/c4em00282b
    Effluent discharge from septic tanks is affecting the environment in developing countries. The most challenging issue facing these countries is the cost of inadequate sanitation, which includes significant economic, social, and environmental burdens. Although most sanitation facilities are evaluated based on their immediate costs and benefits, their long-term performance should also be investigated. In this study, effluent quality-namely, the biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS)-was assessed using a biomimetics engineering approach. A novel immune network algorithm (INA) approach was applied to a septic sludge treatment plant (SSTP) for effluent-removal predictive modelling. The Matang SSTP in the city of Kuching, Sarawak, on the island of Borneo, was selected as a case study. Monthly effluent discharges from 2007 to 2011 were used for training, validating, and testing purposes using MATLAB 7.10. The results showed that the BOD effluent-discharge prediction was less than 50% of the specified standard after the 97(th) month of operation. The COD and TSS effluent removals were simulated at the 85(th) and the 121(st) months, respectively. The study proved that the proposed INA-based SSTP model could be used to achieve an effective SSTP assessment and management technique.
  16. Fulltext Capsaicin and dihydrocapsaicin determination in chili pepper genotypes using ultra-fast liquid chromatography
    Usman MG, Rafii MY, Ismail MR, Malek MA, Latif MA
    Molecules, 2014 May 21;19(5):6474-88.
    PMID: 24853712 DOI: 10.3390/molecules19056474
    Research was carried out to estimate the levels of capsaicin and dihydrocapsaicin that may be found in some heat tolerant chili pepper genotypes and to determine the degree of pungency as well as percentage capsaicin content of each of the analyzed peppers. A sensitive, precise, and specific ultra fast liquid chromatographic (UFLC) system was used for the separation, identification and quantitation of the capsaicinoids and the extraction solvent was acetonitrile. The method validation parameters, including linearity, precision, accuracy and recovery, yielded good results. Thus, the limit of detection was 0.045 µg/kg and 0.151 µg/kg for capsaicin and dihydrocapsaicin, respectively, whereas the limit of quantitation was 0.11 µg/kg and 0.368 µg/kg for capsaicin and dihydrocapsaicin. The calibration graph was linear from 0.05 to 0.50 µg/g for UFLC analysis. The inter- and intra-day precisions (relative standard deviation) were <5.0% for capsaicin and <9.9% for dihydrocapsaicin while the average recoveries obtained were quantitative (89.4%-90.1% for capsaicin, 92.4%-95.2% for dihydrocapsaicin), indicating good accuracy of the UFLC method. AVPP0705, AVPP0506, AVPP0104, AVPP0002, C05573 and AVPP0805 showed the highest concentration of capsaicin (12,776, 5,828, 4,393, 4,760, 3,764 and 4,120 µg/kg) and the highest pungency level, whereas AVPP9703, AVPP0512, AVPP0307, AVPP0803 and AVPP0102 recorded no detection of capsaicin and hence were non-pungent. All chili peppers studied except AVPP9703, AVPP0512, AVPP0307, AVPP0803 and AVPP0102 could serve as potential sources of capsaicin. On the other hand, only genotypes AVPP0506, AVPP0104, AVPP0002, C05573 and AVPP0805 gave a % capsaicin content that falls within the pungency limit that could make them recommendable as potential sources of capsaicin for the pharmaceutical industry.
  17. Fulltext Heritability and genetic advance among chili pepper genotypes for heat tolerance and morphophysiological characteristics
    Usman MG, Rafii MY, Ismail MR, Malek MA, Abdul Latif M
    ScientificWorldJournal, 2014;2014:308042.
    PMID: 25478590 DOI: 10.1155/2014/308042
    High temperature tolerance is an important component of adaptation to arid and semiarid cropping environment in chili pepper. Two experiments were carried out to study the genetic variability among chili pepper for heat tolerance and morphophysiological traits and to estimate heritability and genetic advance expected from selection. There was a highly significant variation among the genotypes in response to high temperature (CMT), photosynthesis rate, plant height, disease incidence, fruit length, fruit weight, number of fruits, and yield per plant. At 5% selection intensity, high genetic advance as percent of the mean (>20%) was observed for CMT, photosynthesis rate, fruit length, fruit weight, number of fruits, and yield per plant. Similarly, high heritability (>60%) was also observed indicating the substantial effect of additive gene more than the environmental effect. Yield per plant showed strong to moderately positive correlations (r = 0.23-0.56) at phenotypic level while at genotypic level correlation coefficient ranged from 0.16 to 0.72 for CMT, plant height, fruit length, and number of fruits. Cluster analysis revealed eight groups and Group VIII recorded the highest CMT and yield. Group IV recorded 13 genotypes while Groups II, VII, and VIII recorded one each. The results showed that the availability of genetic variance could be useful for exploitation through selection for further breeding purposes.
  18. A clonal selection algorithm model for daily rainfall data prediction
    Noor Rodi NS, Malek MA, Ismail AR, Ting SC, Tang CW
    Water Sci Technol, 2014;70(10):1641-7.
    PMID: 25429452 DOI: 10.2166/wst.2014.420
    This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction.
  19. Fulltext Morphological characterization and assessment of genetic variability, character association, and divergence in soybean mutants
    Malek MA, Rafii MY, Shahida Sharmin Afroz M, Nath UK, Mondal MM
    ScientificWorldJournal, 2014;2014:968796.
    PMID: 25197722 DOI: 10.1155/2014/968796
    Genetic diversity is important for crop improvement. An experiment was conducted during 2011 to study genetic variability, character association, and genetic diversity among 27 soybean mutants and four mother genotypes. Analysis of variance revealed significant differences among the mutants and mothers for nine morphological traits. Eighteen mutants performed superiorly to their mothers in respect to seed yield and some morphological traits including yield attributes. Narrow differences between phenotypic and genotypic coefficients of variation (PCV and GCV) for most of the characters revealed less environmental influence on their expression. High values of heritability and genetic advance with high GCV for branch number, plant height, pod number, and seed weight can be considered as favorable attributes for soybean improvement through phenotypic selection and high expected genetic gain can be achieved. Pod and seed number and maturity period appeared to be the first order traits for higher yield and priority should be given in selection due to their strong associations and high magnitudes of direct effects on yield. Cluster analysis grouped 31 genotypes into five groups at the coefficient value of 235. The mutants/genotypes from cluster I and cluster II could be used for hybridization program with the mutants of clusters IV and V in order to develop high yielding mutant-derived soybean varieties for further improvement.
  20. Fulltext Variability and performance evaluation of introgressed Nigerian dura x Deli dura oil palm progenies
    Noh A, Rafii MY, Mohd Din A, Kushairi A, Norziha A, Rajanaidu N, et al.
    Genet. Mol. Res., 2014;13(2):2426-37.
    PMID: 24781997 DOI: 10.4238/2014.April.3.15
    Twelve introgressed oil palm (Elaeis guineensis) progenies of Nigerian dura x Deli dura were evaluated for bunch yield, yield attributes, bunch quality components and vegetative characters at the Malaysian Palm Oil Board Research Station, in Keratong, Pahang, Malaysia. Analysis of variance revealed significant to highly significant genotypic differences, indicating sufficient genetic variability among the progenies for bunch yield and its attributes, vegetative characters and bunch quality components, except fruit to bunch ratio. Fresh fruit bunch yield ranged from 167 kg·palm(-1)·year(-1) in PK1330 to 212 kg·palm(-1)·year(-1) in PK1351, with a mean yield of 192 kg·palm(-1)·year(-1). Among the progeny, PK1313 had the highest oil to bunch ratio (19.36%), due to its high mesocarp to fruit ratio, fruit to bunch ratio and low shell to fruit ratio. Among the progenies, PK1313 produced the highest oil yield of 31.4 kg·palm(-1)·year(-1), due to a high mesocarp to fruit ratio (61.2%) and a low shell to fruit ratio (30.7%), coupled with high fruit to bunch ratio (65.6%). PK1330 was found promising for selection, as it had desirable vegetative characters, including smaller petiole cross section (27.15 cm2), short rachis length (4.83 m), short palm height (1.85 m), and the lowest leaf number (164.6), as these vegetative characters are prerequisites for selecting palms for high density planting and high yield per hectare. The genetic variability among the progenies was found to be high, indicating ample scope for further breeding, followed by selection.
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