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.
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.
This study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). The proposed CSA articulates the idea of utilizing an artificial immune system (AIS) to identify the behaviour of the SSTP, that is, using a sequence batch reactor (SBR) technology for treatment processes. The novelty of this study is the development of a predictive SSTP model for effluent discharge adopting the human immune system. Septic sludge from the individual septic tanks and package plants will be desuldged and treated in SSTP before discharging the wastewater into a waterway. The Borneo Island of Sarawak is selected as the case study. Currently, there are only two SSTPs in Sarawak, namely the Matang SSTP and the Sibu SSTP, and they are both using SBR technology. Monthly effluent discharges from 2007 to 2011 in the Matang SSTP are used in this study. Cross-validation is performed using data from the Sibu SSTP from April 2011 to July 2012. Both chemical oxygen demand (COD) and total suspended solids (TSS) in the effluent were analysed in this study. The model was validated and tested before forecasting the future effluent performance. The CSA-based SSTP model was simulated using MATLAB 7.10. The root mean square error (RMSE), mean absolute percentage error (MAPE), and correction coefficient (R) were used as performance indexes. In this study, it was found that the proposed prediction model was successful up to 84 months for the COD and 109 months for the TSS. In conclusion, the proposed CSA-based SSTP prediction model is indeed beneficial as an engineering tool to forecast the long-run performance of the SSTP and in turn, prevents infringement of future environmental balance in other towns in Sarawak.
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.
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.
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.
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.
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.
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.
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.
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.
A study was performed using 6 × 6 F1 diallel population without reciprocals to assess the mode of inheritance of pod yield and related traits in groundnut with imposed salinity stress. Heterosis was found for pod number and yield. Data on general and specific combining ability (gca and sca) indicated additive and nonadditive gene actions. The gca: sca ratios were much less than unity suggesting predominant role of nonadditive gene effects. Cultivars "Binachinabadam-2" and "Dacca-1" and mutant M6/25/64-82 had the highest, second highest, and third highest pod number, as well as gca values, respectively. These two cultivars and another mutant M6/15/70-19 also had the highest, second highest, and third highest pod yield, as well as gca values, respectively. Therefore, "Dacca-1", "Binachinabadam-2", M6/25/64-82, and M6/15/70-19 could be used as source of salinity tolerance. Cross combinations showing high sca effects arising from parents with high and low gca values for any trait indicate the influence of nonadditive genes on their expression. Parents of these crosses can be used for biparental mating or reciprocal recurrent selection for developing high yielding varieties. Crosses with high sca effects having both parents with good gca effects could be exploited by pedigree breeding to get transgressive segregants.
Morphological and host-plant relationship studies were conducted to differentiate two sympatric populations of brown planthopper (BPH), Nilaparvata lugens, one from rice (Oryza sativa) and the other from Leersia hexandra, a weed grass. In morphometric studies based on esterase activities, an UPGMA dendrogram using 17 quantitative morphological characters, including stridulatory organs (courtship signal-producing organs) between two sympatric populations of N. lugens, one from rice and the other from L. hexandra, a weed grass revealed that both populations were separated from each other. An out-group, N. bakeri, was found to be completely different from the two sympatric populations of N. lugens. Rice plants were best suited for the establishment of the rice-infesting population, and L. hexandra was a favourable host for the Leersia-infesting population. The individuals derived from one host did not thrive on the other host, as shown by a significant reduction in survival and nymphal development, ovipositional preferences, ovipositional response, and egg hatchability. Therefore, morphological and host-plant relationship studies indicate that rice-associated population with high esterase activities and L. heaxandra-associated population with low esterase activities are two closely related sibling species.
Growth parameters such as leaf area (LA), total dry mass (TDM) production, crop growth rate (CGR), relative growth rate (RGR), and net assimilation rate (NAR) were compared in six varieties of mungbean under subtropical condition (24°8' N 90°0' E) to identify limiting growth characters for the efficient application of physiology breeding for higher yields. Results revealed that a relatively smaller portion of TDM was produced before flower initiation and the bulk of it after anthesis. The maximum CGR was observed during pod filling stage in all the varieties due to maximum leaf area (LA) development at this stage. Two plant characters such as LA and CGR contributed to the higher TDM production. Results indicated that high yielding mungbean varieties should possess larger LA, higher TDM production ability, superior CGR at all growth stages, and high relative growth rate and net assimilation rate at vegetative stage which would result in superior yield components.
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.
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.
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).
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.
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.
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.