International Conference on Applied and Pure Sciences (ICAPS)

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Therapeutic potential of Cinnamomum zeylanicum Blum aqueous bark extract on doxorubicin induced cardiotoxicity in Wistar rats
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Sandamali, J.A.N.; Hewawasam, R.P.; Jayatilaka, K.A.P.W.; Mudduwa, L.K.B.
The effectiveness of doxorubicin as an anti-cancer agent is hampered by its’ life-threatening cardiotoxicity induced by oxidative-stress. As Cinnamomum zeylanicum Blum has proven antioxidant activity, the objective of this study was to find out the therapeutic potential of aqueous Cinnamomum bark extract against cardiotoxicity induced by doxorubicin in Wistar rats. Sample size of the study group was determined and an equal number of male and female Wistar rats were randomly selected into five groups. Group 1: normal-control (distilled water for 14 days, normal saline (10 mL/kg) on 11th day); group 2: plant control (2.0 g/kg of freeze dried plant extract for 14 days, normal saline (10 mL/kg); group 3: doxorubicin control (distilled water for 14 days, doxorubicin (18 mg/kg) on 11th day); group 4: freeze dried plant extract (2.0 g/kg) for 14 days, doxorubicin (18 mg/kg) on 11th day; group 5: distilled water for 14 days, dexrazoxane (180 mg/kg) 0.5 h before doxorubicin (18 mg/kg). Animals were sacrificed on the 15th day, blood was drawn for biochemical analysis and heart tissues were collected for estimation of antioxidant parameters and histological assessment of tissue damage. A significant (p ˂ 0.05) elevation in cardiac biomarkers including cardiac troponin I, AST, LDH and NT-proBNP activity were observed in doxorubicin-control group compared to the normal-control. Pretreatment with Cinnamomum bark extract in the doxorubicin treated rats showed a significant reduction (p ˂ 0.05) in above cardiac biomarkers compared to the doxorubicin-control. A significant reduction (p ˂ 0.05) in reduced glutathione, glutathione peroxidase and glutathione reductase was observed in the doxorubicin control group (Group 3) compared to the normal-control. Total antioxidant capacity as well as superoxide dismutase and catalase activity were markedly reduced (p < 0.05) in the doxorubicin control group. However, pretreatment with Cinnamomum extract was capable of significantly increasing (p ˂ 0.05) all of the above antioxidant parameters compared to the rat group which was treated with doxorubicin alone. A significant increase (p ˂ 0.05) in malondialdehyde concentration, which measures the lipid peroxidation and myeloperoxidase activity, which measures the extent of inflammation was observed in the doxorubicin-control compared to the normal-control. The plant-treated group showed a significant decrease (p ˂ 0.05) in malondialdehyde concentration and myeloperoxidase activity compared to the doxorubicincontrol. Histological assessment of tissue damage was scored according to a scale developed by the authors and doxorubicin-treated group showed a significant damage to the myocardium showing the highest score among the five groups. Plant-treated group showed only a minor degree of damage and showed a significant reduction in the score compared to the doxorubicin control. In conclusion, C. zeylanicum Blum bark extract has the potential to significantly reduce doxorubicin induced cardiotoxicity in Wistar rats.
Reduction of experimental error in coconut research by choosing proper data analysis techniques
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Fernando, W.H.H.; Waidyarathn, K.P.; Jayasundara, D.D.M.
Accurate data analysis techniques are essential in field experiments to correctly understand the influence of independent variables on the dependent variable/s. This study compares different statistical techniques used in analyzing longitudinal data (nut yield data collected in multiple years) of a coconut field experiment. Longitudinal studies are necessary for coconut research due to its perennial nature. However, these experiments often have high variability due to the heterogeneity nature of coconut palms, where individual palms display inconsistent temporal behaviour. High variation among the individuals in similarly treated plots makes treatment mean sensitive to those fluctuations ultimately masking the true treatment effect. Even careful planning of the experiment cannot ensure the total elimination of this component. The study highlights the ways in which how this unaccountable variability should be handled to obtain a precise research output. There are many types of statistical techniques used in analyzing data from different experimental designs to achieve optimal research outcomes. This study compares different statistical techniques applied to a randomized complete block design (RCBD), the most frequently used experimental design in coconut research, using a long term coconut fertilizer study. The example illustrates the appropriate types of analyses to meet the precise analysis output by evaluating the model residuals and the Coefficient of Variations (CV). CV, the ratio of the standard deviation to the mean (total average of the design), is a measure of relative variability. In particular ANOVA, Mean Square Error of ANOVA can be used as the standard deviation of the design because Standard Error (SE) of a statistic (usually an estimate of a parameter) represents the standard deviation of its sampling distribution. In this study, Repeated Measures Analysis of Variance (RMANOVA) was used as the classical method. Improved methods used were Linear mixed model and Multivariate Analysis of Variance(MANOVA) with two principal components (representing ≥ 78% variation of the data) as dependent variables. Adequacy of all methods was accepted after checking normality with the Shapiro-Wilk test, homogeneity of variance with Levene’s test, and independence of residuals with the Box-Pierce test. CV resulted from RMANOVA applied on RCBD was 39.2%, while it was 16.51% from the Liner mixed model. The lowest CV (10.04%) resulted from MANOVA with two principal components indicates that it can be more efficiently used to analyze long term experiments of coconuts. The consistency of the results should be studied further with a few more similar kinds of data sets. In addition to the above statistical analysis techniques, Bayesian inference methods will be studied for further improvements in the results.
Framework to select the most suitable production line in an apparel firm in Sri Lanka: use of Analytical Hierarchical Process
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Thalagahage, N.T.H.; Niwunhella, D.H.H.; Wijayanayake, A.
The apparel industry is considered as one of the most labour-intensive industries in the world despite the technological advancements and the amount of automation. Line planning in the garment industry is the process of scheduling and allocating production orders to production lines according to the product setting and due dates of manufacturing completion. Most of the apparel manufacturers in Sri Lanka have switched to lean model production, in which large sewing departments are split into smaller, self-balancing sewing lines. The decisions that address the production line selection process for a particular production order still heavily rely on production planners, based on their experience. These decisions tend to be neither consistent nor scientific because of the lack of interdepartmental connectivity. Little emphasis has been placed on the impact of the planning considerations and ways to apportion certain production orders to the appropriate production system with specific characteristics. This problem is addressed in the research through the development of a multi criteria decision making framework to enable the incorporation of all the parameters to select the best production line for a particular sales order using Analytical Hierarchical Process (AHP). AHP method is adopted for decision making which models multiple, possibly conflicting factors dependent on each other and it makes appropriate trade-offs to recommend well-balanced solutions to different stakeholders. The production line selection criteria identified through expert opinions and literature review were applied in the AHP conceptual model. 23 factors were identified and they were categorized under 5 areas which are characteristics of the product, characteristics of the production order, characteristics of the production line, technical support and quality parameters. In order to build the AHP model, 4 manufacturing firms and 4 senior and middle level managerial industrial experts from each firm were selected and interviewed through AHP questionnaires. After the pairwise comparisons, each criterion was weighted and prioritized. Most of the interviews resulted in high priority for delivery date, technical infrastructure, skills inventory of the line, the efficiency of the line, and cadre requirement while the ability to adopt changeovers, prioritization of machine service, and infrastructure support by the technicians were given low priorities. This interprets that, for any kind of a production order the mostly prioritized criteria are important to be considered. Therefore, focusing on them in line selection would lead to improved planning efficiency. After the criteria comparison, each alternative production line was given a score against the planning criteria and the production lines were ranked in order to select the best production line. Through data analysis, it was found out that the results obtained from different industrial experts representing different apparel manufacturing firms vary from each other depending on individual perspective and policies inherent to the manufacturing firm. However, the framework can relate to any apparel manufacturing firm by allowing Decision Makers to select the valid criteria depending on the Production Order and its related parameters. Also, the framework can be used for other manufacturing industries with few modifications and assumptions. In order to avoid the subjectivity in AHP method, a Linear Programming model can be developed as a future improvement and optimize the production lines selected through AHP ranking.
Morpho-molecular characterization of Lasiodiplodia and Diaporthe species infecting Solanum melongena L. (brinjal) in Gampaha district
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Koshila, H.V.A.S.; Dias, R.K.S.; Wanigatunge, R.P.; Edirisinghe, P.
Solanum melongena L. (brinjal) is a vegetable belonging to the family Solanaceae and a popular commodity among consumers. Brinjal plants are infected by numerous fungal pathogens causing a wide range of diseases such as anthracnose by Colletotrichum sp., stem and leaf lesions by Phomopsis vexans (anamorph of Diaporthe), and early blight by Alternaria solani which significantly reduce the yield. The objective of this study was to isolate and identify the fungal pathogens associated with leaves of S. melongena in the Gampaha district. Necrotic or prematurely wilted leaves of S. melongena were collected and associated fungi were isolated onto PDA medium. Pathogenicity was confirmed by wounded and non-wounded inoculation of a seven-day old isolate onto healthy S. melongena leaves and the confirmed fungal pathogens were identified using their morphological, cultural characteristics and analysis of the internal transcribed spacer region (ITS1-5.8S-ITS2). Fifteen fungal isolates were isolated from necrotic leaves of S. melongena. Three fungal isolates (Isolates H32A, H32B and U11) were identified to be pathogenic on leaves of S. melongena based on the pathogenicity test. A necrotic leaf spot was initiated at the site of inoculation with isolates H32A and H32B in both wounded and nonwounded inoculations, which later developed into wilting and premature falling of the leaf. Leaf blight was observed with non-wounded inoculation of isolate U11. Morphological characters of isolates H32A and H32B were similar, with fluffy, blackish-grey, septate mycelia and dark brown oval shape spores with a septum in the middle. Both had similar growth rates of 2.25 cm/day. They were morphologically identified as Lasiodiplodia sp. Yellowish grey color pigmentation was observed in the isolate U11 which produced aseptate hyphae but could not be identified by its morphological characteristics. The nucleotide sequence of ITS region confirmed the morphological identification of isolates H32A (MT990527) and H32B (MT990528) as Lasiodiplodia theobromae with 99.81% sequence similarity to L. theobromae (IRNKB244) at NCBI database. Further, isolate U11 (MT990529) showed 99.82% sequence similarity with Diaporthe eugeniae (ASHM304) at NCBI database. L. theobromae is reported to cause fruit rot in brinjal, while Diaporthe sp. has caused stem and leaf lesions. L. theobromae and D. eugeniae were confirmed to be pathogenic on S. melongena L. (brinjal) plants in the Gampaha district and further studies will be conducted to develop an environmentally friendly strategy to manage above mentioned diseases.
Investigation of antihyperglycaemic activity of hexane extract of polyherbal mixture in streptozotocin induced diabetic rats
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Sampath, S.N.T.I.; Jayasinghe, J.M.S.; Attanayake, A.P.; Karunaratne, V.
A homemade Ayurvedic remedy made of cloves of Allium sativum, leaves of Murraya koenigii, seeds of Piper nigrum and dried fruit rinds of Garcinia quaesita is considered as an antidiabetic polyherbal mixture. This polyherbal mixture has been used for the treatment of diabetes mellitus and dyslipidaemia in Sri Lankan traditional medicine. The in-vitro antioxidant and in-vivo acute antihyperglycaemic screening of hexane, ethyl acetate and methanol extracts of the above polyherbal mixture were studied and the hexane extract showed a significant antioxidant and dose dependent antihyperglycaemic activity when compared with the two extracts. Hence, the present study was aimed to further investigate the effect of administration of the hexane extract of polyherbal mixture at the optimum effective therapeutic dose for 30 days on serum glycaemic parameters in streptozotocin induced diabetic rats. Diabetes was induced in male Wistar rats by injecting with streptozotocin at the single dose of 65 mgkg-1 . Group one and two considered as the healthy untreated control, diabetic untreated control rats and received standard animal food and distilled water daily for 30 days (n = 6 /group). Group three and four were diabetic rats and were treated with the hexane extract (25 mgkg-1 ) and glibenclamide (positive control - 0.5 mgkg1 ) daily for 30 days respectively (n = 6 /group). Body weight of treated and control group rats were measured on 1 st, 7th, 14th, 21st and 28th day of the experiment. On the 30th day, all experimental rats were euthanized and blood was collected by cardiac puncture. The antihyperglycaemic activity was evaluated by determining the changes of fasting serum glucose concentration in each group using oral glucose tolerance test on 1 st , 7 th, 14th, 21st and 28th day and analyzed through total oral glucose tolerance curve (TAUC) values. Further, the percentage of glycated haemoglobin (HbA1C) and fasting serum glucose concentration were determined as glycaemic parameters in each group. The body weight was increased in healthy untreated control group and treated groups while the diabetic untreated control group showed a 10% reduction of body weight during the intervention period, indicating the treatment led to control loss of body weight. The oral administration of hexane extract and glibenclamde, lowered the TAUC values by 21% and 35% respectively and these values were statistically significant compared with TAUC value of diabetic untreated group (p < 0.05) on the 28th day of experiment. There was a statistically significant reduction in the HbA1C (27%, 33%) and the fasting serum glucose concentration (23%, 33%) in hexane extract and glibenclamide treated diabetic rats when compared to streptozotocin induced untreated diabetic rats (p < 0.05). The findings of the current study revealed that the hexane extract of the polyherbal mixture is a potential source to develop antidiabetic agent/s and further investigations are warranted to study the cellular antidiabetic mechanisms.
Availability and reliability analysis in 5G communication scenarios in IoT applications
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Vithanage, D.S.
Due to the increasing usage of wireless communication devices, the demand for fifth-generation (5G) cellular network access is growing rapidly. Facilities that might be seen with 5G technology include, far better levels of connectivity and coverage. 5G cellular networks provide dynamic coverage with respect to time and enduring overlapped cell areas. Due to this reason, 5G network users can be covered by numerous cells and Radio Access Technology (RATs). This could be done, especially by taking full advantage of network capability to facilitate extreme performance that includes supporting hugely inter-tethered devices attributed to IoT applications in 5G. The main challenge in IoT applications is that scalable and efficient connectivity for a massive number of devices sending very short packets, is not done adequately. In such scenarios, IoT devices are expected to select the most appropriate cell based on the channel availability information of each cell. Therefore, efficient cell selection is needed in 5G. Additionally, in a heterogeneous network with overlapping cells, cell selection could be a critical decision for 5G users. The proposed research is aimed at implementing two schemes for cell selection based on the availability and reliability performance in 5G. The study proposes an algorithm by considering two schemes. The first scheme is contingent on the distance. That is the distance to the base-stations must be considered. If the base stations are close to devices, signal strength is high. The second scheme is based upon the channel availability and the distance while priority goes to the channel availability of each cell. These two schemes were simulated by using a simulation program, which was developed in MATLAB. For cell selection, scheme 2 is much fairer than scheme 1 because by using scheme 2, channels availability is balanced through cells. Despite this, the nearest device is allocated to the nearest place by scheme 1 and as a result of that, signal strength is higher in those devices. By considering all the results obtained, it can be concluded that the proposed schemes are efficient cell selection schemes, which can be used to improve the overall system performance.
Sugarcane bagasse as a potential bacterial carrier
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Randima, G. W. A. P.; Kumari, W.G.S.M.; Masakorala, K.; Yapa, Y.M.A.L.W.
Immobilization of microorganisms into carrier materials is a promising tool in the formation of biofertilizers and biocontrol agents. Selection of an appropriate carrier material for cell immobilization is highly challenging since a carrier material should essentially facilitate cell immobilization, sufficient nutrition and a protective environment for the survival of immobilized cells. Further, there should not be a significant negative impact on ecological receptors of the receiving environment. Continuous supply of carrier materials at low cost is essential when scaling up laboratory made formulations into commercial products. Sugarcane bagasse (SCB) is a readily available industrial organic waste in Sri Lanka. However, it is underutilized as a carrier material in microorganism-based formations. Therefore, the objectives of the present study were to immobilize selected bacterial species in SCB and to estimate the viability of bacteria during storage in order to determine the potential of SCB as a bacterial carrier. The SCB matrix was prepared by grinding and sieving oven dried SCB to a fine powder. One portion of the fine powder was treated with 0.5 M NaOH while the other portion was kept untreated. Bacillus cereus at 1x 108 cell/mL optical density was used as the model bacterial inoculum. Four grams of both alkalitreated and untreated SCB matrices were inoculated with 50 mL of bacterial inoculum in 250 mL flasks. Immobilization of bacteria was facilitated by shaking at 150 rpm for 24 h. Bacteria-SCB matrices were collected by filtration and air dried for 1 h. Dried material was stored for 30 days at room temperature (approximately 30 °C) in sterilized screw-capped 250 mL flasks. Viability of bacteria in SCB matrices were compared with widely used sodium alginate bacterial carrier using the same model bacterial inoculum. Bacteria-sodium alginate homogenate was prepared at a final concentration of 2% (w/v) sodium alginate with 1x 108 cell/mL bacterial inoculum. Beads were prepared in 2.5% (w/v) CaCl2 solution while stirring and washed with sterilized distilled water and air dried aseptically for 1 h. Beads were then stored for 30 days at room temperature in sterilized screw-capped 250 mL flasks. Viability of immobilized bacteria was determined by estimating colony forming units (CFU) per mL at different time intervals from 48 h to 30 days of storage. Results showed the presence of >3 x 108 CFU/mL at 10-6 which was the highest tested dilution until 14 days of storage for all three matrices. However, CFU of untreated SCB dropped up to 10 fold after 14 days at all dilutions whereas CFU of alkali-treated SCB and sodium alginate remained >3 x108 CFU/ mL at 10-6 for 30 days. Growth of immobilized bacteria in SCB carrier matrix with 3 x 108CFU/mL at all dilutions confirms immobilization of tested bacteria in the SCB carrier matrix. Further, it confirms the viability of immobilized bacteria cells in the carrier material during storage at room temperature for 30 days. Scanning electron microscopic images showed attachment of bacteria on the surface of the SCB matrix. Therefore, we conclude the suitability of alkali treated SCB as a low-cost and locally available industrial waste as a carrier material for bacteria. Since B. cereus is a spore forming bacterium, during immobilization, cells may have survived as spores and germinated in the nutrient medium when cultured. Hence, further experiments with non-spore forming bacteria may support the evaluation of efficiency of cell multiplication in the alkali treated SCB matrix.
Tourist volume forecasting: An approach with supervised machine learning algorithms
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Basnayake, B.R.P.M .; Chandrasekara, N.V.
The tourism industry generates almost US$4 billion of income and provides direct and indirect employment to a large number of people in the country. Expert knowledge on the travel behaviour of tourists is an important part of planning and aids decision making for all stakeholders including the government and private business organisations. There was a severe drop in tourist arrivals during the civil war and was also apparent after the more recent Easter Sunday bomb attack. The study compared the predictions of different forecasting models on tourist arrivals in Sri Lanka, in an effort to identify the most appropriate model. The supervised machine learning algorithms (MLA) applied were Time Delay Neural Network (TDNN) and Feedforward Neural Network (FFNN) with two different algorithms namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG). Recently, MLA has started playing a vital role as an effective forecasting tool. A better model in forecasting was identified using the performance criteria of the Normalized Mean Squared Error (NMSE). As an initial step, monthly data from December 2019 to January 2000 were standardized to maintain the consistency of data. The aforementioned models were trained for 100, 200 and 500 epochs separately, with different numbers of hidden layers and hidden neurons, and detected the model with minimum NMSE for further training. For the selected model from TDNN, subsequently, the transfer functions and time delays were modified. A better model was identified in 500 epochs for the network with 2 hidden layers of 4 and 3 hidden neurons with tansig transfer functions from time delay of 3 (NMSE 0.3537). For the FFNN model, input combinations were recognized using the Pearson correlation coefficient and Spearman's rank correlation coefficient. Among the trained models with the different input combinations, the model with MA3, MA6, MA9, MA12, and MA15, lag 1, lag 2, lag 3, lag 11 and lag 12 indicated the lower NMSE of 0.5244 where Moving Average (MA) indicates current and past values and depends linearly on the output variable and lags being predetermined fixed quantity of passing time. For the FFNN, a better model was identified with the adjustment of parameters. A better model was identified in 100 epochs for the network with 3 hidden layers of 3 hidden neurons in each layer with tansig transfer functions, a learning rate (ɳ) of 0.01, a combination coefficient (μ) of 0.001 and a decreasing factor as 0.1 and increasing factor as 10 of μ (NMSE 0.2234). For the SCG algorithm, the lowest performance measurement value, NMSE was 0.3193. The model had 500 epochs with 3 hidden layers of 3, 2 and 2 hidden neurons respectively, transfer functions with tansig in all hidden layers, a sigma parameter value of 5e (- 5) and a lambda of 5e (-7). The main conclusion is that all the discussed network models capture the actual behaviour of the testing set while the minimum NMSE was identified in the FFNN with the LM algorithm. The findings of the analysis are beneficial, as tourism is a global service industry and a source of foreign exchange earnings and a key employment generation sector for the country.
High-resolution melting traceability of black pepper adulteration with papaya seeds, chili and/or other potential plants
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Herath, H.M.P.; Wijesinghe, W.R.P.
Black pepper (Piper nigrum L.) is a valuable medicinal spice and one of the premier exports in Sri Lanka. The black pepper industry in Sri Lanka faces a major threat due to the adulteration of Ceylon pepper with inferior quality substitutes like papaya seeds and chili powder. Therefore, frequent testing of black pepper products is essential to retain the reputation of Ceylon pepper. Application of morphological and chemical methods have limitations in adulteration detection, especially for admixtures in powdery and processed forms. Available molecular techniques also have limitations of being expensive, time consuming and less reliable. The research was aimed at developing an assay based on DNA barcoding coupled High Resolution Melting Analysis (BarHRM) which is sequencing-free, reliable, yet faster and more economical than DNA barcoding to report the spice authentication results. DNA isolation from dried storage tissues is extremely difficult due to the presence of polysaccharides, polyphenols, proteins like compounds and due to the scarcity of DNA. A modified CTAB method was developed along with a phenol extraction to extract and amplify the required DNA regions from the dried processed admixture of black pepper and its major adulterants. Although the DNA quality of the product varies among different samples, the capability of PCR amplification from any material including powdered admixture affirms the validity of the tests being developed in adulterant detection. Two novel gene-specific primer pairs were designed targeting the assay development and both newly developed rbcL markers were successful in PCR amplification. Subsequently, a relatively novel, high throughput technique called Bar-HRM was applied to detect the black pepper adulteration. According to the results melting profiles of pure samples of black pepper, papaya and chili were clearly separated so that they can be differentiated by HRM analysis. HRM data were further examined using Principal Component Analysis (PCA) and the results showed that HRM analysis successfully differentiates three species, separating them into three different clusters. Then the optimized HRM conditions were applied to admixtures and HRM curves of the adulterated samples were clearly deviated from the pure samples. It could be concluded that developed technique is a very first HRM based high-throughput system to authenticate black pepper adulteration with papaya seeds and chili. Although as a proof of concept this technique was developed to detect papaya and chili adulteration, novel system has the potential to detect other black pepper adulterants as well.
Unsaturated fatty acid compositions of selected pigmented and non-pigmented new improved rice varieties (Oryza sativa L.) of Sri Lanka
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Samaranayake, M.D.W.; Abeysekera, W.K.S.M.; Mahanama, K.R.R.; Hewajulige, I.G.N.; Somasiri, H.P.P.S.
Rice is the dietary staple for Sri Lankans and it contains monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) beneficial to human health.There are thousands of rice varieties (RVs) in the country and widely cultivating and consuming varieties are the new improved rice varieties (NIRVs). Studies on fatty acid (FA) compositions of rice are extremely limited and to date there is no single study reported on FA compositions of NIRVs of Sri Lanka. Thus, this study evaluated the MUFA and PUFA compositions of a range of NIRVs of Sri Lanka. Eight Sri Lankan NIRVs including 03 pigmented (At 362, At 311 & Bw 272-6b) and 05 nonpigmented (Bw 367, At 307, At 308, At 309 & Bg 403) RVs were used in this study. Grain lengths of RVs were measured according to internationally accepted standard methods. Fat was extracted from whole grain rice flour by Soxhlet fat extraction method, followed by derivation to methyl esters and analyzed by Gas chromatography with flame ionization detection (GC-FID). Results showed that studied RVs were extra-long (At 311 & At 309), long (At 362 & At 308), medium (At 307 & Bg 403) and short (Bw 272-6b & Bw 367) grains. Total unsaturated FA, MUFA and PUFA contents of studied RVs were varied from 16.97 ± 0.07 to 24.87 ± 0.07, 9.50 ± 0.10 to 14.55 ± 0.01 and 7.47 ± 0.04 to 10.32 ± 0.07 mg/g of rice respectively and highest in Bw 272-6b. The MUFAs in tested RVs were palmitoleic, oleic and ecosenoic acids whereas oleic acid was the most predominant. Short grain red RV, Bw 272-6b had the highest (14.08 mg/g) content of oleic acid while long grain red RV, At 362 had the lowest (9.15 mg/g). Among the studied RVs, PUFAs present were linoleic, gamma linoleic, homogamma linoleic and docosadienoic acids while linoleic acid was the abundant FA. Linoleic acid was most abundant (9.91 mg/g) in Bw 272-6b while least abundant (7.23 mg/g) in At 362. The findings of this study confirm that MUFAs and PUFAs of studied RVs varied significantly (p < 0.05) among the grain sizes while it was insignificant (p > 0.05) between pigmented and non-pigmented RVs.
Chemical profile of Terminalia chebula fruit collected from different regions of Sri Lanka and commercial samples from Sri Lanka and India
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Suraweera, K.P.C.D.; Wickramarachchi, S.R.; Tennakoon, T.M.S.G.
Terminalia chebula is a valuable medicinal plant used in traditional medicine. The fruit of T. chebula contains a large number of biologically active chemical compounds. Demand for the herbal drugs is increasing every day and maintaining the quality of herbal drugs is very important. Therefore, the objective of this study is qualitative and quantitative comparison of the chemical profiles of T. chebula fruit (without seeds) of commercial samples and authentic samples. Authentic samples of T. chebula were analyzed to see the effect of climatic zone variation on chemical profile and physicochemical parameters. Authentic samples (SLA) were collected from T. chebula plant itself from onsite visit, from Bibila, Buththala, Padhiyathalawa, Gampaha and Colombo and authenticated from the voucher specimen available at Herbarium, Link Natural Products (Pvt) Ltd (LNP). One composite sample was made according to sampling protocol, WHO 1998, from each region for analysis. Commercial samples are a mixture of fruits obtained from several suppliers from different areas. Commercial samples were obtained separately from three different batches of T. chebula commercial stocks from Sri Lanka (SLC) and India (INC) at LNP. Powdered dried fruits were extracted with 70% aqueous methanol and concentrated using rotatory evaporator. Physicochemical parameters were determined according to WHO and European pharmacopoeia methods. Total tannin was determined using Folin-Denis assay. Crude T. chebula fruit extract was separated by thin layer chromatography (TLC) and high performance liquid chromatography (HPLC). Gallic acid content and gallic acid/ellagic acid ratio were calculated using the peak area of HPLC chromatograms. TLC and HPLC showed a large number of compounds in the crude extract of T. chebula fruit. Both showed similar profiles with variations in intensity among the samples. Physicochemical parameters (without water soluble extractive values), total tannin, gallic acid content, gallic acid/ellagic acid ratio are statistically different among SLA samples (P < 0.05). Except physicochemical parameters, other parameters are statistically similar among SLC and INC (P<0.05). Total tannin content (% w/w) of SLA samples was; Padiyathalawa: 33.40 ± 0.17, Buththala: 43.39 ± 0.41, Gampaha: 41.13 ± 0.61, Bibila: 42.31 ± 0.23 and Colombo: 34.12 ± 0.01. Gallic acid content (% w/w) of SLA samples was; Padiyathalawa: 0.49 ± 0.01, Buththala: 0.98 ± 0.01, Gampaha: 1.03 ± 0.02, Bibila: 0.83 ± 0.02 and Colombo: 1.86 ± 0.04. Gallic acid/ellagic acid ratio (% w/w) of SLA samples; Padiyathalawa: 0.15 ± 0.0038, Buththala: 0.18 ± 0.0009, Gampaha: 0.16 ± 0.0003, Bibila: 0.16 ± 0.0041 and Colombo: 0.68 ± 0.0040. Total tannin content, gallic acid content and gallic acid/ellagic acid ratio vary in different batches of commercial samples. Mean of total tannin content (% w/w) of SLC was 49.14 ± 6.09 and INC is 42.79 ± 0.76. Mean of gallic acid content (% w/w) of SLC was 1.13 ± 0.28 and INC is 2.25 ± 0.69. Gallic acid/ellagic acid ratio (% w/w) of SLC was 0.30 ± 0.07 and INC is 0.43 ± 0.05. Chemical composition and quality of T. chebula. dried fruit depend on the geographical location, maturity stage, growth condition and raw material processing condition.
Activation of wood biochar and red brick using natural coconut vinegar
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Malka, U.K.M.; De Silva, R.C.L.; De Silva, D.S.M.; Chandrajith, R.
Number of studies have been carried out to determine the efficiency of strong oxidizers in activating natural raw materials used in low cost water purification processes. However, rural communities find it difficult to acquire most of such chemicals. Therefore, this study was aimed to determine the ability of natural coconut vinegar, which is a common domestic acidic solution, in activating abundantly available potential water purifying materials to reduce calcium (Ca2+) ions from water, further reducing the water hardness. In this study mature barks of Glyricidia (Glyricidia sepium), Gadumba (Trema orientalis) and Ipil Ipil (Leucaena leucocephala) were collected and air dried. These were carbonized (400-450 °C) in a closed vessel (2 hours) to produce biochar. Both biochar and brick particles in the range of 2.0-5.6 mm were selected for the analysis. For the activation these samples were soaked in natural coconut vinegar (biochar/brick: vinegar, 1:2 V/V) for 24 hours and completely dried in an oven (120 °C) for 3 hours. Laboratory scale glass columns (2 cm in diameter) were used to calculate Ca2+ adsorption and retaining capacities. Filtrates were analyzed for Ca2+ using flame photometer. Ca2+ adsorption and retaining capacities of each material were calculated per unit bulk volume of the material. Each test was duplicated, and the average was recorded. Untreated red brick and biochar of Glyricidia, Gadumba, Ipil Ipil showed Ca2+ adsorption capacities of 0.44, 0.30, 0.31, 0.27 mg cm3 and retaining capacities of 0.19, 0.01, 0.02, 0.02 mg cm-3 respectively. Activated red brick and biochar of Glyricidia, Gadumba and Ipil Ipil showed Ca2+ adsorption capacities of 0.76, 0.58, 0.68 and 0.63 mg cm-3 and retaining capacities of 0.25, 0.20, 0.23 and 0.15 mg cm-3 respectively. Increase in Ca2+ adsorption and retaining capacities were observed in all the materials tested after activation with vinegar. Further studies are continued to use the vinegar activated natural materials in a low-cost domestic drinking water purification process.
Effect of film thickness on characteristic properties of thermally evaporated cadmium sulphide thin films
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Lakmal, A.A.I.; Kumarasinghe, R.K.K.G.R.G.; Seneviratne, V.A.; Dassanayake, B.S.
Cadmium sulphide (CdS) thin films are regarded as one of the most promising materials for heterojunction solar cells. Due to its wide bandgap (~ 2.42 eV), CdS thin films have been used as the window material together with several semiconductors such as InP, CdTe, Cu2S, and CuInSe2. For the future development of photonic devices based on above materials comprehensive studies on CdS window layer throughout all aspects such as deposition technique, temperature, duration, and post-heat treatments, etc. are highly required. In this study, CdS thin films were deposited on the cleaned FTO glass substrates using vacuum thermal evaporation technique by varying the deposition duration to have different layer thicknesses. The temperature of the substrates and the chamber pressure were maintained at 175 °C and 2×10-5 torr respectively. The deposition was carried out using CdS powder (Sigma-Aldrich, 99.995%) containing in an alumina boat. Deposited samples were then annealed in vacuum (pressure 3×10-5 torr) at 300 °C for 30 minutes. The bandgap and optical transmittance of the deposited thin films were studied using UV-Visible spectrophotometry. The surface topology analysis of the deposited thin films was carried out using Atomic Force Microscopy (AFM). A photoelectrochemical cell of (CdS/0.1 mol L-1 Na2S2O3/Pt) was used to investigate electrical properties such as short circuit current (JSC), open circuit voltage (VOC), carrier concentration, and majority carrier type of the semiconductor with the aid of I-V measurements and Mott–Schottky measurements. The structural and crystal properties such as preferred orientation, phase distribution, crystallite size, microstrain, and lattice parameters were studied by employing the grazing incident X-ray diffraction. The calculations were done using the profile fit, Rietveld refinement, and Pawley refinement techniques. All the results revealed that there exists a correlation between the film thickness and the above-considered properties of the CdS thin film. The highest bandgap of 2.43 eV and optimum JSC and VOC of 113 µA/cm3 and 341 mV respectively were observed for the photoelectrochemical cell made by 210 nm thick CdS thin film.
A review of product based recommender systems used in online shopping platforms
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Boteju, W.P.A.
Online shopping platforms have drastically changed customer behaviour. Customers can make their choices far easier with the help of recommender systems which are an integral part of modern online shopping platforms. As a result of rapid growth in the number of products in the market and due to the complex lifestyle of people, choosing the right product, takes extra time and effort. Therefore, online shopping platforms provide users with recommender systems to help choose shopping items. A recommender system is a software tool used to recommend items of interest to users. Some recommender systems provide personalized recommendations by analysing user persona, personal interests, and requirements. In other words, there are personalized and nonpersonalized recommendation systems, though personalized recommender systems are becoming increasingly popular and the norm. For example, Netflix movie recommender systems, Amazon product recommender system are among many other generic book and music recommendation systems out there. In this study, we review some of the existing personalized recommender systems and analyse its strengths, weaknesses, and vulnerabilities. The basic components of a recommendation system are Items, Users, and Transactions. Apart from that, recommender systems use filtering methods such as collaborative filtering, knowledge-based filtering, constraint-based, content-based and community-based systems. The study investigated around 100 related research papers. There, we found 43 research studies based on a collaborative filtering approach, 31 based on knowledge-based, 8 studies using both methods as a hybrid approach, with the remaining 26 papers using other filtering methods. The acceptance rate of the personalized recommendations made by collaborative filtering is higher because recommendations are made based on user profile similarity and their purchasing behaviour. For example, 60% of movies are chosen by users based on Netflix's recommendations. This shows that collaborative filtering is effective for personalized recommendations. In contrast, the knowledge-based filtering method uses the description of the product and its properties/features with the profile of the user's preferences. For example, the Pandora music streaming service uses knowledge-based filtering for song recommendation and needs very little information to make similar recommendations. However, there are limitations in collaborative filtering and knowledge-based filtering methods. For example, key limitations of collaborative filtering are 'cold start problem', 'sparsity', and 'scalability'. Knowledge-based filtering shows limitations such as 'overspecialization' and 'domain-dependency'. Studies analysing hybrid recommendation methods have indicated better performance, in making recommendations. Further, we investigated some of the privacy issues and vulnerabilities in recommender systems. To our knowledge, only a handful of studies have investigated vulnerabilities of recommender systems. For example, Cyber-attacks can make significant damages to existing recommender systems. One study has simulated 6 inference attacks per user with 90% accuracy. Thus, security and privacy issues of existing recommender systems need to be explored and investigated. The review paper provides some valuable insights about the usability of existing recommender systems and their vulnerabilities. Future work will specifically focus on security issues of recommender systems and investigating novel systems such as GPT-3 empowered recommender systems.
Case study of credit risk analysis and creditworthiness prediction at loan approval
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Weralupitiya, B. N.; Jayatillake, R. V.
The credit risk is considered as the risk associated with a borrower’s failure to pay the loan or interest amount on time. An increase in Non-Performing Loan (NPL) ratio directly affects the financial performance of the banks as well as the economy of the country as a whole. Therefore, this case study was carried out for a specific bank in Sri Lanka with the objectives of developing a predictive model to assess the creditworthiness of potential loan applicants at the approval and to identify factors associated with time to first default. The data used for this study consisted of bank loan details of 10,626 existing customers in their current loan portfolio and their repayment behavior over 2.5 years. It consisted of 11 continuous and 7 categorical variables including customer’s demographic details, personal financial details and bank-specific ratios. Furthermore, it included 10 transaction variables which all are categorical. The univariate tests such as Mann Whitney Test and Chi-Square Independence Test and graphical analyses identified that apart from variables “Age at Approval” and “CRIB Status at the approval”, all the other variables showed a significant relationship with the variable of interest, “Loan Status”. As only 33% of the respondents were non-performers, the Synthetic Minority Oversampling Technique (SMOTE) was used to handle the class imbalance. Several machine learning techniques such as Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network were applied with and without SMOTE Sampling to achieve the optimal model by comparing the ROCAUC value of each model. The Artificial Neural Network model applied with SMOTE sampling was found to be the best model with a ROC-AUC value of 91.6%. Furthermore, the study data consisted of the borrowers’ default status in every quarter. Therefore, a discrete-time hazard survival model was developed to identify the predictors that affect most of the risk of first default. It was found that the risk of first default to be higher in early quarters and decreases over time with the best fitted discrete survival model. Particularly, “Security type”, “Loan to value ratio”, “Tenure”, “Purpose of the loan” and “Interest rate” were some of the variables found as the most significant variables that associate with the risk of first default.
Classifying risk and vulnerability in the supply chain during an epidemic outbreak
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Perera, M.A.S.M.; Wijayanayake, A.; Peter, S.
Companies always try to maximize shareholders' value by reducing the cost and maximizing profits in the long terms. However, one of the primary difficulties they face in doing so, is because of disruptions in the supply chain (SC). The supply chain can be disrupted due to natural disasters, manmade catastrophes, strikes, legal disputes, and special cases like epidemic outbreaks. The study explores what causes the supply chain to be disrupted in a company during an epidemic outbreak. It focuses on the Sri Lankan apparel industry as it contributes 6% to Sri Lanka’s GDP and 44% percent to Sri Lanka’s National Export Revenue, which is a significant proportion of the country’s economy. The primary objective of this study is to identify the supply chain risks in order to be prepared, mitigate the effects and ensure business continuity. The study proposes a model to identify the SC risks and vulnerabilities during an epidemic outbreak, and which risks should be prioritized. The model was primarily developed through a systematic review of literature and information collected from experts in the apparel sector was used to validate the findings. Leading apparel manufacturing companies in Sri Lanka were selected through convenience sampling and managers with more than five years’ experience were selected through random sampling. Using the output, the identified risks are then analysed and mapped in a vulnerability matrix considering cost and time factors. The model was tested and validated using 80%-20% rule. 80% of the collected data was used to develop the model and 20% of the collected data was used for testing and validation. Moreover, experts’ opinions were also used to validate the vulnerability matrix. Loss of local key supplier, loss of international key supplier, local port closure, international port closure, transportation link disruption (other than ports), raw materials delays and shortages, human resource shortages, product demand variations, order cancellations and lead time variations are SC risks which are considered for this study. The loss of international key suppliers and order cancellations were classified as high risks, whereas, human resource shortages were classified as the least risk. Though, a generalized vulnerability model is developed in this study considering cost and time factors, it can be customized using different factors and risks depending on the experience and needs of the company. Participants for the survey assumed that customers are international, and suppliers are both local and international. The study can be further developed to identify the SC strategies which should be taken to mitigate the SC disruptions during an epidemic outbreak or during a major global crisis.
Performance investigation of Perovskite/CIGS tandem solar cell using numerical modelling and simulation
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Ratnasinghe, D.R.; Adihetty, N.L.; Attygalle, M.L.C.
In the modern world, multi-billion projects are going on researching photovoltaic (PV) devices. Considering the global energy demand the contribution of solar power is still negligible. Therefore, researchers are working on finding new solutions to enhance the performances of these PV devices. With the approach of the multi junctional PV devices, researchers identified a clear path to reach Shockley & Queisser’s detailed balanced limit. This research was focused on modelling a tandem cell structure with perovskite and CIGS materials to obtain the best efficient device with enhanced performance. Therefore, a two-terminal tandem structure was modelled computationally. The SCAPS-1D (one-dimensional solar cell capacitance simulator) software was used for the modelling and simulations. The top cell configuration was modelled with SnO2, PCBM, CH3NH3PbI3 and PEDOT: PSS materials and the bottom cell with ZnO, CdS and CIGS materials. The higher energy bandgap materials were used in the top cell to absorb the high energies from the AM1.5G spectrum. The energies penetrating through the top cell are absorbed by the bottom cell. Therefore, low energy bandgap materials were used for the bottom cell absorber. In the simulation procedure, a SCAPS script was used to analyze partial absorptions of the top cell. Additionally, a homojunction was created at the bottom cell CdS/CIGS interface according to previous studies. This process created an SDL (surface defect layer). The defect densities of the two interfaces; CdS/SDL and SDL/CIGS were altered to analyze the possible outcomes. According to the results, 30.946% efficiency was observed for the tandem device with 1.816 V open-circuit voltage and 20.863 mA/cm2 short circuit current. According to the defect density alteration of the interfaces, the defects at the SDL/CIGS interface showed high influence compared to CdS/SDL. With the results of JV characteristic curves and quantum efficiency curves, the current matching condition and the peak efficiency have appeared at the same condition. Therefore, the results adhere to the basic operation of the tandem configuration. By concerning the interface defect densities, it can be concluded that the changing defect densities at SDL/CIGS interface change the direction of the carriers, which causes the efficiency decrement. In numerical modelling, many assumptions were used, and the fabrication of the model is recommended to observe the practical situation.
Time series analysis and forecasting of sector-wise electricity production and consumption in Sri Lanka
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Lakshika, B.H.H.; Dissanayaka, D.M.P.V.
Electricity is one of the main power sources all over the world. Electricity can be defined as Sri Lanka's’ primary breath since disparities of electricity mainly impact on country's socio and economic well-being. Therefore, it is essential to understand the electricity consumption patterns and the future capacity of electricity production for decision-making purposes. One of the study's key objectives is to estimate the appropriate model for defining and forecasting the sector-specific electricity production in Sri Lanka using an efficient and reiterative methodology based on the univariate and multivariate time series modelling approach. The other objective is to define the interrelationships between the production and consumption sectors individually. The electricity production in Sri Lanka has a national grid-primarily powered by hydropower, thermal heat, and wind power. The demand for electricity in Sri Lanka mainly depends on the activities of domestic, industrial, commercial sectors, and religious purposes. The proposed methodology was successfully applied to the monthly data related to the sector-wise electricity production and consumption (Units in GWh) in Sri Lanka over the past eighteen years from the year 2000 to 2018. Electricity production sectors were modelled by using both univariate and multivariate time series applications. Electricity consumption was modelled by using a multivariate time series approach. In the univariate approach, the Autoregressive Integrated Moving Average (0,1,4)(ARIMA(0,1,4)) model was proposed for the hydroelectricity production with Mean Absolute Percentage Error(MAPE) 17.59%, ARIMA(0,1,3)(0,0,2)(12)+ GARCH(1,1) model was fitted for the Thermal heat sector with MAPE 11.98% and the ARIMA(2,0,0)(1,1,1) model was fitted for the wind power sector with 17% of MAPE. According to these univariate analysis results, it can be concluded that there are seasonal patterns in thermal heat and wind power electricity production sectors. In this study, the existence of the correlation and cointegration of variables considered under the sectors of electricity production and consumption lead to consider a Vector Error Correction Model (VECM). The multivariate analysis shows evidence of the existence of the short term and long-term relationship between electricity production and consumption sectors separately.
CNN based deep learning model for tomato crop disease detection
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Gunarathna, M.M.; Rathnayaka, R.M.K.T.
Tomato is one of the most commonly cultivated solanaceous short duration vegetable crops, grown in outdoor and indoor conditions, worldwide. However, unfortunately, many diseases affect these crops which have an impact on quality and the quantity of the produce, agricultural productivity, and causes considerable economic losses to the producers and to the contribution to the growth of the agricultural sector. Therefore, continuous monitoring of the crop is required throughout the growing stage to identify the diseases. The most traditional way of identifying diseases is naked eye observation, which is tedious and time-consuming. Today, advances in computer vision paved by deep learning have led to a situation where disease diagnosis is based on automated recognition. The main objective of this study is to develop an accurate tomato disease classification model which eliminates human error when identifying diseases. Due to a variety of similar disease and pathological problems, even experienced agronomists and plant pathologists often fail to recognize the correct disease. This computer vision system will assist agronomists in detecting a variety of tomato crop diseases. The proposed algorithm consists of four main steps; data collection, data pre-processing, CNN model creation, and evaluation of performance metrics. A leaf is a good indicator of the tomato’s health. Therefore, tomato leaf images belonging to 10 different classes with a resolution of 256x256 were collected from the Internet to build, validate, and test the model. Collected images were normalized and image augmentation techniques were applied to increase the size of the training data set in the preprocessing phase. The CNN model of the study was built from scratch using the Keras library, which runs top of the Tensorflow backend. The model comprises four convolutional blocks followed by batch normalization, max pooling, and dropout layers. Two dense and flatten layers were also included at the end. A time-based learning rate scheduler was used with an initial learning rate of 0.001, momentum of 0.5, an epoch of 15 and a batch size of 27. The final model was able to achieve a training accuracy of 94% and a testing accuracy of 92%. This proposed system would encourage tomato cultivators to detect diseases at an early stage and start treatments without relying on experts. In the future, we hope to build an ensemble approach to classify plant diseases with real-time images towards the development of a decision support system.
Indole Acetic acid production and pathogen growth control abilities of endophytic fungal assemblages associated with two newly improved Oryza sativa varieties of Sri Lanka
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Pathmanathan, N.; Deshappriya, N.; Manamgoda, D.S.; Sandamali, T.G.I.
Newly improved rice varieties, highly dependent on agrochemicals, have been introduced to meet the increasing food demand in Sri Lanka. Health and environmental problems caused by extensive use of agrochemicals in rice cultivation necessitate investigations on alternative, less harmful methods for maintaining high productivity and disease management. Endophytic fungi (EF) of many crops have been reported to have the ability to enhance plant growth through the synthesis of Indole Acetic Acid (IAA) and to possess mechanisms of fungal pathogen control. Therefore, the present study was carried out to assess the levels of IAA production by the endophytic fungi (EF) isolated from two rice varieties, At 362 and Bg 352 with a view to utilise the high producers of IAA as a means of increasing rice plant growth and productivity. Screening test for growth inhibition of two known rice pathogens, Rhizoctonia solani and Bipolaris oryzae, the causative agents of Sheath blight and Brown spot diseases respectively was carried out to test the possibility of using isolated EF for management of the two pathogens. Healthy plant samples of the selected rice varieties were collected during the Yala and Maha seasons (2019) from Anuradhapura, Kurunegala, Gampaha and Kalutara districts. Endophytic fungi present on leaves, stems and roots were isolated onto 2% Malt Extract Agar medium. Fungal isolates were identified based on morphological characters and ITS gene sequencing. A total of 235 EF isolates belonging to 26 genera were isolated from the two rice varieties. IAA production by these fungal isolates was evaluated using Salkowski’s assay. The effect of the isolated EF on the growth of the two fungal pathogens was tested under in-vitro conditions using the dual culture assay. All experiments were conducted in triplicate and data were statistically analysed using one-way ANOVA and Tukey’s pairwise comparisons. Amongst the tested isolates, Curvularia sp and Aspergillus terreus isolated from Bg 352 produced IAA at significantly high levels of 15.642 µg/mL and 15.117 µg/mL respectively (P < 0.05). Dual culture studies showed that Sarocladium oryzae and Rhizopus microsporus isolated from At 362 inhibited the colony growth of R. solani by 68.5% and 58.7% respectively whilst the growth inhibition of B. oryzae was 38.5% and 43.1% respectively. The preliminary tests of this study indicated that some of the EF associated with the two rice varieties have the ability to produce significantly high levels of the growth promoting phytohormone IAA whilst some others have the means to control the growth of two common rice pathogens and therefore have the potential to be used for increased productivity of rice as well as for the control of the two rice diseases after further testing.