Pub Date : 2023-12-04DOI: 10.11113/mjfas.v19n6.3130
H. F. Basri, A. Omoregie, M. A. Mokhter
The escalation of heavy metal pollution in natural ecosystems due to industrialization presents a critical environmental concern, endangering the well-being of living organisms. Microbially Induced Carbonate Precipitation (MICP) technology, an emerging innovation, has gained attention from the scientific community for its potential in biocementation and bioremediation applications. However, a substantial gap in understanding exists regarding the utilization of ureolytic microbial strains from waste sources capable of effectively immobilizing high concentrations of heavy metals. This study endeavors to explore the latent potential of indigenous ureolytic bacteria derived from leachate and restaurant wastewater, possessing bioremediation capabilities for heavy metal immobilization. The investigation includes microbial screening, physiological characterization of ureolytic bacteria, assessment of their tolerance levels, and evaluation of heavy metal removal efficacy through Atomic Absorption Spectrophotometry (AAS) analysis. Notably, the results reveal that ureolytic bacteria from restaurant wastewater are more tolerant to Cd2+ concentrations compared to their leachate counterparts, manifesting optimum conductivity, pH, and optical density (OD). More so, AAS analysis demonstrates the restaurant wastewater-derived sample's remarkable proficiency in Cd2+ removal, achieving a substantial 95% removal rate, significantly outperforming the leachate wastewater sample's removal rate of 53%.
{"title":"Influence of Enriched Urease Producing Bacteria from Leachate and Restaurant Wastewater on Heavy Metal Removal","authors":"H. F. Basri, A. Omoregie, M. A. Mokhter","doi":"10.11113/mjfas.v19n6.3130","DOIUrl":"https://doi.org/10.11113/mjfas.v19n6.3130","url":null,"abstract":"The escalation of heavy metal pollution in natural ecosystems due to industrialization presents a critical environmental concern, endangering the well-being of living organisms. Microbially Induced Carbonate Precipitation (MICP) technology, an emerging innovation, has gained attention from the scientific community for its potential in biocementation and bioremediation applications. However, a substantial gap in understanding exists regarding the utilization of ureolytic microbial strains from waste sources capable of effectively immobilizing high concentrations of heavy metals. This study endeavors to explore the latent potential of indigenous ureolytic bacteria derived from leachate and restaurant wastewater, possessing bioremediation capabilities for heavy metal immobilization. The investigation includes microbial screening, physiological characterization of ureolytic bacteria, assessment of their tolerance levels, and evaluation of heavy metal removal efficacy through Atomic Absorption Spectrophotometry (AAS) analysis. Notably, the results reveal that ureolytic bacteria from restaurant wastewater are more tolerant to Cd2+ concentrations compared to their leachate counterparts, manifesting optimum conductivity, pH, and optical density (OD). More so, AAS analysis demonstrates the restaurant wastewater-derived sample's remarkable proficiency in Cd2+ removal, achieving a substantial 95% removal rate, significantly outperforming the leachate wastewater sample's removal rate of 53%.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"2 6","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138603739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The tripartite industry classification, which divides all economic activities into three parts, is a classification method to reflect the dynamic process of economic development and the historical trend of the change of resource allocation structure.The fact shows that the proportion of each industry has become an important symbol of the level of national economic development. The proportion of each industry is compositional data,which is a kind of complex multidimensional data used in many fields. All components in the compositional data are non-negative and carry only relative information. In practice, there could be missing values in compositional data. However, general statistical analysis methods cannot be firstly used for compositional data with missing values. The complexity of the missing value of compositional data makes traditional imputation methods no longer suitable. Thus, how to carry out effective statistical inference for compositional data with missing values attracts the attention of many scholars, recently. In this paper, we focus on the imputation problem in compositional data containing missing values, and propose an Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) imputation method to obtain a complete datasets through variable selection and parameter estimation. Then, the new method is simulated and empirically analyzed, and a comparative study with mean imputation, k-nearest neighbor imputation, and iterative regression imputation is conducted. The results show that the ALASSO imputation method has the highest accuracy for different missing rates, dimensions and correlation coefficients.
{"title":"Application of Imputation Method for Compositional Data with Missing Values based on Adaptive LASSO Model: the Composition of Employment Industry in Taiyuan, China","authors":"Ying Tian, Majid Khan Majahar Ali, Fam Pei Shan, Lili Wu, Siti Zulaikha Mohd Jamaludin","doi":"10.11113/mjfas.v20n1.3034","DOIUrl":"https://doi.org/10.11113/mjfas.v20n1.3034","url":null,"abstract":"The tripartite industry classification, which divides all economic activities into three parts, is a classification method to reflect the dynamic process of economic development and the historical trend of the change of resource allocation structure.The fact shows that the proportion of each industry has become an important symbol of the level of national economic development. The proportion of each industry is compositional data,which is a kind of complex multidimensional data used in many fields. All components in the compositional data are non-negative and carry only relative information. In practice, there could be missing values in compositional data. However, general statistical analysis methods cannot be firstly used for compositional data with missing values. The complexity of the missing value of compositional data makes traditional imputation methods no longer suitable. Thus, how to carry out effective statistical inference for compositional data with missing values attracts the attention of many scholars, recently. In this paper, we focus on the imputation problem in compositional data containing missing values, and propose an Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) imputation method to obtain a complete datasets through variable selection and parameter estimation. Then, the new method is simulated and empirically analyzed, and a comparative study with mean imputation, k-nearest neighbor imputation, and iterative regression imputation is conducted. The results show that the ALASSO imputation method has the highest accuracy for different missing rates, dimensions and correlation coefficients.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"62 20","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138604797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.11113/mjfas.v19n6.3062
Lili Wu, P. Fam, Majid Khan Majahar Ali, Ying Tian, Mohd. Tahir Ismail, Siti Zulaikha Mohd Jamaludin
Due to the development of information technology, large amounts of data are generated every day in various industries such as engineering, healthcare, finance, anomaly detection, image recognition, and artificial intelligence. This massive data poses the challenge of analyzing accurately and appropriate classifications. The traditional clustering methods require specifying the number of clusters and are mostly based on distance, which cannot effectively consider the correlations between different indicators of high-dimensional and multi-source data. Moreover, the number of clusters cannot automatically adjust when new data is generated. In order to improve the clustering analysis of high-dimensional and multi-source data in a big data environment, this study utilizes non-parametric mixture models based on distribution clustering, which does not require specifying the number of clusters and can auto update with the data. By combining Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and the non-parametric Bayesian method called Dirichlet Process Mixture Model (DPMM), the Bayesian non-parametric PCA model (PCA-DPMM) and Bayesian non-parametric t-SNE model (TSNE-DPMM) are proposed. The Chinese restaurant process of DPMM is used for sampling by introducing a finite normal mixture distribution. The clustering results on the iris dataset are compared and analyzed. The accuracy of DPMM and TSNE-DPMM reaches 0.97, while PCA-DPMM achieves a maximum accuracy of only 0.94. When different numbers of iterations are set, TSNE-DPMM maintains an accuracy ranging from 0.92 to 0.97, DPMM ranges from 0.66 to 0.97, and PCA-DPMM ranges from 0.73 to 0.94. Therefore, the proposed TSNE-DPMM ensures accuracy and exhibits better model stability in clustering results. Future research can explore the improvement of the model by incorporating deep learning algorithms, among others, to further enhance its performance. Additionally, applying the TSNE-DPMM model to data analysis in other fields is also a future research direction. Through these efforts, we can better tackle the challenges of analyzing high-dimensional and multi-source data in a big data environment and extract valuable information from it.
{"title":"Comparative Analysis of Improved Dirichlet Process Mixture Model","authors":"Lili Wu, P. Fam, Majid Khan Majahar Ali, Ying Tian, Mohd. Tahir Ismail, Siti Zulaikha Mohd Jamaludin","doi":"10.11113/mjfas.v19n6.3062","DOIUrl":"https://doi.org/10.11113/mjfas.v19n6.3062","url":null,"abstract":"Due to the development of information technology, large amounts of data are generated every day in various industries such as engineering, healthcare, finance, anomaly detection, image recognition, and artificial intelligence. This massive data poses the challenge of analyzing accurately and appropriate classifications. The traditional clustering methods require specifying the number of clusters and are mostly based on distance, which cannot effectively consider the correlations between different indicators of high-dimensional and multi-source data. Moreover, the number of clusters cannot automatically adjust when new data is generated. In order to improve the clustering analysis of high-dimensional and multi-source data in a big data environment, this study utilizes non-parametric mixture models based on distribution clustering, which does not require specifying the number of clusters and can auto update with the data. By combining Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and the non-parametric Bayesian method called Dirichlet Process Mixture Model (DPMM), the Bayesian non-parametric PCA model (PCA-DPMM) and Bayesian non-parametric t-SNE model (TSNE-DPMM) are proposed. The Chinese restaurant process of DPMM is used for sampling by introducing a finite normal mixture distribution. The clustering results on the iris dataset are compared and analyzed. The accuracy of DPMM and TSNE-DPMM reaches 0.97, while PCA-DPMM achieves a maximum accuracy of only 0.94. When different numbers of iterations are set, TSNE-DPMM maintains an accuracy ranging from 0.92 to 0.97, DPMM ranges from 0.66 to 0.97, and PCA-DPMM ranges from 0.73 to 0.94. Therefore, the proposed TSNE-DPMM ensures accuracy and exhibits better model stability in clustering results. Future research can explore the improvement of the model by incorporating deep learning algorithms, among others, to further enhance its performance. Additionally, applying the TSNE-DPMM model to data analysis in other fields is also a future research direction. Through these efforts, we can better tackle the challenges of analyzing high-dimensional and multi-source data in a big data environment and extract valuable information from it.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"6 4","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138602824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.11113/mjfas.v19n6.3098
Mohamed Shantal, Z. Othman, Azuraliza Abu Bakar
The correlation coefficient is one of the essential statistical techniques used to discover relationships among variables. Various techniques can quantify correlation, such as Pearson's, Spearman's, and Kendall's correlation coefficients, depending on the data type. As with any use of data, missing data will impact the availability of data, reducing it and potentially affecting the results. Furthermore, the removal of missing-value data from the study when using complete case analysis or available case analysis may result in selection biases. In this paper, we investigate the impact of missing data on the correlation coefficient value by calculating the difference between the correlation coefficient of the original complete dataset and that of a dataset with missing data. Two deletion strategies (Listwise and Pairwise) and three imputation strategies (Mean, k-Nearest Neighbors (k-NN), and Expectation-Maximization) were used to prepare the data before calculating the correlation coefficient. Unique correlation coefficient values were created by converting unique values to a one-dimensional array, and RMSE metrics were used to evaluate the experiments. Eight UCI and Kaggle datasets with different sizes and numbers of attributes were used in this study. The experiment results demonstrate that the Pairwise strategy and k-NN give good results on the correlation coefficient, respectively, when the missing rate is moderate or less. Pairwise uses all the available values and discards only the missing values of the related attribute, while k-NN fills the missing values with new values that produce correlation coefficient values close to the actual values.
{"title":"Impact of Missing Data on Correlation Coefficient Values: Deletion and Imputation Methods for Data Preparation","authors":"Mohamed Shantal, Z. Othman, Azuraliza Abu Bakar","doi":"10.11113/mjfas.v19n6.3098","DOIUrl":"https://doi.org/10.11113/mjfas.v19n6.3098","url":null,"abstract":"The correlation coefficient is one of the essential statistical techniques used to discover relationships among variables. Various techniques can quantify correlation, such as Pearson's, Spearman's, and Kendall's correlation coefficients, depending on the data type. As with any use of data, missing data will impact the availability of data, reducing it and potentially affecting the results. Furthermore, the removal of missing-value data from the study when using complete case analysis or available case analysis may result in selection biases. In this paper, we investigate the impact of missing data on the correlation coefficient value by calculating the difference between the correlation coefficient of the original complete dataset and that of a dataset with missing data. Two deletion strategies (Listwise and Pairwise) and three imputation strategies (Mean, k-Nearest Neighbors (k-NN), and Expectation-Maximization) were used to prepare the data before calculating the correlation coefficient. Unique correlation coefficient values were created by converting unique values to a one-dimensional array, and RMSE metrics were used to evaluate the experiments. Eight UCI and Kaggle datasets with different sizes and numbers of attributes were used in this study. The experiment results demonstrate that the Pairwise strategy and k-NN give good results on the correlation coefficient, respectively, when the missing rate is moderate or less. Pairwise uses all the available values and discards only the missing values of the related attribute, while k-NN fills the missing values with new values that produce correlation coefficient values close to the actual values.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"7 5","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138603004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.11113/mjfas.v19n6.3128
Lini Idris, Muhammad Amirul Adli, Nurul Najihah Yaacop, Rozaini MOHD ZOHDI
Propolis, a natural resinous substance secreted by bees, has garnered considerable interest due to its diverse bioactive compounds and potential health benefits. Nevertheless, the phytochemical composition of propolis exhibits significant variation, influenced by multiple factors including geographical region, and botanical origin. These determinants exert profound effects on the distinctive properties and biological diversities of propolis. This study aimed to investigate the phytochemical composition and antioxidant activities of Geniotrigona thoracica propolis extracts collected from three apiary sites, designated as apiary A, apiary B, and apiary C, located in different regions within Selangor. The ethanolic extracts of propolis were prepared using 70% of ethanol and subjected to phytochemical screening to identify the presence of flavonoids, terpenoids, alkaloids, saponins, tannins, steroids, and cardiac glycosides, whilst the total phenolic content (TPC) and total flavonoid content (TFC) were measured using the Folin-Ciocalteu colorimetric and aluminium chloride methods, respectively. Additionally, the antioxidant activities of the propolis extracts were evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging and ferric-reducing antioxidant power (FRAP) assays. The phytochemical screening revealed the presence of flavonoids, terpenoids, alkaloids, saponins, tannins, steroids, and cardiac glycosides in all propolis extracts. The propolis from apiary A exhibited significantly higher TPC (302.21 ± 0.11 mg/mL GAE) and TFC values (99.08 ± 0.03 mg/mL QE) compared to apiary B and C. The results also indicated that propolis from apiary A possessed significantly higher antioxidant activities, with IC₅₀ value of DPPH at 25.27 μg/mL and FRAP value of 727.53 ± 0.09 μM Fe²+, in comparison to apiary B and C. A strong correlation was observed between TPC, TFC, and IC₅₀ of DPPH. This study highlights significant variations in the phytochemical compositions and antioxidant activities of propolis samples collected from different geographical and botanical sources. Further investigation is in progress to identify the specific phytochemical constituents responsible for these variations.
{"title":"Phytochemical Screening and Antioxidant Activities of Geniotrigona thoracica Propolis Extracts Derived from Different Locations in Malaysia","authors":"Lini Idris, Muhammad Amirul Adli, Nurul Najihah Yaacop, Rozaini MOHD ZOHDI","doi":"10.11113/mjfas.v19n6.3128","DOIUrl":"https://doi.org/10.11113/mjfas.v19n6.3128","url":null,"abstract":"Propolis, a natural resinous substance secreted by bees, has garnered considerable interest due to its diverse bioactive compounds and potential health benefits. Nevertheless, the phytochemical composition of propolis exhibits significant variation, influenced by multiple factors including geographical region, and botanical origin. These determinants exert profound effects on the distinctive properties and biological diversities of propolis. This study aimed to investigate the phytochemical composition and antioxidant activities of Geniotrigona thoracica propolis extracts collected from three apiary sites, designated as apiary A, apiary B, and apiary C, located in different regions within Selangor. The ethanolic extracts of propolis were prepared using 70% of ethanol and subjected to phytochemical screening to identify the presence of flavonoids, terpenoids, alkaloids, saponins, tannins, steroids, and cardiac glycosides, whilst the total phenolic content (TPC) and total flavonoid content (TFC) were measured using the Folin-Ciocalteu colorimetric and aluminium chloride methods, respectively. Additionally, the antioxidant activities of the propolis extracts were evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging and ferric-reducing antioxidant power (FRAP) assays. The phytochemical screening revealed the presence of flavonoids, terpenoids, alkaloids, saponins, tannins, steroids, and cardiac glycosides in all propolis extracts. The propolis from apiary A exhibited significantly higher TPC (302.21 ± 0.11 mg/mL GAE) and TFC values (99.08 ± 0.03 mg/mL QE) compared to apiary B and C. The results also indicated that propolis from apiary A possessed significantly higher antioxidant activities, with IC₅₀ value of DPPH at 25.27 μg/mL and FRAP value of 727.53 ± 0.09 μM Fe²+, in comparison to apiary B and C. A strong correlation was observed between TPC, TFC, and IC₅₀ of DPPH. This study highlights significant variations in the phytochemical compositions and antioxidant activities of propolis samples collected from different geographical and botanical sources. Further investigation is in progress to identify the specific phytochemical constituents responsible for these variations.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"33 35","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138601565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.11113/mjfas.v19n6.3126
Siti Nasyitah Jaman, R. Zakaria, I. Ismail
The problem of uncertain data cannot be solved by conventional methods, which results in inaccurate data analysis and prediction. During the data collecting phase, ambiguous data are often collected, but they cannot be used immediately to generate geometric models. In this case, the new approaches to intuitionistic fuzzy sets will be used to determine the alpha cut value for uncertainty data sets. To solve the uncertainty data and build the mathematical model, this study applied fuzzy set theory, intuitionistic fuzzy sets, and rational Bézier curve geometric modelling. There are three main methods in this study. The triangular fuzzy number is used to define the uncertainty data in the first place. The alpha value can then be found using a centre of mass alpha-cut. The intuitionistic alpha-cut can then be applied to both membership and non-membership data. This procedure, also called fuzzification, is defined as fuzzy intuitionistic into alpha-cut values. The data set will then undergo the defuzzification procedure to get single value data. For the purpose of analysis and conclusion-making, the modeling data for each process will be visualised using an interpolation rational Bézier curve. The findings demonstrate that using the intuitionistic fuzzy set for the alpha-cut value was more effective than the previous method without considering both membership and non-membership values.
{"title":"Fuzzy Intuitionistic Alpha-cut Interpolation Rational Bézier Curve Modeling for Shoreline Island Data","authors":"Siti Nasyitah Jaman, R. Zakaria, I. Ismail","doi":"10.11113/mjfas.v19n6.3126","DOIUrl":"https://doi.org/10.11113/mjfas.v19n6.3126","url":null,"abstract":"The problem of uncertain data cannot be solved by conventional methods, which results in inaccurate data analysis and prediction. During the data collecting phase, ambiguous data are often collected, but they cannot be used immediately to generate geometric models. In this case, the new approaches to intuitionistic fuzzy sets will be used to determine the alpha cut value for uncertainty data sets. To solve the uncertainty data and build the mathematical model, this study applied fuzzy set theory, intuitionistic fuzzy sets, and rational Bézier curve geometric modelling. There are three main methods in this study. The triangular fuzzy number is used to define the uncertainty data in the first place. The alpha value can then be found using a centre of mass alpha-cut. The intuitionistic alpha-cut can then be applied to both membership and non-membership data. This procedure, also called fuzzification, is defined as fuzzy intuitionistic into alpha-cut values. The data set will then undergo the defuzzification procedure to get single value data. For the purpose of analysis and conclusion-making, the modeling data for each process will be visualised using an interpolation rational Bézier curve. The findings demonstrate that using the intuitionistic fuzzy set for the alpha-cut value was more effective than the previous method without considering both membership and non-membership values.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"18 7","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138601840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.11113/mjfas.v19n6.3164
M. F. Karim, Mohd Razi Ismail
It has been known that the application of beneficial fungi and compost, has a favourable effect on easing water deficiency stress in plants, hence helping to boost agricultural activities in times of climate uncertainty. In this study, the influence of arbuscular mycorrhizal fungi (AMF) in combination with oil palm empty fruit bunch compost (EFB) on the growth, yield, and physiology of chilli under deficit fertigation was investigated. Throughout the study, five-week-old chilli seedlings were fertigated daily with 100% and 60% of daily evapotranspiration (ET) readings. Three days after transplanting, 10g of sandy soil containing roughly 120-150 mycorrhizal spores was applied to the root zone. Physiological data such as real-time photosynthesis and stomatal conductance were measured at vegetative, early flowering, fruit setting, and maturity or harvesting stages. Meanwhile, yield and morphological measurements were recorded at the end of the study. It was discovered that the addition of EFB to the coconut coir dust media enhanced the beneficial effects of AMF on all parameters including total biomass, chlorophyll fluorescence Fv/Fm, total chlorophylls, photosynthesis rate and stomatal conductance regardless of fertigation levels. The study also revealed that AMF inoculation alone was less effective than non-inoculation + EFB. In conclusion, it is suggested that incorporation of AMF and EFB compost positively affect the yield, growth and physiology of chilli under deficit fertigation.
{"title":"Effects of Bio-Amendment of Coconut Dust with Empty Fruit Bunch Compost on the Efficacy of Mycorrhizae Under Deficit Fertigation","authors":"M. F. Karim, Mohd Razi Ismail","doi":"10.11113/mjfas.v19n6.3164","DOIUrl":"https://doi.org/10.11113/mjfas.v19n6.3164","url":null,"abstract":"It has been known that the application of beneficial fungi and compost, has a favourable effect on easing water deficiency stress in plants, hence helping to boost agricultural activities in times of climate uncertainty. In this study, the influence of arbuscular mycorrhizal fungi (AMF) in combination with oil palm empty fruit bunch compost (EFB) on the growth, yield, and physiology of chilli under deficit fertigation was investigated. Throughout the study, five-week-old chilli seedlings were fertigated daily with 100% and 60% of daily evapotranspiration (ET) readings. Three days after transplanting, 10g of sandy soil containing roughly 120-150 mycorrhizal spores was applied to the root zone. Physiological data such as real-time photosynthesis and stomatal conductance were measured at vegetative, early flowering, fruit setting, and maturity or harvesting stages. Meanwhile, yield and morphological measurements were recorded at the end of the study. It was discovered that the addition of EFB to the coconut coir dust media enhanced the beneficial effects of AMF on all parameters including total biomass, chlorophyll fluorescence Fv/Fm, total chlorophylls, photosynthesis rate and stomatal conductance regardless of fertigation levels. The study also revealed that AMF inoculation alone was less effective than non-inoculation + EFB. In conclusion, it is suggested that incorporation of AMF and EFB compost positively affect the yield, growth and physiology of chilli under deficit fertigation.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"51 11","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138602134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.11113/mjfas.v19n6.3076
Nur Batrisyia Ahmad Azmi, R. Zakaria, I. Ismail
The notion of fuzzy sets is fast becoming a key instrument in defining the uncertainty data and has increasingly been recognised by practitioners and researchers across different disciplines in recent decades. The uncertainty data cannot be modeled directly and this causes hindrance in obtaining accurate information for analysis or predictions. Hence, this paper contributes to another approach in which an application of type-2 intuitionistic fuzzy set (T-2IFS) in geometric modeling onto complex uncertainty data where the data are defined using the type-2 fuzzy concept. T-2IFS is the generalized forms of fuzzy sets, intuitionistic fuzzy sets, interval-valued fuzzy sets, and interval-valued intuitionistic fuzzy sets. Based on the concept of T2IFS, type-2 intuitionistic fuzzy point (T-2IFP) is defined in order to generate a type-2 intuitionistic fuzzy control point (T-2IFCP). Following, the T-2IFCP will be blended with the Bernstein blending function through the interpolation method, resulting to a type-2 intuitionistic interpolation cubic fuzzy Bézier curve. Shoreline data is used as the data and further verifies that the model can be conceivably accepted. In conclusion, the proposed methods are reliable and can be expanded to many other areas.
{"title":"Type-2 Intuitionistic Interpolation Cubic Fuzzy Bézier Curve Modeling using Shoreline Data","authors":"Nur Batrisyia Ahmad Azmi, R. Zakaria, I. Ismail","doi":"10.11113/mjfas.v19n6.3076","DOIUrl":"https://doi.org/10.11113/mjfas.v19n6.3076","url":null,"abstract":"The notion of fuzzy sets is fast becoming a key instrument in defining the uncertainty data and has increasingly been recognised by practitioners and researchers across different disciplines in recent decades. The uncertainty data cannot be modeled directly and this causes hindrance in obtaining accurate information for analysis or predictions. Hence, this paper contributes to another approach in which an application of type-2 intuitionistic fuzzy set (T-2IFS) in geometric modeling onto complex uncertainty data where the data are defined using the type-2 fuzzy concept. T-2IFS is the generalized forms of fuzzy sets, intuitionistic fuzzy sets, interval-valued fuzzy sets, and interval-valued intuitionistic fuzzy sets. Based on the concept of T2IFS, type-2 intuitionistic fuzzy point (T-2IFP) is defined in order to generate a type-2 intuitionistic fuzzy control point (T-2IFCP). Following, the T-2IFCP will be blended with the Bernstein blending function through the interpolation method, resulting to a type-2 intuitionistic interpolation cubic fuzzy Bézier curve. Shoreline data is used as the data and further verifies that the model can be conceivably accepted. In conclusion, the proposed methods are reliable and can be expanded to many other areas.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"68 23","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138605043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.11113/mjfas.v19n6.3243
Nursyafreena Attan, Desmilia Putri Ramadhani, Asmi Munadhiroh, Hadi Nur
This research explores the impact of magnetic fields on dye adsorption onto graphite carbon, utilizing electric currents to generate varying magnetic field strengths, as determined by the Biot-Savart law. The study demonstrates that even with small current magnitudes typically used in physics laboratories, the generated magnetic fields significantly influence dye adsorption. Through experiments with currents ranging from 1.5 A to 7.5 A, resulting in magnetic fields from 1.54 µT to 4.63 µT, we observed enhanced adsorption for congo red, methylene blue, and methyl orange. In contrast, phenol red exhibited a unique desorption pattern due to electrostatic repulsion. Temperature variations were noted but were considered to have a negligible effect on the adsorption behavior. The findings highlight the crucial role of magnetic energy density and the charge of dye molecules in the adsorption process, leading to the conclusion that magnetic fields, indeed, play a significant role in influencing dye adsorption onto graphite carbon, with potential applications in environmental conservation and industrial waste management.
{"title":"What is the Effect of a Magnetic Field on Dye Adsorption onto Graphite Carbon?","authors":"Nursyafreena Attan, Desmilia Putri Ramadhani, Asmi Munadhiroh, Hadi Nur","doi":"10.11113/mjfas.v19n6.3243","DOIUrl":"https://doi.org/10.11113/mjfas.v19n6.3243","url":null,"abstract":"This research explores the impact of magnetic fields on dye adsorption onto graphite carbon, utilizing electric currents to generate varying magnetic field strengths, as determined by the Biot-Savart law. The study demonstrates that even with small current magnitudes typically used in physics laboratories, the generated magnetic fields significantly influence dye adsorption. Through experiments with currents ranging from 1.5 A to 7.5 A, resulting in magnetic fields from 1.54 µT to 4.63 µT, we observed enhanced adsorption for congo red, methylene blue, and methyl orange. In contrast, phenol red exhibited a unique desorption pattern due to electrostatic repulsion. Temperature variations were noted but were considered to have a negligible effect on the adsorption behavior. The findings highlight the crucial role of magnetic energy density and the charge of dye molecules in the adsorption process, leading to the conclusion that magnetic fields, indeed, play a significant role in influencing dye adsorption onto graphite carbon, with potential applications in environmental conservation and industrial waste management.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"9 4","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138602255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.11113/mjfas.v19n6.3143
Siti Sarah Saniman, M. F. Omar
This research introduces an alternative approach on materials characterization by developing an in-house X-ray Absorption Spectroscopy (XAS) system utilizing powder X-ray Diffraction (XRD) machine. The performance of the in-house XAS system was investigated by analysing the position of Cu K-edge and the absorption spectrum shape within the X-ray Absorption Near Edge Structure (XANES) region. Copper (Cu) based samples were used to test the performance of the system where Cu and Copper Oxide (CuO) thin film deposited on polyimide tape and silicon wafer (100) prepared through the deposition process carried out using RF Magnetron Sputtering machine. Phase confirmation analysis were conducted by XRD and the deposited films’ thickness were measured by Scanning Electron Microscope (SEM). The laboratory-based XAS measurement was carried out using Rigaku SmartLab X-ray Diffractometer configured for Bragg-Brentano (BB) measurement mode. Molybdenum (Mo) target was used to produce white X-rays by energizing it near 20 keV ±0.01 keV. XRD measurements on XRD and SEM analysis proves successful deposition of pure Cu and CuO thin films and the film thickness measured is 1.432 μm and 0.680 μm respectively. The conclusive findings of the laboratory-based XAS measurements indicate successful acquisition of XAS data with similar spectrum shape of experimental Cu and CuO XANES in comparison with theoretical data. Next, experimental XANES shows clear observation of Cu K-edge peaks for Cu thin film at 8.9737 keV, while Cu K-edge for CuO thin films is not observable. Lastly, there is also presence of significant XANES broadening and which then effect consequent peak shiftings.
{"title":"Probing the Electronic Properties of Cu and CuO Thin Films via XANES utilizing Powder XRD System","authors":"Siti Sarah Saniman, M. F. Omar","doi":"10.11113/mjfas.v19n6.3143","DOIUrl":"https://doi.org/10.11113/mjfas.v19n6.3143","url":null,"abstract":"This research introduces an alternative approach on materials characterization by developing an in-house X-ray Absorption Spectroscopy (XAS) system utilizing powder X-ray Diffraction (XRD) machine. The performance of the in-house XAS system was investigated by analysing the position of Cu K-edge and the absorption spectrum shape within the X-ray Absorption Near Edge Structure (XANES) region. Copper (Cu) based samples were used to test the performance of the system where Cu and Copper Oxide (CuO) thin film deposited on polyimide tape and silicon wafer (100) prepared through the deposition process carried out using RF Magnetron Sputtering machine. Phase confirmation analysis were conducted by XRD and the deposited films’ thickness were measured by Scanning Electron Microscope (SEM). The laboratory-based XAS measurement was carried out using Rigaku SmartLab X-ray Diffractometer configured for Bragg-Brentano (BB) measurement mode. Molybdenum (Mo) target was used to produce white X-rays by energizing it near 20 keV ±0.01 keV. XRD measurements on XRD and SEM analysis proves successful deposition of pure Cu and CuO thin films and the film thickness measured is 1.432 μm and 0.680 μm respectively. The conclusive findings of the laboratory-based XAS measurements indicate successful acquisition of XAS data with similar spectrum shape of experimental Cu and CuO XANES in comparison with theoretical data. Next, experimental XANES shows clear observation of Cu K-edge peaks for Cu thin film at 8.9737 keV, while Cu K-edge for CuO thin films is not observable. Lastly, there is also presence of significant XANES broadening and which then effect consequent peak shiftings.","PeriodicalId":18149,"journal":{"name":"Malaysian Journal of Fundamental and Applied Sciences","volume":"32 18","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138602427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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