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Optimisation of co-culture fermentation of lactobacillus casei and propionibacterium jensenii in rice bran extract
Get PDFCo-culture fermentation is a fermentation process involving two defined microorganisms growing together in the same culture. A co-culture of lactic acid-producing bacteria (LAB) and propionic acid-producing bacteria (PAB) is beneficial in producing direct-fed microbial (DFM) products. The synergistic activity between LAB and PAB in co-culture fermentation can improve the survival of LAB and the growth of PAB. On this basis, the objectives of this study are two-fold. Firstly, the optimisation of co-culture fermentation involving Lactobacillus casei and Propionibacterium jensenii in the agricultural waste extract. Secondly, the development of an artificial neural network (ANN) predictive model for predicting the cell biomass concentration and the co-culture-specific growth rate. In the preliminary phase, two different substrates, namely rice bran and banana peel, were used in this study. This step was conducted to select the suitable carbon source for L. casei to grow and produce lactic acid for P. jensenii consumption. From the observation, rice bran was found more suitable as a carbon source and fermentation medium. Next, the co-culture optimisation of L. casei and P. jensenii fermentation was conducted using the one-factor-at-a-time approach. The fermentations were optimised for rice bran at concentration of 5% to 25% w/v; incubation temperature (30? to 42?); inoculation ratio (1:1 to 1:10 % v/v) and the initial pH (5.0 to 7.0). The optimum fermentation condition was obtained at 20% w/v rice bran concentration, incubated at 35? with an inoculation ratio of 1:4 % v/v and initial pH of 6.5. The optimum growth (2.74 g dry cell weight/L) was recorded after 96 hours of incubation. The highest viable cell counts for L. casei and P. jensenii were 9.10 log CFU/mL and 9.42 log CFU/mL, respectively. The optimum specific growth rate, µ obtained, was 0.41 h-1. The growth of L. casei and P. jensenii was compared to its monoculture fermentation, and it was found that the co-culture did not affect the growth of L. casei but helped maintain its survival. Moreover, P. jensenii gained benefits in the co-culture system, as its growth improved compared to during its monoculture. The ANN predictive model was developed using the multilayer perceptron and trained using the Levenberg-Marquardt training algorithm. Five input parameters, incubation time (h), the concentration of total reducing sugar (g/L), pH culture, incubation temperature (?) and inoculation ratio (% v/v), were used to train the network for the prediction of cell biomass concentration (g/L) and the co-culture specific growth rate, µ (h-1). The model has a low mean square error and high regression coefficient (R2) for the training and testing set, indicating the model is fit to predict the cell biomass produced and its specific growth rate during the co-culture of L. casei and P. jensenii. The structure obtained for ANN predictive model consist of five inputs, eight hidden nodes and two outputs, 5-8-2. The optimum predicted cell biomass concentration and the specific growth rate, µ, were 2.24 g dry cell weight/L and 0.51 h-1, respectively. In conclusion, this work provides a strategy to produce multispecies DFM through co-culture fermentation using rice bran and presented the first predictive ANN model to predict the cell biomass concentration and the co-culture-specific growth rate of L. casei and P. jensenii
Prediction of student‘s academic performance during online learning based on regression in support vector machine
No full textSince the Movement Control Order (MCO) was adopted, all the universities have implemented and modified the principle of online learning and teaching in consequence of Covid-19. This situation has relatively affected the students’ academic performance. Therefore, this paper employs the regression method in Support Vector Machine (SVM) to investigate the prediction of students’ academic performance in online learning during the Covid-19 pandemic. The data was collected from undergraduate students of the Department of Mathematics, Faculty of Science and Mathematics, Sultan Idris Education University (UPSI). Students’ Cumulative Grade Point Average (CGPA) during online learning indicates their academic performance. The algorithm of Support Vector Machine (SVM) as a machine learning was employed to construct a prediction model of students’ academic performance., Two parameters, namely C (cost) and epsilon of the Support Vector Machine (SVM) algorithm should be identified first prior to further analysis. The best parameter C (cost) and epsilon in SVM regression are 4 and 0.8. The parameters then were used for four kernels, i.e., radial basis function kernel, linear kernel, polynomial kernel, and sigmoid kernel. from the findings, the finest type of kernel is the radial basis function kernel, with the lowest support vector value and the lowest Root Mean Square Error (RMSE) which are 27 and 0.2557. Based on the research, the results show that the pattern of prediction of students’ academic performance is similar to the current CGPA. Therefore, Support Vector Machine regression can predict students’ academic performance
Social innovation across non-profit organisations: analytical hierarchical approach
No full textThis work aims to assess social innovation across non-profit organisations of the United Arab Emirates. This work aims to assess social innovation across non-profit organisations of the United Arab Emirates by exploring the criteria and sub-criteria of social innovation relevant to UAE NGOs. The work develops a hierarchical framework of social innovation using the analytic hierarchy process (AHP) model. Needed data was collected from top directors and senior managers at NGOs in the UAE through personal interviews. All to recommend strategies for improving social innovation practices in NGOs of the UAE. The proposed framework should help NGOs and social innovators to improve their social innovative practices. Literature on social innovation in the UAE context is limited, and data was collected from NGOs in the UAE only. This work provides a comprehensive strategy for improving social innovation across NGOs of the UAE by contributing to the emerging field of social innovation literature
Site coefficient and design spectral acceleration evaluation of new Indonesian 2019 website response spectra
Get PDFCalculation of site coefficient and design response spectral acceleration are two important steps in the seismic design of buildings. According to Indonesian Seismic Code 2019, two information requirements for site coefficient calculations are the site soil class and Risk-targeted Maximum Considered Earthquake (MCER-SS for short and MCER-S1 for long period) spectral acceleration. Three different hard/SC, medium/SD and soft/SE are typically site soil classes used for building designs. Two different site coefficients (Fa for MCER-SS and Fv for MCER-S1 spectral acceleration) are used for surface and design response spectral acceleration calculations. The Indonesian Seismic Code provides two (Fa and Fv) tables for calculating site coefficients. If the MCER-SS or MCER-S1 values developed for a specific site are not exactly equal to the values in Fa or Fv tables, the site coefficients can then be predicted using straight-line interpolation between the two closest Fa or Fv values within the tables. When the straight-line interpolation is adjusted for Fa or Fv calculation, different results were observed in comparison to the values developed using website-based software (prepared by Ministry of Public Works and Human Settlements). This study evaluates site coefficients and design response spectral acceleration predictions in Semarang City, Indonesia, according to straight-line interpolation method and website software calculations. The study was conducted at 203 soil boring positions in the study area. The site soil classes were predicted using average standard penetration test values (N-SPT) of the topmost 30 m soil deposit layer (N30). Three different site soil classes were observed in the study area. On average, the largest differences between the two analysis (linear interpolation and website) methods in the site coefficient values and design response spectral acceleration calculation were observed for the SD and SE classes. However, for the SC site soil class, the difference was small, with their values approximately similar
A reconstruction study on the 30-wheeler ceremonial vehicle of Melaka Sultanate
Get PDFPortuguese historical sources mentioned the existence of a 30-wheeler ceremonial vehicle that belonged to the Sultan of Melaka embedded in the 1511 war narrative. This report documented several measurements on the vehicle but was rarely analyzed academically. This project's fundamental goal was to reconstruct the arguably historical and extinct vehicle. It employed narrative analysis in dealing with textual clues involving components and measurements of the vehicle. Moreover, each component was carefully scrutinized as an integrated whole. Visual anthropological research was applied to cross-compare related historical visuals involving a Dutch Melaka sketch of a similar concept vehicle. It also investigates the route to where the vehicle was driven which has direct and indirect implications on its design. Subsequently, design thinking was applied to pursue the design process to achieve the research’s reconstruction objective. The analysis and design process of the reconstruction consider the context in which the vehicle was used. According to the findings of this study, this Malay Sultanate ancient vehicle has a unique form and has complex mechanical design and maneuvering capability. Nevertheless, it is not comparable to the Malay royal vehicles that existed during the Dutch Melaka period. The study has limitations since it relies on English translation in dealing with ancient Portuguese texts. The long-term goal of this reconstruction study is to promote historical Melaka identity tourism, which is in line with SDGs 8.9 and 11.4
Dynamics of contextual factors, technology paradox, and job performance in smartphone usage: a systematic review
Get PDFThe purpose of this paper is to explore the theories pertaining to the dynamics of contextual factors, technology paradox, and job performance of employees so as to answer specific questions related to the theories’ progressive advancement, and to evaluate the relationships among them in the context of mobile phones, using the evidence-based systematic review methodology. The term technology paradox has evolved over past decades, and theories have been postulated to explain its nature and relationships with its antecedents and outcomes; however, there is a dearth in the integrative models. Thus, the theory of paradox has been combined with other theoretical lenses to conceptualize tensions and responses to enrich extant theories on technology paradox and job performance. The finding of the study identifies seven research gaps in the available literature, which need to be plugged so that a holistic model is developed to address the interrelationships among the aforementioned research constructs
Evaluating the role of sodium dodecylbenzene sulfonate as surfactant towards enhancing thermophysical properties of paraffin/graphene nanoplatelet phase change material: synthesis and characterization in PV cooling perspective
No full textNanoparticles addition in the phase change material (PCMs) has been proved to improve its thermophysical properties. However, it also has been reported to cause agglomeration, which will counteract the thermophysical enhancement. The addition of surfactant to this matrix is believed to reduce the agglomeration. However, no comparison studies reported how the addition of surfactant in nano-enhanced phase-change material (NPCMs) improves its thermophysical properties and has assessed its performance in enhancing the temperature-reduction characteristics of PV panels under field-testing conditions. Hence, this work aims to experimentally evaluate the impact of adding surfactants to the NPCMs matrix to improve its morphological and thermophysical properties and evaluate its performance in outdoor conditions. Graphene nanoplatelet (GNP) with 1, 3, and 5 wt% (PG1, PG3, and PG5) was added to the paraffin wax (PW), followed by the addition of sodium dodecylbenzene sulfonate (SDBS) as the surfactant (PGS1, PGS3, and PGS5). Thermophysical properties such as latent heat, specific heat capacity, thermal conductivity, and total heat stored were investigated. The best improvement was shown by sample PGS5 (PW/5 wt% GNP with SDBS) with the performance of; (a) 43.2% improvement in latent heat, (b) 69.5% improvement in specific heat capacity, (c) 73.45% enhancement of heat transfer rate, (d) total heat stored with 64.13% improvement, and (e) relative enhancement by a factor of 25.94 in thermal conductivity. On-site evaluation on PV module also showed the reduction of temperature as high as 44.2%. All this proving the importance of SDBS to improve the thermophysical properties and suitability as a PV module coolant
An action research on e-commerce adoption for a frozen food manufacturer in Malaysia
Get PDFThe objective of this research action is to introduce interventions into a frozen food manufacturer (CS), and assists the company venture into online sales channels. This research action will assist the company to generate income from a new profit stream as especially during this COVID-19 pandemic. This action research involved redesign product packaging, improve production process, design marketing strategy, and forming new delivery service provider collaboration. The intervention team identified Shopee.my is the suitable starting selling channel as it provides the most competitive advantage. The intervention team, launched a series of promotion and reach out the target group of customers by using Facebook advertisement. Along the intervention, there are some unintended results which affected the intervention plan, but the team was successfully overcome it. The intervention was taking place on 17th March until 16th May 2020 and successfully generate RM116,690 of revenue to the company and increase the gross profit margin from 25% to 33% for these online sales transactions. The intervention shown positive unintended results too which open up some distributor partnership at other state of Malaysia. Meanwhile for cycle two intervention, a distribution hub was setup in Oct 2021, in order to improve delivery service which is the main complaint issue received during year 2020 and 2021. The intervention is implemented and success maintaining Selangor area online revenue as the pilot project meanwhile other area revenue been decreased 35% to 67% due to the movement control lifted, and public tend to have dine in instead of cook at home. Overall, the action research assisted CS Foodstuff Sdn Bhd to setup an online store from zero, and provide new profit stream to the customer as well as improve CS product appearance in digital channel
An improvement of employees job satisfaction and work performance by using the (leader member exchange) LMX theory at Geomatika University College (GUC)
No full textThroughout the years, it is evident that employee’s job satisfaction and work performance becoming the main challenge to the organizations. The increase in turnover among employees in the organization has become an exciting topic for research. The increasing rate of employees’ turnover has made employee engagement to be an important issue. The organizations believed that employee engagement is one of the determinants to examine the withdrawal behaviour. The purpose of this action research is to identify present factors that influenced employee job satisfaction and work performance in Geomatika University College. At the same time, the research aims to propose training and development programs as an intervention plan towards the organization and hence to evaluate the effectiveness of training and development in the process of mitigating withdrawal behaviour among employees in Geomatika University College. This research was design to obtain intimate knowledge on how employees feel towards establishment of flexibility in working environment. Generation X and Y are already mostly attempting to get more compensation from bosses after they have fulfilled with their duties that have been assigned to them. If they have not been happy with their top management of the business, they will be using the social networking site to express their emotions, and this will influence their image. Therefore, workers will be demotivated, and their efficiency will be demotivated.The career development was also important to the employees to achieve work safety performance and job satisfaction. When they did not get any appreciation from the top management, they will feel frustrated, and their performance will be affected. This situation happened because their employees are not satisfied with their immediate manager and the management itself
Robust PRIDIT scoring method for classification fraud cases in financial data
No full textIncreasing number of fraud cases could jeopardize business solvency. Identification of fraud using effective statistical methods, such as classification, can protect organisations from this pitfall. However, identifying fraud cases can be a statistical challenge due to several characteristics of financial datasets. These data typically form large datasets that are highly dimensional, contain mixed data types and can involve an imbalanced number of fraud and non-fraud cases. This study employed the Principal Component Analysis (PCA) based on Relative to an Identified Distribution (RIDIT) scores, known as the PRIDIT method, to classify and identify data that could potentially be fraudulent cases. The classical PRIDIT method involves the transformation of each analysed dataset into a probability scale, RIDIT score. PCA is then employed to the RIDIT score data matrix to capture the highest variability in the dataset. However, the classical PRIDIT method framework has a limitation in the form of the PCA based Pearson correlation’s measures being insensitive to the variability of the data. In addition, there are no specific measurements for assessing the PRIDIT method’s performance under different data characteristics. Hence, this study proposed a robust PRIDIT methodology framework by incorporating several robust estimators (M-Huber, M-Tukey Bisquare, MM and LTS estimators) to improve the performance of classification tasks in identifying potentially fraudulent case data. The proposed method is applied on a German Credit Card Dataset. The analysis indicates that the highest accuracy rate of 48.5% was obtained by robust PRIDIT based on M-Tukey Bisquare estimator, followed by the results of robust PRIDIT based on MM and LTS estimators, which show similar accuracy scores of 48.1% with classical PRIDIT. The lowest accuracy score was obtained by robust PRIDIT based on M-Huber at 47.9%. A simulation study was also conducted to assess the performance of different PRIDIT methods. Behaviours of different PRIDIT methods were observed under different credibility percentage settings (Non-Fraud (NF); Fraud (F) cases, 95%NF;5%F, 90%NF;10%F, 80%NF;20%F and 70%NF;30%F) and variability levels (low, medium and high) in the datasets. The simulation results show that the accuracy rate obtained by classical PRIDIT, robust PRIDIT based M-Tukey Bisquare, MM, LTS and Huber are 64.3%, 65.3%, 65%, 63.7% and 61.7% respectively at credibility setting (70%NF;30%F) and medium variability. Thus, the findings indicate that the robust PRIDIT based on M-Tukey Bisquare outperform the other estimators by achieving the highest accuracy rate of 65.3%. In addition, the robust PRIDIT method also has a better rate of accuracy when data variability is medium or high compared to the classical PRIDIT method. Thus, this study has introduced a new method using robust PRIDIT to assess the credibility of financial data effectively