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Predicting annual workers income requires ones to deep dive into several factors. Factors that majorly being discuss were age, gender, education and occupation. On the other hand, there are other factors that may affect the annual workers income where it yet to be discussed. The traditional way of predicting the annual workers income was multiple linear regression. This parametric approach requires assumptions to be fulfilled and this will actions is a time-consuming activity. Data mining approach in predicting the workers income is important to understand on how the economy and compensation work in the United States. Machine learning will cover all aspect without needing to fulfil certain assumptions as compared to traditional method. Hence, the best way to predict the worker's income in the United States is the best using machine learning and concurrently solve the SDG 8: Decent Work & Economic Growth aspect. The dataset used in this study is acquired from Kaggle website. At first, features weight using filter method (Weight by Information Gain, Weight by Information gain Ratio and Weight by Chi – Squared Statistics) were taken to identify the influential factors towards annual workers' income. The three different methods employed in the model to predict worker income are logistic regression, decision trees, and artificial neural networks. The second goal is to contrast the effectiveness of worker income prediction using under sampling and oversampling techniques. The results show that, with the exception of decision tree, oversampling strategy provides the best performance of prediction model when compared to under sampling technique. Since under sampling techniques randomly delete observations when there is a chance that such observations could be significant to the data and have an impact on the prediction model, oversampling techniques perform better than under sampling techniques. The third goal is to identify the most effective classification model for predicting worker's income. The oversampling strategy with backward selection represents the best model when applying the Logistic Regression model. Additionally, the optimal model for Decision Trees is the backward selection with under sampling strategy. The best model criterion for artificial neural networks is the oversampling method via backward selection. Data mining approach in predicting the workers income is important to understand on how the economy and compensation work in the United States. [ABSTRACT FROM AUTHOR]

Both hybrid nanofluid and porous media (e.g., sand, composite materials, and metal foams) are utilized for heat transfer enhancement and can be found during the filtration of oil flow, thermal insulation, and heat exchangers. The current investigation examines the movement of a water-based hybrid Ag-CuO nanofluid. The hybrid nanofluid flows over a stretching/shrinking sheet with a magnetic field, porous medium, and multiple slips. Then, the bvp4c solver is utilized to solve the non-linear ordinary differential equations and boundary conditions derived from the similarity transformations. The computation produces two solutions, and stability analysis is performed. The implementation of stability analysis provides novelty to this study in determining the stable and physically meaningful solution. Additionally, the analysis of other controlling parameters on the hybrid nanofluid flow is also conducted. Since only the first solution is deemed stable and has physical significance, the discussion is centered around the behavior of this solution. The porous medium is found to dwindle the momentum boundary layer. Meanwhile, the inclusion of the porous medium and the increment of nanoparticle Volume fraction of Ag enhances the heat and mass transfer rates. The imposition of the magnetic field reduces the momentum boundary layer thickness over the stretching/shrinking sheet. [ABSTRACT FROM AUTHOR]

The palm oil industry is one of the most important agro-based industries in Malaysia, with more than 400 palm oil mills nationwide. These mills are generally self-sustained in energy by converting empty fruit bunch (EFB), palm mesocarp fibre (PMF) and palm kernel shell (PKS), into energy. However, palm fuel burned in boilers have caused severe air pollution problems due to improper boiler operation and inefficient air pollution control systems. This results in emission of bottom ash particulates from multicyclone and electrostatic precipitator (ESP) hopper after combustion. This paper discusses the characteristics of palm fuel, namely PMF, PKS and EFB, based on proximate analysis, gross calorific value, ultimate analysis and element composition. Morphology of bottom ash emitted from multicyclone and ESP was also examined using scanning electron microscope (SEM). EFB was found to have the ' highest moisture content of 63.1% among others, while mixture of PMF and PKS had the highest volatile matter of 54.5% and fixed carbon of 18.5%. PMF has the highest calorific value of 2993 kcals/kg among others. The bottom ash from multicyclone and ESP exhibited particulate sizes ranging from 18.4 to 68 µm and 10.9 to 44.6 µm, respectively. These were considered as preliminary results which require further future research. The outcomes of this study are useful for mitigation of fine particulate emission from air pollution control system in the palm oil mill. [ABSTRACT FROM AUTHOR]

A composite hydrogel beads bio-sorbent was introduced through this work by cross-linking cellulose, chitosan, magnetite, and alginate (CCMA) for removal of heavy metal copper. The effect of CCMA's dosage and contact time in adsorbate solution was also studied through this work to determine the highest percentage of copper metal, Cu (II) removal. Based on the effect of CCMA dosage and adsorption contact time in copper solution studied, an excellent percentage of Cu (II) removal of 97.47% was achieved at 4g of CCMA dosage and 24-hour contact time to reach its adsorption equilibrium state. Moreover, the thermal properties and stability of CCMA investigated through TGA analysis in this work revealed that the thermal stability of CCMA was significantly improved with a high mass residual of 92.35% compared to cellulose and chitosan powder. Thus, it is proven that the cross-linking of cellulose-chitosan-magnetite-alginate (CCMA) in this work was successfully done. [ABSTRACT FROM AUTHOR]

Municipal wastewater treatment plant generates a large amount of sludge each year with a generation rate of up to 30.8 kg per population equivalent per year. These sewage sludge cakes contain an abundant amount of lipids that can be extracted, and their compound can be utilized in the production of value-added products. The separation of lipids subsequently reduces the volume of sludge to be managed and disposed of. Lipids can be separated using the conventional Soxhlet extractor, and the yield depends on process parameters such as solvent to sludge ratio, temperature, extraction time, and particle size. Among these parameters, particle size was scarcely studied while extraction time shows an insignificant effect on lipids yield. Therefore, this paper focuses on the impact of the particle size as effective extraction size as well as significant extraction parameters; temperature and sludge-to-solvent ratio on the lipids yield of extraction from the sewage sludge cake using methanol as solvent. Four sludge particle size ranges (x) were selected as the effective extraction size (x≤0.425mm, 0.4254.00mm), five solvents to sludge ratio (7, 8, 10, 13, and 20mL/g) and solvent temperature ranging from 60 to 90℃ were studied with the percentage of lipids yield as the response. The results show that the fine treatment region of x≤2.00mm resulted in higher lipids output compared to the coarse treatment region of x>2.00mm. The highest lipid of 9.70% was obtained at an effective size range of 1.00

Biodiesel is an environmentally friendly alternative to traditional petrol diesel, described as the mono-alkyl esters of vegetable oils or animal fats. Commonly, produced through transesterification with methanol or ethanol as alcohol with the help of a catalyst to increase the reaction rate and shift the equilibrium to the right. This study was conducted to determine the ideal operating conditions for alkaline-catalyzed transesterification to obtain the highest yield of FAME (%). The transesterification process was done using palm oil as feedstock reacted with methanol and assisted with potassium hydroxide(KOH) as the catalyst. Four different variables are done in the research which are the molar ratio (methanol: oil), reaction temperature (°C), reaction time (min), and catalyst loading (%). The highest yield obtained was at molar ratio 15:1, reaction temperature of 60°C, the reaction time of 120 min, and catalyst loading of 1% where the yield obtained was 87.47%, 87.73%, 87.97%, and 89.93%. However, according to a Minitab simulation, the optimum yield of 91.65% could be reached by performing the transesterification process with a molar ratio of 15:1, a reaction temperature of 60°C, a reaction time of 90 minutes, and a catalyst loading of 2%. Next, the physicochemical properties of the FAME were determined for its saponification value, iodine value, viscosity, and moisture content using the ASTM method referring to the biodiesel fuel standard ASTM D 6751. The value of these tests was then compared with the limit set by ASTM D 6751 and EN 14214 for the qualification on diesel-engine testing. Results obtained showed that the FAME produced has a saponification value of 197.80mgKOH/g, an iodine value of 58.29mgI2/100g, the kinematic viscosity of 5.06mm2/s, and moisture content of 0.0015% in the range of ASTM D 6751 and EN 14214 specifications. [ABSTRACT FROM AUTHOR]

The potential of Waste paper sludge (WPS) and cockle shell (CS) as an alternative composite absorbent has been investigated to obtain the optimum mixing ratio in removing two working parameters such as ammonia nitrogen (NH3-N) and COD. Both WPS and CS were mixed together in different ratio. The absorption batch study includes the preparation of 5g composite adsorbent, 200 mL of raw leachate sample at pH 8.64, with a shaking speed of 200 rpm for 300 minutes at room temperature. The result shows, the optimum ratio obtained for composite mixture absorbent is (1:7) and the concentration capacity achieved for the ratio was 502 mg/L and 540 mg/L for NH3-N and COD. The percentage optimum removal is 50.20% NH3-N and 81.25% COD respectively. The optimum pH and absorbent dosage expressed the result at pH 7 and 50 g with the percentage optimum removal value is 50.10% NH3-N and 88.89% COD; and 503 mg/L NH3-N and 320 mg/L COD; and 51.98% NH3-N and 89.51% COD; and 484 mg/L NH3-N and 302 mg/L COD, respectively during the experiment. [ABSTRACT FROM AUTHOR]

Many major issues occur from material failure, and Six Sigma presents a solution to those problems caused by poor production quality. Investigating and eliminating the causes of defects can improve the quality of the finished product and boost the productivity and profitability of a company. The objectives of this research are to improve product quality based on the lean Six Sigma methodology for the plastic injection moulding process. Following the Six Sigma steps of define, measure, analysis, improvement and control, many defects can be identified and avoided using an experimental method. Defects on plastic injection moulding products include flash defects, warpage and sink marks. An experiment was conducted to determine the optimal injection moulding process parameters at 31.79 MPa strength that could meet the yield strength standard. The Six Sigma method was proven to improve the quality of products manufactured in the plastics industry. [ABSTRACT FROM AUTHOR]

This article discuss electric vehicle charging scheduling algorithms to minimize the peak load demand. The charging schedule makes use of a time-of-use system and an optimum method that adjusts the amount of electricity used to charge an electric car depending on its battery's state of charge. This study was implemented in a commercial-residential area and considered various constraints to ensure the study practicality. Three scenarios of electric vehicle charging were tested and analyzed. The outcome of the first scenario demonstrated that uncoordinated charging of electric vehicles would cause a new peak in the grid load. For the second scenario, time-of-use scheme was implemented for electric vehicle charging, which was able to reduce peak loads compared to uncoordinated electric vehicle charging. The final scenario demonstrated that the suggested coordinated electric vehicle charging scheduling algorithm successfully reduced uncoordinated charging load by 33% and increased off-peak load by 87.5% while maximizing the charging power of each electric vehicle battery. [ABSTRACT FROM AUTHOR]

Blockchain technology is one of the most novel technologies that received attention from academia and practitioners in various industries because of its profound nature and the opportunities that it offers, especially in the digital age. Ethereum, which is the largest decentralized blockchain software, was introduced in 2015 and is best known for its smart contracts that facilitate different utilizations over its blockchain network. Also, it can be used as a cryptocurrency, and historically it has been the 2nd most valuable cryptocurrency following Bitcoin. In order to be able to use Ethereum smart contracts over the blockchain network, a transaction fee or a gas fee is set by the network according to that specific task. This study is aimed to develop Machine Learning (ML) models to predict the Ethereum gas fee by using a comparison of Long Short-Term Memory (LSTM) and the Facebook Prophet Model (FPM). The gas fee prediction modeling was done based on the daily dataset of eight years from 2015 to 2022. The results in this study showed that the FPM model in the scenario of this study resulted in a Mean Absolute Error (MAE) of 0.02 and a Root Mean Squared Error (RMSE) of 0.05. However, MAE and RMSE values of the LSTM model turned out equal to 0.006 which showed a higher performance and more accuracy compared to the FPM model. [ABSTRACT FROM AUTHOR]

The paper presents an IoT based water consumption management system via LPWAN gateway at OrbiWise on Ubidot Cloud Services. The data collection involves collecting of the water flow using Message Queing Telemetry Transport (MQTT) protocol from LoRa sensor Node to IoT LoRaWAN gateway at Thing Network. The total water consumption data and location based is then sent to IoT Cloud of OrbiWise and stored on Ubidot database. The ownerr consumption, latitude and longitude of sensor node are display on Ubidot Dashboard and a SMS notification is send to owner when water flow exceeds the maximum water usage level data. The owner of a home with the water monitoring system can be notify through SMS the water usage had exceed the maximum water usage level to control and save water consumption at his/her home. Experimental results show that a successful implementation of prototyped of IoT based LoRa connected for Water Consumption Management System. [ABSTRACT FROM AUTHOR]

Students' pattern of learning that does not involve active thinking will make it difficult for them to master the elements of higher order thinking skills (HOTS) in mathematics. Therefore, more effective learning methods need to be implemented to help students deal with this problem. This paper presents the results of a study after a cooperative method through paired learning was conducted on a selection of Year 6 students for the subject of Mathematics. The purpose of the study was to see the effectiveness of paired learning methods in the teaching and learning process of mathematics that apply the elements of HOTS. A total of twenty-eight Year 6 students from a school in Penang were selected as the sample for this study. Students' performance was tested before and after treatment to see if there was a difference between their performances. After that, a questionnaire was given to see the level of students' motivation and identify the factors contributing to their difficulties in mastering HOTS. From the results of this study, all students (100%) have shown an improved performance in the post-test. The mean scores from the questionnaire responses also indicated that students had a positive perception of the paired learning method. [ABSTRACT FROM AUTHOR]

Multivariate data may be contaminated by cellwise and/or casewise outliers. Cellwise outliers are individual data points within a variable that are extreme whereas casewise outliers are observations that come from a different distribution. Similar to other parametric methods, the Classical Multiple Discriminant Rule (CMDR) achieve optimal performance only when the normality assumption is fulfilled. The coexistence of cellwise-casewise outliers can disrupt the data distribution of the sample. Thus, in order to alleviate the problem, this paper employed a distribution-free estimator, Harrell-Davis Median (θ̂HD), together with Robust Covariance (SR) to construct Robust MDR (RMDRHD). The MDRs were evaluated based on misclassification rate via simulation study. The simulation results show that RMDRHD is able to achieve consistently lower misclassification rate than CMDR. Overall, the findings confirmed that the use of the distribution-free θ̂HD to robustify MDR is practical when dealing with both cellwise and casewise outliers. [ABSTRACT FROM AUTHOR]

The presence of outliers in a dataset can cause the outcome of classical statistical tools to be inaccurate. Especially in a multivariate context, where researchers have to deal with either or both cellwise and casewise outliers. This study investigated the accuracy of the Multiple Discriminant Rule (MDR) when both cellwise and casewise outliers exist in a proportionate manner. Classical MDR (CMDR) was constructed using the classical sample mean (x̅) and sample covariance (S) while Robust MDR (RMDRHL) was constructed using the Hodges-Lehmann estimator (θ̂HL) and Robust Covariance (SR). The simulation was carried out where cellwise outliers were shifted in location value and casewise outliers were involved with location, covariance and dual influence. Based on the simulation results, despite the performance of both CMDR and RMDRHL being quite close when dealing with cellwise-location and casewise-location outliers, RMDRHL outperformed CMDR in both cellwise-location and casewise-covariance as well as cellwise-location and casewise-dual conditions. In summary, the use of θ̂HL in robustifying MDR was competent even though dealing with outliers percentage beyond its tolerance. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT), uses an electromagnetic field to transmit power from the source to the load without any physical contact. This paper explains the classification of WPT and analyses the series-parallel (SP) compensation topologies for inductive power transfer (IPT). The system is designed and modelled using JMAG software. The result conveys that at 50 mm air gap and 50 kHz resonant frequency the efficiency is at 99.2%, whereas the efficiency is only 62% when the air gap is increased to 200 mm at the same resonant frequency. The output power, and efficiency, highly depend on the mutual inductance, and the mutual inductance depends on the air gap between the transmitter and receiver's coil. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) system is recognized as one of the most current renewable energy types in producing electricity power. However, one of the problems of PV system is that the performance of PV panel output is incompatible and affected due to changing climate condition. Hence, it is important to predict the actual generating output temperature of PV system. This study will cover up the implementation of PV system at the rooftop of Mega label SDN.BHD with type of poly-crystalline 405.72KWP as use for generate electricity for production purpose and to get Module temperature as actual measurement. Then, the optimal PV output temperature on the rooftop which is module temperature predict the comparison data with simple mathematical method and Artificial Neural Network (ANN). To produce ANN values then, custom MLPBP ANN network and generate in MATLAB. The results have shown that ANN has the ability to predict the PV output close to the module temperature which is the actual measurement compared to mathematical method. [ABSTRACT FROM AUTHOR]

Adsorption of methylene blue (MB) was investigated using physically prepared Durio Zibethinus Murr shell activated carbon (DZMAC). Response surface methodology (RSM) statistical technique was used to optimise the preparation conditions: activation power, activation time, and CO2 flow, with the percentage of MB dye removal and DZMAC yield as the targeted responses. Based on the central composite design (CCD), two empirical models were developed and validated by applying ANOVA analysis incorporating interaction effects of three variables using RSM to the two responses. The optimum conditions for preparing DZMAC for adsorption of MB dye were found as follows: activation power of 666-Watt, activation time of 2.83 minutes, and CO2 flow of 100 cm3, which resulted in 70% of MB dye removal and 25.21% of DZMAC yield. Experimental results obtained agreed satisfactorily well with the model predictions. [ABSTRACT FROM AUTHOR]

Defect reporting is essential software testing activity to communicate issues and errors found in the software product to the developers. To produce high-quality defect reports, apart from technical knowledge and experience, the personality of the tester plays an important role. However, studies on personality in software testing are more focused the influence of personality in testing performance, and studies exploring software defect reporting are very limited. In this research, we surveyed 105 software engineering professionals to understand the relationship between personality and their thoughts on the importance and difficulties of information to be reported in software defect reports. From the survey, we found out that the software testers/ most personality traits were agreeableness and intellect imagination. From their opinions, cause of the problem is important information to be included when describing usability defects, but writing in non-human computer interaction terms is seems difficult to them. [ABSTRACT FROM AUTHOR]

This paper describes a preliminary investigation (PI) on how the student-centered learning can be adapted through computational thinking (CT) and segmenting principle. The PI is conducted to investigate the current issues of teaching and learning and the importance of student-centered learning. This study highlights two main domains; CT and segmenting principle as well as one technology intervention to be adapted in designing a learning material that could support the student-centered learning. Articles' reviews and semi-structured interviews are two main methods used in this study. Ten respondents were selected to participate in the interview which are one content expert of CT, three lecturers and six students from Malaysian Technical University Network (MTUN). The findings of PI proved that there is a need to develop a learning material that support the student-centered learning and strengthen the findings of literature reviews that there is a lack of CT knowledge and awareness among MTUN students. Researcher highlights that a learning material related to digital video production could be designed and developed to support the student-centered learning and adapting the segmenting, CT as well as interactive mobile AR technology intervention to reduce the students' cognitive load and increase students' performance and interest in learning. [ABSTRACT FROM AUTHOR]

Sand is a precious mineral that contribute development in construction industry in this world. As sand is a vital material especially in producing concrete, it is important to seek for other alternative material such as Palm Oil Fuel Ash (POFA), which can preserve the nature. POFA is palm oil residue, rich in silica and calcium like natural sand. It is found that POFA usage in mortar is limited. Therefore, this paper presents the performance of POFA as partial sand replacement. The POFA was added in the mortar mix at 0%, 5%, 10%, 15% and 20% by weight of sand. The performance of mortar was assessed in term workability and mechanical properties. For workability, flow test was performed while compressive strength test was carried out at 1, 3, 7 and 28 days on hardened mortar cubes to measure its mechanical properties. Workability of mortar containing 5% POFA replacement level were 438 mm, which was in the range of mortar's requirement. Meanwhile, the mortar compressive strength of 10% POFA by weight of sand increased the strength to 28 MPa at 28 days. From the result, POFA showed significant result to act as an alternative and sustainable material for construction industry. [ABSTRACT FROM AUTHOR]

The performance of university students during their academic session are vital to their overall grade throughout their term in the university. There are multiple factors that could lead to the loss of performance but the foremost factor is their level of emotions. Previous research has shown that to determine the performance of the students, the best way to do so is by analysing their attention levels. With the development of portable Electroencephalogram (EEG) devices and machine learning algorithms, it is easy to obtain the students attention and emotion level during their academic sessions. This paper aims to present a method of obtaining the EEG signals using a portable EEG device and classifying it into the type of emotions that are present in the human brain. The EEG device will obtain the attention level and EEG signals during two scenarios which are lectures/tutorials and exams/quizzes. The signals are then compiled and analysed to determine the emotion labels based on a normalization process that categories the signals into positive or negative emotions. The dataset and labels are then used to train and evaluate multiple machine learning models and a deep learning model in order to determine which model has the best accuracy and performance. The chosen model is then used to predict the emotions of several students during both scenarios and the average emotions are then compared with their average attention to determine the effect of emotions on the students' performance. Hence, this paper will first provide a method on obtaining the emotion labels, followed by the models' development and finally correlating the predicted emotions with the students' performance during their academic sessions. [ABSTRACT FROM AUTHOR]

Research on user modelling has blossomed in recent years. The user model is designed to represent characteristics of users or students, including preferences, knowledge, competencies, tasks and objectives. The user model helps instructors to monitor students' learning processes and to clearly see the outcome and effectiveness of the learning. Therefore, it is necessary to fill in the gap of no research done on user modelling for each Learning Orientations Profile with regard to social interactions in e-learning. Therefore, this research was done with the objectives to investigate students' social interactions in e-learning and synthesize a user model for each learner's profile based on students' social interactions in e-learning. Accordingly, this research was done by referring quantitative research design that follows inferential approach. Therefore, firstly, students are categorized into four Learning Orientations Profiles, which are Transformance, Performance, Conformance and Resistance. All students learned through the same e-learning system for fifteen weeks. Then, the number of frequencies of their social interactions in e-learning are analyzed through the e-learning database. The interactions are including students' involvement in social interactivity functions such as forum, chatting, and messaging. The data analyzed is then synthesized into a user model for each Learning Orientations Profile. Thus, four user models for all four Learning Orientations Profiles are the expected output of this research. Significantly, in order to emphasize individual difference in learning, the user models could be used by instructors or researchers to design and develop social interactivity functions that suitable for each Learning Orientations Profile. [ABSTRACT FROM AUTHOR]

Our current education approach has evolved towards developing and equipping students with relevant skills such as creative and innovative thinking in finding solutions to any given problems. Due to unpredictable future challenges, the UN 2030 agenda of Sustainable Development Goals (SDGs) has called to action to end poverty, protect the planet, improve lives and focus on the need emphasis of Quality Education in Goal 4. Having quality education means having good educators whose role is crucial in preparing the new generations of students with prospective work skills. Conventionally, educators have the tendency of conducting laboratory class based on lab facilitator's instruction and guidance. In addition, there is no standardize approach on the grading scheme on laboratory report and practical activities been developed. Therefore, this study has developed a framework that connects the SDGs with based learning approaches in education (OBE) and enables student's performance been measured according to course outcomes (COs) and programme outcomes (POs). The OBE-SDGs related adoption has been applied to guide educators to grade students according to the appropriate level of task. Thus, this paper presents an overview framework of Open-ended Laboratory (OEL) for OBE-SDGs implementation in the Faculty of Civil Engineering, Universiti Teknologi MARA, Shah Alam. In addition, this paper describes the OEL elements in the form of rubric assessment and attainment based on the outline of POs for laboratory report and practical activities that allow continuously monitoring aligned with the aim to achieve global sustainable development. [ABSTRACT FROM AUTHOR]

The aim of this pilot study is to examined friendship maintenance behaviour and psychological well-being in single and married working women. A total of 45 women who work at non-engineering school in Universiti Malaysia Perlis were randomly selected as participant. Of these, 27 women are married and 18 women are single lady. A Friendship Maintenance scale by Oswald and Ryff's Psychological well-being scale were employed as the research instrument. This study used independent-sample t-test to compare the mean scores of variables for the groups. Result show there is no significant different in the mean scores of both variables for both groups. [ABSTRACT FROM AUTHOR]