Comparison of Thermal Insulation Concrete Panel Yield Based on Natural Fibres: A Review

Over time, many researchers have conducted studies to investigate the construction sector by assessing those related to energy, environmental and economic problems to find ways to improve global sustainability. The studies on the use of natural fibers: wheat, date palm and hemp as an insulating material in concrete panel yields have been conducted through ten previous research studies. In the market, there are various types of thermal insulation materials but these materials are sold at high prices and even worse some of them contain harmful chemicals that can threaten the health of consumers. This study is intended to identify ten previous research studies on the use of natural fibers in concrete panel yield as thermal insulation materials. Also, to analyse the data of density and coefficient of thermal conductivity accumulated through Microsoft Excel and propose the best concrete panels yield between these three types of natural fibers. The research was based on the value of density and coefficient of thermal conductivity of concrete panel yield. The results reveal that the presence of natural fibers in concrete panels can insulate heat well. The lowest thermal conductivity coefficient obtained from concrete of Hemp Fibre Gypsum (HG) with 0.051 W/mK. The composition of 35g of hemp fiber, 200g of gypsum and 130ml of water has shown that the amount of fibre and binder used plays an important role in determining the value of density and thermal conductivity. Finally, based on the analysis that has been conducted, found that density and thermal conductivity are inversely proportional when there is a change in the composition of fibers and binders in the concrete panel yields

An Investigation of Toilet Cleanliness Assessment and People Monitoring at Rest and Service Area (R&R) X and Y

Facility management services are essential for maintaining the effective functioning and safety of buildings. Soft facility management services aim to enhance the work environment by improving efficiency and safety. Poor toilet hygiene due to inadequate care poses a threat to human health and safety. The objectives are to measure the effect of public toilet cleanliness based on the number of people and to analyse the relationship between the level of toilet cleanliness and the number of people. Observation methods were employed to monitor the number of people and evaluate toilet cleanliness from 9:00 a.m. to 17:30 p.m. at R&R X and Y. Linear regression analysis were conducted to determine R2 values for each location and using a one-way ANOVA. The R2 values represent the relationship between the star rating and the number of people. The result shows the R2 valuesfor men’s and women’s toilets at R&R X on weekdays and weekends are R2 = 0.7643, R2 = 0.7784, R2 = 0.2479, and R2 = 0.2245. For men’s and women’s toilets at R&R Y on weekdays and weekends, R2 = 0.1733, R2 = 0.2583, R2 = 0.5591, and R2 = 0.6939. In conclusion, it was found that the R2 values on the men’s toilets at R&R X and the women’s toilets at R&R Y had a strong and moderate relationship to the number of people and star rating. Meanwhile, women’s toilets at R&R X and men’s toilets at R&R Y have a weak relationship between the number of people and the star rating. It important to maintain the cleanliness, user comfort and safety, as well as the overall satisfaction of use, to maintain 5-star rating to ensure thatusers who use public toilets feel comfortable

Parameter Magnitude-Based Information Criterion in Identification of Discrete-Time Dynamic System / Md Fahmi Abd Samad and Abdul Rahman Mohd Nasir

Information criterion is an important factor for model structure selection in system identification. It is used to determine the optimality of a particular model structure with the aim of selecting an adequate model. A good information criterion not only evaluate predictive accuracy but also the parsimony of model. There are many information criterions those are widely used such as Akaike information criterion (AIC), corrected Akaike information criterion (AICc) and Bayesian information criterion (BIC). This paper introduces a new parameter-magnitude based information criterion (PMIC2) for identification of linear and non-linear discrete time model. It presents a study on comparison between AIC, AICc, BIC and PMIC2 in selecting the correct model structure for simulated models. This shall be tested using computational software on a number of simulated systems in the form of discrete-time models of various lag orders and number of terms/variables. It is shown that PMIC2 performed in optimum model structure selection better than AIC, AICc and BIC

Selenium in human nails: a preliminar ystudy using INAA

Selenium (Se) is an essential trace element for healthy body functions in humans. Deficiency of Se may cause diseases such as cancer and cardiovascular disease. However, Se toxicity or selenosis can also occur in humans as a result of high doses of dietary intake or industrial exposure. This study aims to use human nails for biomonitoring of Se in a healthy Malaysian adult. The results are used to assess long-term Se status and comparisons are made with literature values. Instrumental neutron activation analyses ( ..

Discrete-Time System Identification Based On Novel Information Criterion Using Genetic Algorithm

Model structure selection is a problem in system identification which addresses selecting an adequate model i.e. a model that has a good balance between parsimony and accuracy in approximating a dynamic system. Parameter magnitude-based information criterion 2 (PMIC2), as a novel information criterion, is used alongside Akaike information criterion (AIC). Genetic algorithm (GA) as a popular search method, is used for selecting a model structure. The advantage of using GA is in reduction of computational burden. This paper investigates the identification of dynamic system in the form of NARX (Non-linear AutoRegressive with eXogenous input) model based on PMIC2 and AIC using GA. This shall be tested using computational software on a number of simulated systems. As a conclusion, PMIC2 is able to select optimum model structure better than AIC

Comparison Of Information Criterion On Identification Of Discrete-Time Dynamic System

Information criterion is an important factor for model structure selection in system identification. It is used to determine the optimality of a particular model structure with the aim of selecting an adequate model. A good information criterion not only evaluate predictive accuracy but also the parsimony of model. There are many information criterions those are widely used such as Akaike Information Criterion (AIC) corrected Akaike Information Criterion (AICc) and Bayesian Information Criterion (BIC). Another information criterion suggesting use of logarithmic penalty, named as Parameter Magnitude-based Information Criterion (PMIC) was also introduced. This study presents a study on comparison between AIC, AICc, BIC and PMIC in selecting the correct model structure for simulated models. This shall be tested using computational software on a number of simulated systems in the form of discrete-time models of various lag orders and number of term/variables. As a conclusion, PMIC performed in optimum model structure selection better than AIC, AICc and BIC

Performance Of Parameter-Magnitude Based Information Criterion In Identification Of Linear Discrete-Time Model

Information criterion is an important factor for model structure selection in system identification. It is used to determine the optimality of a particular model structure with the aim of selecting an adequate model. There had not been, or scarcely have been, any loss function that evaluates parsimony of model structures (bias contribution) based on the magnitude of parameter or coefficient. The magnitude of parameter could have a big role in choosing whether a term is significant enough to be included in a model and justifies ones' judgement in choosing or discarding a term/variable. This study intends to develop a new information criterion such that the bias contribution is related not only to the number of parameters, but mainly to the magnitude of the parameters. The parameter-magnitude based information criterion (PMIC2) is demonstrated in identification of linear discrete time model. The demonstration is tested using computational software on a number of simulated systems in the form of discrete-time linear regressive models of various lag orders and number of term/variables. It is shown that PMIC2 is able to select the correct the model based on all of the tested datasets

Parameter Magnitude-Based Information Criterion In Identification Of Discrete-Time Dynamic System

Information criterion is an important factor for model structure selection in system identification. It is used to determine the optimality of a particular model structure with the aim of selecting an adequate model. A good information criterion not only evaluate predictive accuracy but also the parsimony of model. There are many information criterions those are widely used such as Akaike information criterion (AIC), corrected Akaike information criterion (AICc) and Bayesian information criterion (BIC). This paper introduces a new parameter-magnitude based information criterion (PMIC2) for identification of linear and non-linear discrete time model. It presents a study on comparison between AIC, AICc, BIC and PMIC2 in selecting the correct model structure for simulated models. This shall be tested using computational software on a number of simulated systems in the form of discrete-time models of various lag orders and number of terms/variables. It is shown that PMIC2 performed in optimum model structure selection better than AIC, AICc and BIC

The Impact of Social Media Information Literacy on Malaysian Youth’s Emotional Intelligence

Social media has evolved into a network that helps users find information. Social media platforms like Facebook, Twitter and Instagram are now important sources of up-to-date news and information. In order to give people the ability to assess, inform about potential threats or effects, and be aware of false information in the network, social media information literacy (SMIL) has been developed. However, that social media can have negative emotional effects that decrease in emotional quotient (EQ) among young people will directly lead to social problems like crime. In this quantitative study, Malaysian youth between the ages of 19 and 40 were examined to determine the impact of social media information literacy (SMIL) on emotional intelligence (EQ). 241 students from three universities in Malaysia who were selected through proportional stratified random sampling and simple random sampling made up the respondents. Data are gathered using a "Google Form" questionnaire and data analysis uses descriptive and inferential statistical analysis to get the results. Social media information literacy (SMIL) and emotional intelligence (EQ) were found to be significantly correlated by Pearson correlation inference analysis. The results of this study have implications for institutions seeking to develop human capital, technology, and media. Other organizations interested in exploring the field of social media information literacy (SMIL) and emotional intelligence (EQ) can use it to meet the sustainable development goals (SDG) in digital sustainability as recommended by UNESCO

IIUM Entrepreneurship Educators Module 1.0

IUM Entrepreneurship Educators Module 1.0 is a precise and comprehensive module created and intended to guide and accelerate the IIUM Entrepreneurship Educators professional knowledge and skills development. This module provides an extensive curriculum toward development of entrepreneurs' holistic skills and knowledge from basic level of entrepreneurship, social entrepreneurship, digital entrepreneurship to business tools such as Value Proposition Canvas