Malaysian Air Research Articles

Integration Model of Human Dimensions, Military Minds and Patriostism

Objective: The objectives of this study are to examine the key dimensions of military minds as a determining factor of human dimensions and patriotism among Malaysian Armed Forces personnel and the seconds objective is to test the extent of mediating effects of human dimensions with military minds and patriotism.This research used quantitative research methods to get the data from the respondent of this study. The dependent variable for this study is patriotism while heroism, clear behaviour, military decency, responsibility for equipment and military skills are the independent variable and talent is the mediating in this study.The population of this study is from the Malaysian Armed Forces personnel, consist of the Malaysian Army, Royal Malaysian Navy, and Royal Malaysian Air Forces, which accounted for 0.32% of the total Malaysian Armed Forces personnel in this country. The utilised partial least square structural equation modelling (PLS-SEM) method to analyse the data and obtain results for the hypotheses generated.The results indicate of this study to be the best practice for developing training methods for future military personnel towards societal impact 4.0 (IR).This research profound implications and contribution towards Malaysian Army as managing and cultivating human dimensions with military minds and patriotism among Malaysian Army to the attainment of mission in the organization In general, this study will be beneficial for Malaysian Armed Forces to understand the importance implications of integration model of human dimensions, military minds and patriostism.

Air Pollution Index Forecasting Model using Automated Machine Learning

Malaysia's Air Pollution Index (API) has steadily climbed in recent decades, posing a severe environmental hazard. In this paper, authors used the Automated Machine Learning (AutoML) technique for, to analyse daily integer value time series data for API in Malaysia from January to December in 2019. The parameters of the models will be calculated using an error rated, and their model’s result will be compared to determine which model best fits the data. The results showed that the AutoML model was more accurate in predicting API values. The MSE value of the AutoML model and the RMSE were lower, indicating that the model had a lower error rate than traditional regression models. Furthermore, predicting ability will be evaluated using mean square error (MSE) and correlation coefficient as accuracy. The findings indicated that the AutoML model produced better results in MSE and better accuracy. The fundamental benefit of utilising the AutoML model is that it is readily automated, making it a valuable tool for academics and scientists. This model may also be used as a forecasting tool to enhance air quality in a certain location. Overall, the results indicate that the AutoML model is a viable and promising technique for predicting air pollution. The findings are critical for managing API outcomes in the future and adopting protective measures for air conservation.

Factors Affecting the Likelihood of Fraudulent in Procurement Process: the Case of Royal Malaysian Air Force

Procurement encompasses activities and processes of purchasing good products and services in the public or private sector. It has been reported that 60% of fraud and economic crime perpetrators in the public sector were from government officials. Procurement fraud has increased in the public sector since 2012. This study aims to investigate the practice and likelihood of fraud in the Royal Malaysian Air Force (RMAF) procurement process and to recommend the course to improve the effectiveness of implementing the e-procurement system. This study employed a quantitative method to achieve the objectives of the research. This study adopted a questionnaire from previous research instruments used in many studies related to fraud intention as the primary data collection method. The questionnaire was distributed to the selected officers and other ranks within RMAF. The respondents were among all officers and employees involved directly in the procurement process. Using quantitative research for the data collection method allowed the researcher to evaluate and understand this data through statistical analysis. This study's findings revealed that work commitment, capability, and opportunity had a significant association with the likelihood of fraud in the RMAF e-procurement system when Pearson's correlation was used. Results from regression analysis addressed that capability and opportunity significantly influence the possibility of fraud.

Cardiovascular, respiratory and all-cause (natural) health endpoint estimation using a spatial approach in Malaysia

In 2016, the World Health Organization (WHO) estimated that approximately 4.2 million premature deaths worldwide were attributable to exposure to particulate matter 2.5 μm (PM2.5). This study assessed the environmental burden of disease attributable to PM2.5 at the national level in Malaysia. We estimated the population-weighted exposure level (PWEL) of PM10 concentrations in Malaysia for 2000, 2008, and 2013 using aerosol optical density (AOD) data from publicly available remote sensing satellite data (MODIS Terra). The PWEL was then converted to PM2.5 using Malaysia's WHO ambient air conversion factor. We used AirQ+ 2.0 software to calculate all-cause (natural), ischemic heart disease (IHD), stroke, chronic obstructive pulmonary disease (COPD), lung cancer (LC), and acute lower respiratory infection (ALRI) excess deaths from the National Burden of Disease data for 2000, 2008 and 2013. The average PWELs for annual PM2.5 for 2000, 2008, and 2013 were 22 μg m-3, 18 μg m-3 and 24 μg m-3, respectively. Using the WHO 2005 Air Quality Guideline cut-off point of PM2.5 of 10 μg m-3, the estimated excess deaths for 2000, 2008, and 2013 from all-cause (natural) mortality were between 5893 and 9781 (95 % CI: 3347–12,791), COPD was between 164 and 957 (95 % CI: 95–1411), lung cancer was between 109 and 307 (95 % CI: 63–437), IHD was between 3 and 163 deaths, according to age groups (95 % CI: 2–394) and stroke was between 6 and 155 deaths, according to age groups (95 % CI: 3–261). An increase in estimated health endpoints was associated with increased estimated PWEL PM2.5 for 2013 compared to 2000 and 2008. Adhering the ambient PM2.5 level to the Malaysian Air Quality Standard IT-2 would reduce the national health endpoints mortality.

Application of K-Means Clustering and Calendar View Visualisation for Air Pollution Index Analysis

Two years of diurnal concentration of particulate matter (PM10 ) and nitrogen dioxide with the addition of relative humidity measurement, collected from Putrajaya, Malaysia’s ground-based measurement station from January 2014 to December 2015, were analysed. K-means clustering was employed and optimal clusters of four were identified for each year based on the most suggested number of clusters from internal cluster validation measures of the total within sum of square, silhouette index and gap statistics. Each cluster was then profiled where each mean pollutant sub-indices were calculated and the contributing pollutant to the air pollution index (API) was determined by looking at the maximum value from all sub-indices. This mechanism closely follows the Recommended Malaysian Air Quality Guidelines (RMG) for determining API. Particulate matter was found to be the dominant sub-index in all clusters and then paired with the mean relative humidity for visualisation. A calendar view was selected to show the temporal patterns and we observed a consistent cluster profile with the actual mean values of the selected parameters for most months. The calendar view also suggested that overall, the API (based on particulate matter) in 2014 was much better as compared to 2015.

Application of 3D scanning technology in Royal Malaysian Air Force Industrial Revolution 4.0‐based aircraft maintenance

Abstract3D scanning is rapidly becoming a key maintenance tool. Aerospace was a pioneer in adopting 3D scanning technology because aircraft manufacture and maintenance require precision. Monitoring deterioration, removing components for maintenance, and verifying covert operations are not practical or helpful without technology. Ultra‐precise 3D scanning is needed to ensure aeroplane airworthiness. The Royal Malaysian Air Force is facing issues maintaining the structural integrity and time‐consuming manual inspections, which exposes technicians to error and delays aircraft maintenance. Furthermore, the Sukhoi Su‐30MKM fighter jet has updated avionics, missiles, and bombs. Sukhoi's structural integrity is under jeopardy after a decade of service. This study describes the process of scanning aircraft in 3D and creating 3D representations in Su‐30MKM maintenance. The study also covers the digital transformation of aircraft construction into a model. As a result, the proposed 3D Scanning Maintenance System has assisted the maintenance team to accelerate reverse engineering, aircraft maintenance operations. Also, the 3D scanning application increases the effectiveness of repairing and maintaining Su‐30MKM in line with the Royal Malaysian Air Force's mission readiness to embrace Industrial Revolution 4.0.

Time series clustering of Malaysia Air quality time series data

Air quality is often associated with the area location and activities where air quality in cities is usually more polluted than in rural areas. This study aims to study the pattern of time series data from air quality stations by performing cluster analysis of air quality station based on the particulate matter 10 micrometres or less in diameter (PM10) and particulate matter 2.5 micrometres or less in diameter (PM2.5) time series data. The clusters obtained from the cluster analysis were compared with the station area category and station location. This study which uses air quality data obtained from the Department of Environment, Malaysia from 5 July 2017 until 30 June 2019, shows five types of air quality patterns in Malaysia. The results also show that none of the clusters is dominated by any station's category. Therefore, it is less appropriate to relate the air quality patterns and the station area category. However, the results show that air quality patterns were related to the station's location, where nearby stations have similar air quality patterns.

Machine learning methods to predict particulate matter PM 2.5.

Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM 2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM 2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM 2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM 2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM 2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.

Preface

Abstract The 2021 The Air Quality and Climate Change (1st AQCC) and The 3rd Malaysia Air Quality Annual Symposium (MAQAS) organized by Clean Air Forum Society of Malaysia (MyCAS) and supported by Department of Environment Malaysia (Air Division) and Taiwan Association for Aerosol Research (TAAR) was held on the 17th–18th November 2021 in virtual form. Due to the Covid-19 pandemic, “Combating Climate Change and Improving Air Quality in a Global Pandemic” was chosen as the theme of this year’s conference. High challenges have been faced by our team members to manage this event due to the Covid-19. However, with the collaboration and hard work among committee members and participants this event was successfully conducted virtually and managed remotely from Bangi Resort Hotel, Putrajaya and from University Malaysia Terengganu as a second centre. With that, the conference had emphasized interdisciplinary research along with practices and fosters exchanges among tiscientists, practitioners, professionals and young researchers in finding solutions and sharing latest technology in aerosol and air quality studies and work during this situation. It was an honour to receive more than 50 papers submitted to the conference and I am very pleased to announce that around 17 papers have been selected to be published in the IOP proceeding. All papers which have been accepted were subjected to a rigorous peer-reviewing process to ensure quality and consistency. Hence, it is vital to tackle air quality issues. The conference also served as an opportunity in establishing networking among scientists, practitioners, professionals and young researchers in international regions in strengthening international collaboration. A wide range of topics was covered from fundamental, applied and technology aspects of aerosol science and air quality that focused on monitoring, control strategies, health impact, policy and management, big data and air pollution modelling using earth observation data, atmospheric transport and emissions from combustion process. I acknowledge with gratitude the assistance rendered by the scientific team of 2021 AQCC and MAQAS was led by Prof. Dr Juliana Jalaludin and her publication team along-with all the track chairs with continuous support and advice led to the smooth publication of this issue. Last, but not least, we thank the authors, reviewers and the conference secretariat for their contribution which has enabled this publication to become a reality. Hopefully, all participants and other interested readers will benefit scientifically from the proceedings and also find it stimulating in the process. With our warmest regards, Ts Dr Noor Zaitun Yahaya (AHEA) The Conference Chair lists are available in the pdf

Air Quality Status During Pandemic Covid19 in Urban and Sub-Urban Area in Malaysia

Abstract This paper discussed the API status in the urban and sub-urban cities in Malaysia before, during and after Movement Control Order (MCO) was implemented in year the 2020. The lockdown implemented worldwide including Malaysia has come as a respite to the environment, especially in air pollutant levels. 2 cities have been chosen in this study that is Kangar (suburban) and Cheras (urban). The Air Pollutant Index (API) data recorded on an hourly basis throughout different series of national lockdown phases was obtained from the Malaysian Air Pollutant Index website. The findings show that the API status quality in sub-urban does not change much compare to the urban area. But it can be seen that the API reduces during MCO implementation in the sub-urban and urban areas. This is because, during the MCO, there are several restrictions on mass movement and gatherings, and the closure of educational institutions, government and private agencies (except for essential services). This MCO restriction reduced the traffic density, industrial activities, and other social activities.

Maintenance Strategy Selection using Fuzzy Delphi Method in Royal Malaysian Air Force

The proper maintenance strategy is significant in extending assets and equipment, thus saving maintenance within an organization. Currently, there are three types of maintenance strategies implemented in the Royal Malaysian Air Force (RMAF), namely Reactive Maintenance (RM), Preventive Maintenance (PM), and Condition Based Maintenance (CBM). Due to the constraints in terms of maintenance costs by RMAF, choosing the right maintenance strategy is important to ensure that the maintenance provision can be optimized. In this research study, the Fuzzy Delphi Method has been used as a tool in determining the most effective maintenance strategies to be adopted by the RMAF. The output of agreement and opinion from experts in the related field has been used to select the appropriate maintenance strategy. In choosing this maintenance strategy, goals are set first in line with RMAF maintenance’s objectives. The specified maintenance goals are as follows; low maintenance cost, reducing the chance of a breakdown, safety, feasibility on the acceptance by labor, and response time starting from failure. Later, the result showed that the fuzzy score for RM, PM, and CBM was 0.747, 0.789, and 0.767, respectively. The highest fuzzy score showed the most accepted method chosen by the expert. Based on the result and maintenance goals that have been outlined, experts have agreed to choose PM as a maintenance method that should be given priority to be implemented in RMAF compared to other maintenance methods due to the highest fuzzy score.

Measurement of Intangible Human Elements in Determining Military Combat Readiness

Combat readiness is a composite concept that encompasses both tangible military logistics and intangible human components associated with soldiers preparing for combat missions. Militaries throughout the world define combat readiness as their military doctrine, policies, and public communications being in place to prepare their military troops for combat responsibilities. Situational Force Scoring (SFS) is a quantitative tangible measurement of forces' combat readiness in which numerical tangible scores are assigned based on logistics and manpower required percentages. To complete the equations for combat readiness, this research will identify and quantify the soldier's intangible human components of intangible combat readiness in the areas of morale, quality of life, and military psychological aspects, along with four antecedents for each domain. This quantitative study demonstrates military troops from operating units of the Royal Malaysian Navy and Royal Malaysian Air Force throughout Malaysia. The model was statistically validated against the data (n = 2466) using PLS-SEM. The findings reveal that external characteristics such as morale, quality of life, and psychological well-being account for 62.9 percent of intangible combat readiness. The results reveal that morale (= 0.578) has a greater direct effect on the measure of combat readiness than psychological factors (= 0.171) or quality of life factors (= 0.091). Morale is a critical component in intangible combat readiness enhancement. The findings indicate that boosting efforts to improve personnel morale will undoubtedly improve the Malaysian Armed Forces' soldiers' intangible combat readiness. Efforts to enhance the effectiveness of quality of life and psychological issues must be prioritised.

GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms.

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.

The Effects of Air Quality on Hospital Admissions for Chronic Respiratory Diseases in Petaling Jaya, Malaysia, 2013–2015

This study assesses the impact of a decrease in air quality and the risk of hospital admissions to a public hospital for chronic respiratory diseases for residents of Petaling Jaya, a city in the Greater Kuala Lumpur area in Malaysia. Data on hospital admissions for asthma, bronchitis, emphysema and other chronic obstructive pulmonary disease, weather conditions and the Malaysian Air Pollution Index, a composite indicator of air quality, were collated. An unconstrained distributed lag model to obtain risk of hospitalization for a 10 μg/m3 increase in the API. The lag cumulative effect for a 10 μg/m3 increase in the API was calculated to test for harvesting in the short term. Findings indicate that after an initial decrease in admissions (days 3 and 4), admissions increased again at day 7 and 8 and this relationship was significant. We therefore conclude that a 10 μg/m3 increase has a greater effect on admissions for respiratory health in the short term than a harvesting effect alone would suggest. These results suggest that while air quality is improving in the Greater Kuala Lumpur area, no level of air pollution can be deemed safe.

Source of apportionment of Air Quality Parameters at Federal Port of Malaysia with Emphasis on Ship Emission

Air pollution is becoming a major environmental issue in Malaysia. Centered on data obtained from the Malaysian Department of Environment (DOE), this analysis focused on identifying possible causes of air quality heterogeneity across the research area. 14 air quality parameters in 7 monitoring stations for ten years (2009 – 2018) were gathered. To determine the source of air pollution around the study region, the Principal Component Analysis (PCA) approach from a chemometric methodology was applied. The PCA method has identified three varimax factor (VF) with 11 air quality parameters are the most significant parameters around the study area. It can be inferred from the analysis that the use of the PCA approach in chemometric techniques can be applied for the purpose of source apportionment. Accordingly, this study suggested that efforts should be put as a priority in the monitoring of ships emissions sources in Malaysia Federal Ports area and successful management of Malaysian air quality.

Willingness to carbon offset: value of malaysian air travellers’ experience, general and specific environmental knowledge

Greenhouse gas emissions from the aviation sector contribute significantly to global warming. Air transportation contributed 4% to 9% of total CO2 emissions in Malaysia. Several approach have been done by aviation industries in Malaysia to reduce carbon emissions from airlines, but still unsuccessful to implement carbon offset program. This study investigated experience, understanding of carbon offsets and the information passengers have concerning environmental issues and the ability to evaluate and understand impact on society and the environment among Malaysian airlines passengers. A descriptive statistics is reported as data collected from 407 Malaysian airlines passengers. The result shows only 1.5% of passengers experienced bought carbon offset schemes in past flight, more than 83% are knowledgeable with environment knowledge and more than 75.7% enlightened with understanding of carbon offsets. The finding obtained will help in providing insightful understanding about Malaysian airlines passengers’ acceptance on carbon offsets and willingness to contribute with carbon offsetting.

A review of the effect of climate change on malaysia's air conditioning system and dependability

The results of research on the impact of climate change on air conditioner performance and dependability in the tropics are presented in this article. The effect was primarily concentrated on three aspects: cooling or heating load, energy usage, and outside design conditions for the air conditioning system. The relevant articles were divided into three categories, and theoretical and experimental investigations were analyzed. Since the last few decades, many studies have been conducted on the effects of climate change on air conditioners in the tropics, but most of them have focused on current cooling as well as heating demands, regional overall electricity consumption, as well as existing outdoor design requirements for the air conditioning system rather than the potential impacts on heating, ventilation, as well as air conditioning systems. Based on the literature review, more extensive research is required to gain a clearer picture of the climate variation effects on air conditioning systems, so that a comprehensive guideline for future air conditioning system design in the tropics that takes into account possible climate change can be prepared.

Burning mouth syndrome caused by xerostomia secondary to amlodipine

Background: Xerostomia, generally referred to as dry mouth, has been identified as a side effect of more than 1,800 drugs from more than 80 groups. This condition is frequently unrecognised and untreated but may affect patients’ quality of life and cause problems with oral and medical health, including burning mouth syndrome (BMS). Purpose: The purpose of this case is to discuss how to manage a patient with BMS caused by xerostomia secondary to medication that has been taken by the patient. Case: We reported that a 45-year-old male military officer from the Royal Malaysian Air Force came to Kuching Armed Forces Dental Clinic with dry mouth and a burning sensation since he started taking 10 mg of amlodipine due to his hypertension. After a thorough physical and history examination, we made a diagnosis of burning mouth syndrome (BMS) caused by xerostomia secondary to amlodipine. Case Management: Oral hygiene instructions, diet advice and prescription of Oral7 mouthwash has been given to reduce the symptoms of BMS. The patient has been referred to the general practitioner to reduce his amlodipine dosage from 10 mg to 5 mg (OD) in order to prevent xerostomia, and oral hygiene instructions have been given. A review after two weeks showed significant changes in the oral cavity, and the patient was satisfied as he is no longer feeling the burning sensation and can enjoy his food without feeling difficulty in chewing and swallowing. Conclusion: Adverse drug events are normal in the oral cavity and may have a number of clinical presentations such as xerostomia. Xerostomia can cause many implications as saliva helps in maintaining oral mucosa and has a protective function. The signs of adverse drug incidents in the oral cavity should be identified to oral health care professionals.

Perception of Search and Rescue Crew of Malaysian Police Air Wing towards General Issues of Search and Rescue

Perception of Search and Rescue Crew of Malaysian Police Air Wing towards General Issues of Search and Rescue

Evaluation of air quality in Sunway City, Selangor, Malaysia from a mobile monitoring campaign using air pollution micro-sensors

Due to the increase of the human population and the rapid industrial growth in the past few decades, air quality monitoring is essential to assess the pollutant levels of an area. However, monitoring air quality in a high-density area like Sunway City, Selangor, Malaysia is challenging due to the limitation of the local monitoring network. To establish a comprehensive data for air pollution in Sunway City, a mobile monitoring campaign was employed around the city area with a duration of approximately 6 months, from September 2018 to March 2019. Measurements of air pollutants such as carbon dioxide (CO2) and nitrogen dioxide (NO2) were performed by using mobile air pollution sensors facilitated with a GPS device. In order to acquire a more in-depth understanding on traffic-related air pollution, the measurement period was divided into two different time blocks, which were morning hours (8 a.m.–12 p.m.) and afternoon hours (3 p.m.–7 p.m.). The data set was analysed by splitting Sunway City into different zones and routes to differentiate the conditions of each region. Meteorological variables such as ambient temperature, relative humidity, and wind speed were studied in line with the pollutant concentrations. The air quality in Sunway City was then compared with various air quality standards such as Malaysian Air Quality Standards and World Health Organisation (WHO) guidelines to understand the risk of exposure to air pollution by the residence in Sunway City.