Search

Ionospheric scintillation is a common phenomenon in the equatorial, auroral, and polar areas, posing a threat to the Global Navigation Satellite System's (GNSS) operation. This ionospheric effect is thought to impact satellite signal propagation, hence, lowering positioning accuracy. Therefore, detecting ionospheric scintillation is critical in enhancing GNSS performance, particularly at low latitudes with severe amplitude scintillation. This paper investigates the effects of ionospheric scintillation on GPS signals at UNIMAS station, the East part of Malaysia. This study aims to identify the occurrence of ionospheric scintillation by calculating the index of scintillation strength during solar maximum 2014. The amplitude scintillation, which is measured in the S 4 index is obtained and classified accordingly from the data recorded at the UNIMAS station. The results have shown that strong scintillation (S 4 ≥ 0.5) occurred in the spring (April) and autumn (September and October) seasons, reaching 8 incidents. This paper also presents the diurnal, seasonal, and solar activity dependence of ionospheric scintillation. It is evident that the occurrence of amplitude scintillation is influenced by variations in solar activity, including solar flares and sunspots, as well as diurnal and seasonal changes. These findings provide valuable insights into the behaviour of the ionosphere and its impact on space-based communication and navigation systems. [ABSTRACT FROM AUTHOR]

Hand, foot, and mouth disease (HFMD) is a viral infection that primarily affects children. The disease is characterized by fever, sores in the mouth, and a rash on the hands, feet, and buttocks. In this case study, we analyze the dynamics of HFMD in children under 15 years old in Malaysia using an age-structured modeling approach. First we prove backward bifurcation for a baseline model which is then extended to an age-structured model. We used data on the incidence of HFMD in Malaysia from 2021 to 2022 to calibrate the model parameters and simulate the disease dynamics. Sensitivity analysis is done by Latin hypercube sampling - partial rank correlation coefficient (LHS/PRCC) method. Simulation is done and the impact of imperfect vaccine and quarantine is explored. Our results show that the dynamics of the disease is mostly driven by children under 5 years old. The model also predicts that vaccination could significantly reduce the incidence of HFMD in Malaysia. Vaccination is most effective when targeted at young children, who are at the highest risk of disease as this will lead to drastic decrease in the incidence of HFMD in all age groups. Our simulations show that quarantining of infected children until they fully recover will reduce the transmission of HFMD, but this strategy needs to be sustained for a sufficient duration to have a significant impact. The result of this paper can inform the development of policies for controlling the spread of HFMD and other infectious diseases in Malaysia and other similar settings. • A novel age-structured mathematical model of HFMD. • Interactions between different age groups are investigated. • The model is calibrated with real-life data. • Sensitivity analysis is performed to illustrate the role of parameters with respect to disease control. • Prove backward bifurcation due to the incorporation of imperfect vaccination. [ABSTRACT FROM AUTHOR]

• Sunbelt countries can develop reliable solar-dominant grids in the absence of wind. • Policymakers should be highly sceptical of low solar technical potential estimates. • Zero energy emissions Malaysia requires about 0.7 TW solar, 2 TWh pumped hydro. • Sarawak-Peninsular Malaysia interconnection reduces storage needs, but not costs. • Inclusion of about 0.4% hydrogen or synthetic methane reduces storage needs and cost. Three-quarters of the global population lives in the sunbelt region (±35o latitude) with excellent access to solar resources. Wind resources at the equator are very limited, while supply chain and permitting delays are slowing wind deployment at a global scale. This paper investigates the potential for solar photovoltaic-dominant 100% renewable grids to be developed in sunbelt countries through a case study of Malaysia. It was expected that the technical potential for solar photovoltaics and off-river pumped hydro would be far in excess of what is required to decarbonise Malaysia's electricity grids. A Geographic Information System analysis determined that Malaysia has the potential to deploy approximately 8.5 Terawatts of terrestrial photovoltaics and 25 Terawatts of marine floating solar on calm oceans. There are roughly 450 Terawatt-hours of energy storage available in the form of off-river (closed-loop) pumped hydro energy storage. A least-cost, 16-year hourly energy balance model was used to determine that an affluent, fully electrified, highly interconnected Malaysia could greatly reduce emissions through the deployment of 660 Gigawatts of photovoltaics and 2200 Gigawatt-hours of pumped hydro, supported by small amounts of hydrogen-based gas peaking. All-in costs for such a fully decarbonised energy system were modelled to be in the range of 95–103 USD per Megawatt-hour. A Sarawak-Peninsular Malaysia interconnected grid could produce a similar electricity cost to two separate grids while reducing the long-term energy storage needs by approximately 33%. Inclusion of about 3 Terawatt-hours (0.1% of average annual generation at 20 Megawatt-hours/capita/year demand) of hydrogen, synthetic methane, or legacy fossil fuels could further reduce long-term energy storage needs by about 30% by helping to ride through occasional prolonged cloudy periods. Policymakers in sunbelt countries should be highly sceptical of technical potential estimates that suggest limited area for solar photovoltaics. Sunbelt countries can comfortably proceed with the development of cheap, reliable solar-dominant grids primarily supported by pumped hydro energy storage and inter-regional transmission. [ABSTRACT FROM AUTHOR]

[Display omitted] • We looked into the potential applications of hydrate-based desalination (HBD) • HBD process treats produced water with a concentration 2.8 wt% • CO 2 as guest gas produced a water recovery of 45.55% • The desalination efficiency is strongly dependent on the ionic radii and strength of the ionic charge. This paper emphasizes sustainable development goal 6 and accepts that social progress and economic prosperity depend on the appropriate management of produced water. Produced water being the largest source of wastewater, several conventional desalination methods (thermal, membrane and freeze–thaw), are applied to obtain treated water. Nevertheless, the methods are highly energy intensive. Prospective utilization of hydrate-based desalination was proposed with a novel filter-based apparatus design to separate hydrate crystals from residual water and enhance the gas–liquid contact with all the unit operations (hydrate formation, and dissociation) occurring inside a single reactor. Experimentations were implemented to obtain the optimum conditions for CO 2 hydrate formation and compared with pure water. CO 2 hydrate formation kinetics revealed induction time, moles consumed, water-to-hydrate conversion, and water recovery were 57 mins, 1.28 mol, 27.72 %, and 50.65 % at optimum conditions of 500 ml, 3.0 MPa, 450 rpm, and 275.15 K for purification of produced water. A desalination efficiency of 62–80 % is achieved with each of the metal ions in the sequence K+>Na+>Mg2+>Ca2+, and anions SO 4 2- > Cl- without any pre and post-treatment. Also, demonstrated that the ionic charge and size have a significant impact on the ion rejection mechanism. The findings disclosed that with an upsurge in the temperature to 277.15 K the desalination efficiency enhanced nevertheless the volume of formed hydrate is less resulting in less driving force. The total dissolved solids and electrical conductivity results indicate that the treated water meets Malaysia's water quality standards for irrigation purposes. This showcases the execution of the hydrate-based desalination process, investigating salt rejection and yield with our custom reactor design. The proposed reactor design may be utilized for the effective separation of hydrate and effectively desalinating waters of higher salinities. Hydrate-based desalination integrated with membrane reactors could lead to a sustainable source of clean water by treating produced water. [ABSTRACT FROM AUTHOR]

• Importance of predicting Water Quality Index (WQI) for assessing water bodies' health and safety. • Development of hybrid model (CNN-CRNN-M5T) combining CNN, Clockwork RNN, and M5 Tree for WQI prediction. • Enhancement of M5T model's data analysis capability for intricate patterns in water quality parameters. • Utilization of GLM-ANOVA to determine significant input variables for WQI prediction. • CNN-CRNN-M5T model's superiority in reducing MAE and increasing efficiency for spatial and temporal WQI predictions in Malaysia. Predicting a water quality index (WQI) is important because it serves as an important metric for assessing the overall health and safety of water bodies. Our paper develops a new hybrid model for predicting the WQI. The study uses a combination of a convolutional neural network (CNN), clockwork recurrent neural network (Clockwork RNN), and M5 Tree (CNN-CRNN-M5T) to predict a WQI. The M5T model lacks advanced operators for extracting meaningful data from water quality parameters, so the new model enhances its ability to analyze intricate patterns. The general linear model analysis of variance (GLM-ANOVA) is an improved version of the ANOVA. Our study uses the GLM-ANOVA to determine significant inputs. As all input variables had p < 0.050, they were defined as significant variables. Results showed that NH-NL and PH had the highest and lowest impact, respectively. Our study used the CNN-CRNN-M5T, CNN-CRNN, CRNN-M5T, CNN-M5T, CRNN, CNN, and M5T models to predict the WQI of a large basin in Malaysia. The CNN-CRNN decreased testing mean absolute error (MAE) of the CRNN, CNN, and M5T models by 2.1 %, 12 %, and 15 %, respectively. The CNN-CRNN-M5T model increased Nash–Sutcliffe efficiency coefficient of the other models by 4–20 % and 2.1–19 %, respectively. The CNN-CRNN-M5T model was a reliable tool for spatial and temporal predictions of WQI. [ABSTRACT FROM AUTHOR]

Sustainable development has many dimensions, such as clean energy, green growth, and sustainable industry. Green finance has a key role in realizing these targets. The core focus of the existing paper is to demystify the influences of green finance, green energy on environmental dynamics in Malaysia covering the timeframe from 1990 to 2018. To this end, we employ the QARDL technique to peruse the effect of green finance, renewable energy, and economic growth on CO 2 emissions and ecological footprint separately. By doing so, we crosscheck the empirical outcomes and inspect the consistency of the analysis. Firstly, empirical results show that the ecological footprint model yields similar results to the CO 2 emissions model in terms of statistically significant quantile ranges and the magnitude of the coefficients. According to the overall results, GDP increases environmental degradation while green finance and renewable energy have a mitigating role. Finally, we confirm a significant EKC hypothesis for Malaysia. Based on these findings, we recommend Malaysian policymakers focus on energy efficiency, realize financial reforms and promote green energy through the financial system to achieve the 2050 net-zero emission target. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper investigates the climate change influence on landslide susceptibility mapping (LSM) using a case study conducted on Penang Island in Malaysia, a region prone to landslides. This study was carried out due to limited research assessing the climate change effect on LSM, considering rainfall and temperature. The study employs climate factors, including temperature and rainfall, alongside 12 causative factors in a Support Vector Machine (SVM) model to develop LSM. The Statistical Downscaling Model (SDSM) is used to derive climate change projections under two Representative Concentration Pathways (RCP) scenarios (RCP4.5 and RCP8.5). Data preparation and normalization are performed using ArcGIS 10.7. Based on the results, future annual rainfall and daily temperatures are expected to rise under both scenarios, with RCP8.5 exhibiting more significant climatic changes. The LSM zonation is impacted more significantly under RCP8.5 due to the severity of climate change. LSM under an observation period achieves the best results (area under the curve (AUC) = 85.75, average statistical index (SI) = 94.48%, kappa = 0.885), followed by LSM under RCP4.5 (AUC = 84.38, average SI = 93.54%, kappa = 0.865) and LSM under RCP8.5 (AUC = 84.13, average SI = 93.34%, kappa = 0.860), demonstrating their reliability and adequate performance. These LSMs can assist local authorities in designating critical areas for monitoring and implementing an early-warning system to respond more effectively to landslide risks caused by climate change. However, the study's limitation is considering only two climate scenarios (RCP4.5 and RCP8.5). Future research should encompass a broader range of climate scenarios to develop the most reliable LSM, given the high uncertainty associated with climate change. • Climate change was projected using the SDSM model. • Climate change has more changes under RCP8.5. • Climate change, directly and indirectly, affect slope stability. • LHM was developed using the SVM model under the climate change effect. • Climate change has a significant impact on landslide distribution zoning in LHM. [ABSTRACT FROM AUTHOR]

In the context of the escalating burden of diabetes in low and middle-income countries (LMICs), there is a pressing concern about the widening disparities in care and outcomes across socioeconomic groups. This paper estimates health poverty measures among individuals with type 2 diabetes mellitus (T2DM) in Malaysia. Using data from the National Diabetes Registry between 2009 and 2018, the study linked 932,855 people with T2DM aged 40–75 to death records. Cox proportional hazards models were used to estimate the 5-year survival probabilities for each patient, stratified by age and sex, while controlling for comorbidities and area-based indicators of socio-economic status (SES), such as district-level asset-based indices and night-time luminosity. Measures of health poverty, based on the Foster-Greer-Thorbecke (FGT) measures, were employed to capture excessive risk of premature mortality. Two poverty line thresholds were used, namely a 5% and 10% reduction in survival probability compared to age and sex-adjusted survival probability of the general population. Counterfactual simulations estimated the extent to which comorbidities contribute to health poverty. 43.5% of the sample experienced health poverty using the 5% threshold, and 8.9% were health poor using the 10% threshold. Comorbidities contribute 2.9% for males and 5.4% for females, at the 5% threshold. At the 10% threshold, they contribute 7.4% for males and 3.4% for females. If all patients lived in areas of highest night-light intensity, poverty would fall by 5.8% for males and 4.6% for females at the 5% threshold, and 4.1% for males and 0.8% for females at the 10% threshold. In Malaysia, there is a high incidence of health poverty among people with diabetes, and it is strongly associated with comorbidities and area-based measures of SES. Expanding the application of health poverty measurement, through a combination of clinical registries and open spatial data, can facilitate simulations for health poverty alleviation. • Novel health poverty measures applied to T2DM in Malaysia using early mortality risk. • Administrative dataset comprising 1M adults combined with death records and SES. • Male gender, older age and area deprivation drive health poverty. • Nighttime luminosity reveals regional patterns in health poverty. • Combining luminosity data and medical datasets enhances risk stratification in LMICs. [ABSTRACT FROM AUTHOR]

As a maritime country, Malaysia relies on its diverse marine ecosystems, contributing to 23% of the country's Gross Domestic Product (GDP), amounting to USD 63 billion. Anthropogenic activities threaten these ecosystems, impacting both the environment and socio-economics. Environmental non-governmental organizations (NGOs) play a crucial role in protecting the marine environment. This paper examines the educational initiatives of the Malaysian Nature Society (MNS), the oldest environmental NGO in Malaysia which promotes marine conservation. Through document analysis of the society's annual management report from June 2016 to May 2021, three educational strategies emerged: dissemination of knowledge, hands-on activities and application of knowledge. A guideline for implementing effective marine educational programs has been developed to enable other environmental NGOs to adopt these strategies and contribute to the conservation of marine ecosystems. • The Malaysian Nature Society (MNS) demonstrates the role of NGOs in the conservation of marine ecosystem through education. employed three educational strategies: dissemination of knowledge, hands-on activities and application of knowledge • MNS employed three educational strategies: dissemination of knowledge, hands-on activities and application of knowledge. • A framework for effective implementation of marine conservation education programs was developed. [ABSTRACT FROM AUTHOR]

Uniaxial compressive strength (UCS) of rocks is one of key rock strength parameters. Generally speaking, UCS can be measured directly through uniaxial compression tests, which is often unfeasible, especially when intact rock samples are highly fragile. Alternatively, the UCS of rocks can be estimated indirectly from other easily available rock indices. Note that adequate measurement data is the prerequisite for the accurate estimation of UCS using indirect methods. This may be difficult to achieve due to the limitation of time and budget, especially for small- to medium-sized projects. In this case, it becomes a challenging issue on how to develop a robust and reliable model for UCS estimation using the sparse measurement data. A fully Bayesian Gaussian process regression (fB-GPR) approach with Synthetic Minority Oversampling Technique (SMOTE) is proposed in this paper to address this problem. A real-life example from Malaysia was used for illustration and validation of proposed method. Results showed that when the synthetic sample size in SMOTE reaches 30 (i.e., optimal synthetic sample size), the coefficient of determination (R 2) increases by about 18.92%, and the accuracy of feature selection reaches 98%, compared with the scenario with only sparse measurement data used for fB-GPR model development. [ABSTRACT FROM AUTHOR]