Displaying publications 1 - 20 of 116 in total

  1. Wilson T
    Malayan Medical Journal, 1935;10:39-48.
    Matched MeSH terms: Weather
  2. Rendana M, Idris WMR
    J Infect Public Health, 2021 Oct;14(10):1320-1327.
    PMID: 34175236 DOI: 10.1016/j.jiph.2021.05.019
    BACKGROUND: World Health Organization has reported fifty countries have now detected the new coronavirus (B.1.1.7 variant) since a couple of months ago. In Indonesia, the B.1.1.7 cases have been found in several provinces since January 2021, although they are still in a lower number than the old variant of COVID-19. Therefore, this study aims to create a forecast analysis regarding the occasions of COVID-19 and B.1.1.7 cases based on data from the 1st January to 18th March 2021, and also analyze the association between meteorological factors with B.1.1.7 incidences in three different provinces of Indonesia such as the West Java, South Sumatra and East Kalimantan.

    METHODS: We used the Autoregressive Moving Average Models (ARIMA) to forecast the number of cases in the upcoming 14 days and the Spearman correlation analysis to analyze the relationship between B.1.1.7 cases and meteorological variables such as temperature, humidity, rainfall, sunshine, and wind speed.

    RESULTS: The results of the study showed the fitted ARIMA models forecasted there was an increase in the daily cases in three provinces. The total cases in three provinces would increase by 36% (West Java), 13.5% (South Sumatra), and 30% (East Kalimantan) as compared with actual cases until the end of 14 days later. The temperature, rainfall and sunshine factors were the main contributors for B.1.1.7 cases with each correlation coefficients; r = -0.230; p < 0.05, r = 0.211; p < 0.05 and r = -0.418; p < 0.01, respectively.

    CONCLUSIONS: We recapitulated that this investigation was the first preliminary study to analyze a short-term forecast regarding COVID-19 and B.1.1.7 cases as well as to determine the associated meteorological factors that become primary contributors to the virus spread.

    Matched MeSH terms: Weather*
  3. Ueng SK, Chan Yao-Hong, Lu WH, Chang HW
    Sains Malaysiana, 2015;44:1701-1706.
    Wind turbines are massive electrical structures. They produce large returns when illuminated by radar waves. These
    scatterings have a great impact on the operation of surveillance, air traffic control and weather radars. This paper presents
    two geometric modelling methods for reshaping wind turbine towers so that the Radar Cross Section (RCS) of wind turbines
    is reduced. In the proposed reshaping methods, bump structures are created on the surface of the conventional cylinder
    wind turbine tower. When a reshaped tower is illuminated by radar waves, the bump structures scatter incident radar
    waves into insignificant directions so that the strength of back-scattering is declined and the RCS of the wind turbine is
    decreased. The test results confirmed that the proposed methodssignificantly reduce bi-static RCS values of wind turbines.
    The proposed reshaping methods are practical, flexible and effective in alleviating the scatterings of wind turbines.
    Matched MeSH terms: Weather
  4. Gentry JW, Phang OW, Manikumaran C
    PMID: 918713
    Studies of larval mite populations along transects, as measured with black plates, were conducted in forest and grassland habitats for a period of 67 weeks. Larvae of both Leptotrombidium (Leptotrombidium) deliense and L. (L.) fletcheri were influenced greatly by rainfall, with the larvae being abundant and easily collected during periods of heavy rainfall and difficult or impossible to collect during dry periods. Simulated rainfall maintained larval populations for longer periods during dry weather.
    Matched MeSH terms: Weather*
  5. Segun OE, Shohaimi S, Nallapan M, Lamidi-Sarumoh AA, Salari N
    PMID: 32429373 DOI: 10.3390/ijerph17103474
    Background: despite the increase in malaria control and elimination efforts, weather patterns and ecological factors continue to serve as important drivers of malaria transmission dynamics. This study examined the statistical relationship between weather variables and malaria incidence in Abuja, Nigeria. Methodology/Principal Findings: monthly data on malaria incidence and weather variables were collected in Abuja from the year 2000 to 2013. The analysis of count outcomes was based on generalized linear models, while Pearson correlation analysis was undertaken at the bivariate level. The results showed more malaria incidence in the months with the highest rainfall recorded (June-August). Based on the negative binomial model, every unit increase in humidity corresponds to about 1.010 (95% confidence interval (CI), 1.005-1.015) times increase in malaria cases while the odds of having malaria decreases by 5.8% for every extra unit increase in temperature: 0.942 (95% CI, 0.928-0.956). At lag 1 month, there was a significant positive effect of rainfall on malaria incidence while at lag 4, temperature and humidity had significant influences. Conclusions: malaria remains a widespread infectious disease among the local subjects in the study area. Relative humidity was identified as one of the factors that influence a malaria epidemic at lag 0 while the biggest significant influence of temperature was observed at lag 4. Therefore, emphasis should be given to vector control activities and to create public health awareness on the proper usage of intervention measures such as indoor residual sprays to reduce the epidemic especially during peak periods with suitable weather conditions.
    Matched MeSH terms: Weather*
  6. Jin BL, Jaal Z
    Trop Biomed, 2009 Aug;26(2):140-8.
    PMID: 19901900 MyJurnal
    Changes in the abundance of the house fly, Musca domestica, was studied for a period of one year in two poultry farms in Penang, Malaysia: one in Balik Pulau, located in Penang island, and the other in Juru, located on mainland Penang. The sampling of house flies were carried out from March 2007 to April 2008 using the Scudder grill, and the correlation with meteorological conditions particularly rainfall, relative humidity and temperature were observed. In Balik Pulau, the fly abundance showed an inverse relationship to relative humidity and total rainfall. However, no significant correlations were found between the abundance of flies and the above mentioned climatic factors. In contrast, the occurrence of flies in Juru showed strong correlation indices with relative humidity (r=0.803, p<0.05) and total rainfall (r=0.731, p<0.05). Temperature had no significant effect on the abundance of flies in both poultry farms due to imperceptible changes in monthly temperature.
    Matched MeSH terms: Weather*
  7. Humada AM, Hojabri M, Sulaiman MH, Hamada HM, Ahmed MN
    PLoS One, 2016;11(4):e0152766.
    PMID: 27035575 DOI: 10.1371/journal.pone.0152766
    A grid-connected photovoltaic (PV) system operates under fluctuated weather condition has been modeled and characterized based on specific test bed. A mathematical model of a small-scale PV system has been developed mainly for residential usage, and the potential results have been simulated. The proposed PV model based on three PV parameters, which are the photocurrent, IL, the reverse diode saturation current, Io, the ideality factor of diode, n. Accuracy of the proposed model and its parameters evaluated based on different benchmarks. The results showed that the proposed model fitting the experimental results with high accuracy compare to the other models, as well as the I-V characteristic curve. The results of this study can be considered valuable in terms of the installation of a grid-connected PV system in fluctuated climatic conditions.
    Matched MeSH terms: Weather
  8. Huat, Bujang B.K, Faisal AIi, Choong, Foong Heng
    Residual soils occur in most countries of the world but the greater areas and depths are normally found in tropical humid areas. In these places, the soil forming processes are still very active and the weathering is much faster than the erosive factor. Most residual exhibit high soil suctions for most of the year. The absence of positive pore water pressure except immediately after rain, renders conventional soil mechanics for saturated soil irrelevant. In particular, the effective stress theories of saturated soil are not applicable at the practical leve l. Ignorance or lack of understanding of the geotechnical behavior of soil in the partially or unsaturated state has caused a lot of damages to infrastructures, buildings and other structures. For instances, the collapsibility and volume change of partially saturated soils in connection with the drying or wetting causes a lot of damage to foundation, roads and other structures. As such, the development of extended soil mechanics, which embraces the soil in the unsaturated state or subjected to soil suction, is essential. This paper examines the collapsibility and volume change behavior specifically of an unsaturated residual soil under various levels of applied matric suction (u -u ), and net mean stress (a-u) in a predetermined stress path. The volume change of ;he"' soil is found to be sensitive to both the applied matric suction and net mean stress. The soil is found to exhibit a collapsibility behavior upon a reduction in applied matric suction to 25 kPa at constant net mean stress.
    Matched MeSH terms: Weather
  9. Pereira, J.J., Hunt, J.C.R., Chan, J.C.L.
    ASM Science Journal, 2014;8(1):1-10.
    The role of science and technology (S&T) in preventing disasters and building resilience to climate change is featured in this paper, drawing primarily on the presentations and discussion of researchers, practitioners and policy makers from 31 institutions in 17 countries during the Workshop on Natural Disasters and Climate Change in Asia, held on 5–7 November 2012 in Bangi, Malaysia. Issues highlighted include advances in climate modelling and weather forecasts, with emphasis on information gaps; hazards and its cascading effects, focusing on current research and approaches; and the potential for land-based mitigation-adaptation strategies. Progress in mobilizing S&T to support disaster prevention and climate resilience is hindered by factors such as absence or lack of research, incomplete and non-existent scientific records, restricted access to data and capacity to innovate and transmit S&T, among others. The establishment of an Asian Network for Climate Science and Technology is proposed to provide and facilitate exchange of information and aid development of research co-ordination projects led by Asian researchers and possibly to act as a one-stop repository of global climate change related research too. The scope of the network would cover climate research with particular relevance to disaster resilience, including scientific capacity, which is all very distinct in Asia.
    Matched MeSH terms: Weather
  10. Markus Bulus, Lim, Yaik-Wah, Malsiah Hamid
    Scholars have opined that the courtyard is a passive architectural design element and
    that it can act as a microclimate modifier provided that its design requirements are not
    ignored. But despite the assertions, empirical studies on the microclimatic
    performance of a fully enclosed courtyard house and the non-courtyard house seems
    to be deficient, and the assumption that the Courtyard is a passive architectural design
    element needs to be substantiated. Therefore, the purpose of this study is to
    investigate the microclimatic performance of a fully enclosed courtyard and noncourtyard
    residential buildings. The main objective is to compare their microclimatic
    performances in other to draw a conclusion on the best option. Three Hobo Weather
    Data Loggers were used to collect climatic data in the buildings, and the third one was
    situated in the outdoor area as a benchmark. The climatic variables investigated are;
    air temperature and relative humidity. The fully enclosed courtyard residential building
    is seen to have a better air temperature difference of 2 oC to 4 oC and the relative
    humidity of 2 % to 6 %. In conclusion, the fully enclosed courtyard residential building
    has confirmed a more favorable microclimatic performance, and future studies
    towards its optimization are recommended.
    Matched MeSH terms: Weather
  11. Muniasamy, Arun Kumar, Tinia Idaty Binti Mohd Ghazi
    The effects of propylene stored in pressurized spherical vessel were investigated using radiation & explosion modeling using PHAST 6.7 software in one of the refinery in Malaysia. The simulations were performed for various weather conditions with different leak scenarios in deterministic approach. Modeling approach was standard with current industry practice. Resulting events such as jet fire, vapor cloud explosion, boiling liquid evaporating vapor explosion effects shown in thermal radiation and overpressure towards targeted technical buildings. The effects of resulting jet fire flame length increase with release rate and explosion overpressure effects increase with degree of confinement and volume fraction respectively. The results were reviewed, interpreted against industry standard. The sensitivity cases show that, using lower inventory with moderate operating conditions will keep the consequence in acceptable region. This consequence analysis will form a basis for layout development, safety distance and fire zone segregation during conceptual design stage. Propylene storage conditions, layout arrangements and blast protections were recommended as part of preventive and mitigative measures.
    Matched MeSH terms: Weather
  12. Mousa MA
    J Contemp Dent Pract, 2020 Jun 01;21(6):678-682.
    PMID: 33025938
    AIMS: The purpose of this study was to assess the influence of hot and dry weather on the hardness and surface roughness of four different maxillofacial silicone elastomeric materials (MFSEM) including two room-temperature vulcanized (RTV) and two high-temperature vulcanized (HTV) materials.

    MATERIALS AND METHODS: Eighty test specimens were fabricated according to the manufacturer's instructions into rectangular test specimens. The hardness and surface roughness were tested, after 6 months of exposure to natural hot and dry weather. The hardness was measured through the International Rubber Hardness Degree (IRHD) scale using an automated hardness tester. The surface roughness was measured using a novel 3D optical noncontact technique using a combination of a light sectioning microscope and a computer vision system. Statistical Package for Social Sciences software SPSS/version 24 was used for analysis and a comparison between two independent variables was done using an independent t test, while more than two variables were analyzed, F test (ANOVA) to be used followed by a post hoc test to determine the level of significance between every two groups.

    RESULTS: The hot and dry weather statistically influenced the hardness and surface roughness of MFSEM. Cosmesil M-511 showed the least hardness in test groups while A-2000 showed the hardest material (p < 0.05). A-2000 showed significant changes from rough in case of nonweathered to become smoother in weather followed by A-2186 (p < 0.05). Cosmesil M-511 showed the roughest material.

    CONCLUSION: Cosmesil M-511 showed the least hard MFSEM after outdoor weathering while A-2000, the highest and least material showed hardness and surface roughness, respectively.

    CLINICAL IMPLICATION: A-2000 had a high IRHD scale hardness. This makes this material more suitable for the replacement of ear and nose defects. Cosmesil M-511 is soft and easily adaptable material that makes the material more appropriate for the replacement of small facial defect with undercut area to be easily inserted and removed. Whilst A-2000 is smoother and finer in test specimens after weathering, Cosmesil M-511 became rougher after weathering.

    Matched MeSH terms: Weather
  13. Khalid H, Hashim SJ, Ahmad SMS, Hashim F, Chaudhary MA
    Sensors (Basel), 2021 Feb 18;21(4).
    PMID: 33670675 DOI: 10.3390/s21041428
    The development of the industrial Internet of Things (IIoT) promotes the integration of the cross-platform systems in fog computing, which enable users to obtain access to multiple application located in different geographical locations. Fog users at the network's edge communicate with many fog servers in different fogs and newly joined servers that they had never contacted before. This communication complexity brings enormous security challenges and potential vulnerability to malicious threats. The attacker may replace the edge device with a fake one and authenticate it as a legitimate device. Therefore, to prevent unauthorized users from accessing fog servers, we propose a new secure and lightweight multi-factor authentication scheme for cross-platform IoT systems (SELAMAT). The proposed scheme extends the Kerberos workflow and utilizes the AES-ECC algorithm for efficient encryption keys management and secure communication between the edge nodes and fog node servers to establish secure mutual authentication. The scheme was tested for its security analysis using the formal security verification under the widely accepted AVISPA tool. We proved our scheme using Burrows Abdi Needham's logic (BAN logic) to prove secure mutual authentication. The results show that the SELAMAT scheme provides better security, functionality, communication, and computation cost than the existing schemes.
    Matched MeSH terms: Weather
  14. K. Ramesh, P. Ramalakshmi, R. Anitha
    Sains Malaysiana, 2015;44:1389-1396.
    The determination of variance of surface air temperature is very essential since it has a direct impact on vegetation, environment and human livelihood. Forecast of surface air temperature is difficult because of the complex physical phenomenon and the random-like behavior of atmospheric system which influences the temperature event on the earth surface. In this study, forecast models based on artificial neural network (ANN) and genetic programming (GP) approaches were proposed to predict lead seven days minimum and maximum surface air temperature using the weather parameters observed at the station Chennai, India. The outcome of this study stated that models formulated using ANN approach are more accurate than genetic programming for all seven days with the highest coefficient of determination (R2), least mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) on deployment with independent test dataset. ANN models give statistically acceptable mean absolute error of 0.59oC for lead day one in minimum temperature forecast and 0.86oC variance for lead day one in maximum temperature forecast. The study also clarified that the level of accuracy of the proposed prediction models were found to be better for smaller lead days when compared with higher lead days with both approaches.
    Matched MeSH terms: Weather
  15. Pazikadin AR, Rifai D, Ali K, Malik MZ, Abdalla AN, Faraj MA
    Sci Total Environ, 2020 May 01;715:136848.
    PMID: 32040994 DOI: 10.1016/j.scitotenv.2020.136848
    The increased demand for solar renewable energy sources has created recent interest in the economic and technical issues related to the integration of Photovoltaic (PV) into the grid. Solar photovoltaic power generation forecasting is a crucial aspect of ensuring optimum grid control and power solar plant design. Accurate forecasting provides significant information to grid operators and power system designers in generating an optimal solar photovoltaic plant and to manage the power of demand and supply. This paper presents an extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting. The instrument used to measure the solar irradiance is analysed and discussed, specifically on studies that were published from February 1st, 2014 to February 1st, 2019. The selected papers were obtained from five major databases, namely, Direct Science, IEEE Xplore, Google Scholar, MDPI, and Scopus. The results of the review demonstrate the increased application of ANN on solar power generation forecasting. The hybrid system of ANN produces accurate results compared to individual models. The review also revealed that improvement forecasting accuracy can be achieved through proper handling and calibration of the solar irradiance instrument. This finding indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter.
    Matched MeSH terms: Weather
  16. Kamaruddin FA, Anggraini V, Kim Huat B, Nahazanan H
    Materials (Basel), 2020 Jun 17;13(12).
    PMID: 32560432 DOI: 10.3390/ma13122753
    The durability of natural and treated clay soil stabilized with lime and alkaline activation (AA) affected by environmental factors (hot and humid) was determined in this study. Investigation and evaluation on the strength of the soil, moisture content, and volume change of the specimen were determined at each curing period (7, 28, and 90 days) based on the weather conditions. An unconfined compressive strength (UCS) of the specimen at three different wetting/drying cycles (one, three, and five cycles) was determined. The findings show that the strength of the treated specimens fluctuated with increment and decrement strength (one and three cycles) in the range of 1.41 to 1.88 MPa (lime) and 2.64 to 8.29 MPa (AA), while for five cycles with a curing period of 90 days the decrement was in the range of 1.62 to 1.25 MPa and 6.06 to 5.89 MPa for lime and AA, respectively. The decrement percentage for treated samples that were subjected to five cycles of wetting and drying in 90 days was found to be 20.38% (lime) and 38.64% (AA), respectively. Therefore, it can be summarized that wetting/drying cycles have a significant influence on the durability, strength, and the volume changes of the specimens.
    Matched MeSH terms: Weather
  17. Ismail A, Rahman F
    Trop Life Sci Res, 2013 Aug;24(1):1-7.
    PMID: 24575237 MyJurnal
    Environmental factors can play important roles in influencing waterbird communities. In particular, weather may have various biological and ecological impacts on the breeding activities of waterbirds, though most studies have investigated the effect of weather on the late stages of waterbird breeding (e.g., hatching rate, chick mortality). Conversely, the present study attempts to highlight the influence of weather on the early nesting activities of waterbirds by evaluating a recently established mixed-species colony in Putrajaya Wetlands, Malaysia. The results show that only rainfall and temperature have a significant influence on the species' nesting activities. Rainfall activity is significantly correlated with the Grey Heron's rate of establishment (rainfall: rs = 0.558, p = 0.03, n = 72) whereas both temperature and rainfall are associated with Painted Stork's nesting density (temperature: rs = 0.573, p = 0.013; rainfall: rs = -0.662, p = 0.03, n = 48). There is a possibility that variations in the rainfall and temperature provide a cue for the birds to initiate their nesting. Regardless, this paper addresses concerns on the limitations faced in the study and suggests long-term studies for confirmation.
    Matched MeSH terms: Weather
  18. Zhang X, Chan NW, Pan B, Ge X, Yang H
    Sci Total Environ, 2021 Nov 10;794:148388.
    PMID: 34217078 DOI: 10.1016/j.scitotenv.2021.148388
    The SAR has the ability of all-weather and all-time data acquisition, it can penetrate the cloud and is not affected by extreme weather conditions, and the acquired images have better contrast and rich texture information. This paper aims to investigate the use of an object-oriented classification approach for flood information monitoring in floodplains using backscattering coefficients and interferometric coherence of Sentinel-1 data under time series. Firstly, the backscattering characteristics and interference coherence variation characteristics of SAR time series are used to analyze whether the flood disaster information can be accurately reflected and provide the basis for selecting input classification characteristics of subsequent SAR images. Subsequently, the contribution rate index of the RF model is used to calculate the importance of each index in time series to convert the selected large number of classification features into low dimensional feature space to improve the classification accuracy and reduce the data redundancy. Finally, the SAR image features in each period after multi-scale segmentation and feature selection are jointly used as the input features of RF classification to extract and segment the water in the study area to monitor floods' spatial distribution and dynamic characteristics. The results showed that the various attributes of backscatter coefficients and interferometric coherence under time series could accurately correspond with the actual flood risk, and the combined use of backscattering coefficient and interferometric coherence for flood extraction can significantly improve the accuracy of flood information extraction. Overall, the object-based random forest method using the backscattering coefficient and interference coherence of Sentinel-1 time series for flood extraction advances our understanding of flooding's temporal and spatial dynamics, essential for the timely adoption of adaptation and mitigation strategies for loss reduction.
    Matched MeSH terms: Weather
  19. Haque R, Ho SB, Chai I, Abdullah A
    F1000Res, 2021;10:911.
    PMID: 34745565 DOI: 10.12688/f1000research.73026.1
    Background - Recently, there have been attempts to develop mHealth applications for asthma self-management. However, there is a lack of applications that can offer accurate predictions of asthma exacerbation using the weather triggers and demographic characteristics to give tailored response to users. This paper proposes an optimised Deep Neural Network Regression (DNNR) model to predict asthma exacerbation based on personalised weather triggers. Methods - With the aim of integrating weather, demography, and asthma tracking, an mHealth application was developed where users conduct the Asthma Control Test (ACT) to identify the chances of their asthma exacerbation. The asthma dataset consists of panel data from 10 users that includes 1010 ACT scores as the target output. Moreover, the dataset contains 10 input features which include five weather features (temperature, humidity, air-pressure, UV-index, wind-speed) and five demography features (age, gender, outdoor-job, outdoor-activities, location). Results - Using the DNNR model on the asthma dataset, a score of 0.83 was achieved with Mean Absolute Error (MAE)=1.44 and Mean Squared Error (MSE)=3.62. It was recognised that, for effective asthma self-management, the prediction errors must be in the acceptable loss range (error<0.5). Therefore, an optimisation process was proposed to reduce the error rates and increase the accuracy by applying standardisation and fragmented-grid-search. Consequently, the optimised-DNNR model (with 2 hidden-layers and 50 hidden-nodes) using the Adam optimiser achieved a 94% accuracy with MAE=0.20 and MSE=0.09. Conclusions - This study is the first of its kind that recognises the potentials of DNNR to identify the correlation patterns among asthma, weather, and demographic variables. The optimised-DNNR model provides predictions with a significantly higher accuracy rate than the existing predictive models and using less computing time. Thus, the optimisation process is useful to build an enhanced model that can be integrated into the asthma self-management for mHealth application.
    Matched MeSH terms: Weather
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