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.
Dengue is a complex disease with an increasing number of infections worldwide. This study aimed to analyse spatiotemporal dengue outbreaks using geospatial techniques and examine the effects of the weather on dengue outbreaks in the Klang Valley area, Kuala Lumpur, Malaysia. Daily weather variables including rainfall, temperature (maximum and minimum) and wind speed were acquired together with the daily reported dengue cases data from 2001 to 2011 and converted into geospatial format to identify whether there was a specific pattern of the dengue outbreaks. The association between these variables and dengue outbreaks was assessed using Spearman's correlation. The result showed that dengue outbreaks consistently occurred in the study area during a 11-year study period. And that the strongest outbreaks frequently occurred in two high-rise apartment buildings located in Kuala Lumpur City centre. The results also show significant negative correlations between maximum temperature and minimum temperature on dengue outbreaks around the study area as well as in the area of the high-rise apartment buildings in Kuala Lumpur City centre.
Unpredictable natural disasters, disease outbreaks, climate change, pollution, and war constantly threaten food crop production. Smart and precision farming encourages using information or data obtained by using advanced technology (sensors, AI, and IoT) to improve decision-making in agriculture and achieve high productivity. For instance, weather prediction, nutrient information, pollutant assessment, and pathogen determination can be made with the help of new analytical and bioanalytical methods, demonstrating the potential for societal impact such as environmental, agricultural, and food science. As a rising technology, biosensors can be a potential tool to promote smart and precision farming in developing and underdeveloped countries. This review emphasizes the role of on-field, in vivo, and wearable biosensors in smart and precision farming, especially those biosensing systems that have proven with suitably complex and analytically challenging samples. The development of various agricultural biosensors in the past five years that fulfill market requirements such as portability, low cost, long-term stability, user-friendliness, rapidity, and on-site monitoring will be reviewed. The challenges and prospects for developing IoT and AI-integrated biosensors to increase crop yield and advance sustainable agriculture will be discussed. Using biosensors in smart and precision farming would ensure food security and revenue for farming communities.
To investigate the prevalence of container breeding mosquitoes with emphasis on the seasonality and larval habitats of Aedes aegypti (Ae. aegypti) in Makkah City, adjoining an environmental monitoring and dengue incidence.
Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.
The Asia Pacific region is regarded as the most disaster-prone area of the world. Since 2000, 1.2 billion people have been exposed to hydrometeorological hazards alone through 1215 disaster events. The impacts of climate change on meteorological phenomena and environmental consequences are well documented. However, the impacts on health are more elusive. Nevertheless, climate change is believed to alter weather patterns on the regional scale, giving rise to extreme weather events. The impacts from extreme weather events are definitely more acute and traumatic in nature, leading to deaths and injuries, as well as debilitating and fatal communicable diseases. Extreme weather events include heat waves, cold waves, floods, droughts, hurricanes, tropical cyclones, heavy rain, and snowfalls. Globally, within the 20-year period from 1993 to 2012, more than 530 000 people died as a direct result of almost 15 000 extreme weather events, with losses of more than US$2.5 trillion in purchasing power parity.
Surveillance of mosquitoes and their distribution in association with rainfall, relative humidity, and temperature were conducted in selected suburban and forested areas, namely, Sungai Penchala (Kuala Lumpur) and Taman Alam (Selangor) for 12 months. Armigeres kesseli was the most abundant species in Sungai Penchala while Aedes butleri was the most dominant species in Taman Alam. A positive correlation between mosquito distribution and rainfall was observed in selected mosquito species in Sungai Penchala (Armigeres kesseli, r = 0.75; Armigeres subalbatus, r = 0.62; and Aedes albopictus, r = 0.65) and Taman Alam (Armigeres sp, r = 0.59; Ae. butleri, r = 0.85; and Ae. albopictus, r = 0.62). However, no significant cor- relation was found either between selected mosquito species in both study areas and relative humidity or temperature. Results obtained suggested that vector control programs to be conducted based on temporal distribution of vectors in order to achieve beneficial outcomes with effective costing.
An epidemio-meteorotropic analytical study of Selangor, in the Southwest coast of Peninsular Malaysia, examines the monthly incidence of dengue for the period 1973-1982 to assess possible quantitative association with the monthly rainfall. The relationships between rainfall, abundance of A. aegypti and dengue infection during 1982 in Jinjang, a dengue-prone area in Selangor, were also examined. A quantitative association between rainfall and the number of dengue cases was found during the first wet period. The lag time between the onset of heavy rain and dengue outbreak was about two to three months. A 120% increase in the number of dengue cases was observed when the monthly rainfall was 300 mm or more. Positive associations were seen between the incidence of dengue and the Aedes house index and the Breteau index in Jinjang. The relationships between these three variables and rainfall suggest that the latter might have exerted its effect on dengue infection partly through the creation of more breeding sites for A. aegypti. Assessment of the importance of A. aegypti in the transmission of dengue in this locality was not possible because of the lack of adjustment for A. albopictus, the other known vector of dengue in the state, and for social and other environmental factors influencing infection rates. In spite of this and the interpretational problems common in aggregate studies, the present analyses have provided relatively strong statistical evidence of an association between rainfall and dengue outbreaks in Selangor, thereby indicating that it is a factor worthy of careful surveillance and monitoring.
AbstractOrganisms living at high elevations generally grow and develop more slowly than those at lower elevations. Slow montane ontogeny is thought to be an evolved adaptation to harsh environments that improves juvenile quality via physiological trade-offs. However, slower montane ontogeny may also reflect proximate influences of harsh weather on parental care and offspring development. We experimentally heated and protected nests from rain to ameliorate harsh montane weather conditions for mountain blackeyes (Chlorocharis emiliae), a montane songbird living at approximately 3,200 m asl in Malaysian Borneo. This experiment was designed to test whether cold and wet montane conditions contribute to parental care and postnatal growth and development rates at high elevations. We found that parents increased provisioning and reduced time spent warming offspring, which grew faster and departed the nest earlier compared with offspring from unmanipulated nests. Earlier departure reduces time-dependent predation risk, benefitting parents and offspring. These plastic responses highlight the importance of proximate weather contributions to broad patterns of montane ontogeny and parental care.
Within recorded history, most Southeast Asian peoples have been of "southern Mongoloid" physical type, whether they speak Austroasiatic, Tibeto-Burman, Austronesian, Tai-Kadai, or Hmong-Mien languages. However, population distributions suggest that this is a post-Pleistocene phenomenon and that for tens of millennia before the last glaciation ended Greater Mainland Southeast Asia, which included the currently insular world that rests on the Sunda Shelf, was peopled by short, dark-skinned, frizzy-haired foragers whose descendants in the Philippines came to be labeled by the sixteenth-century Spanish colonizers as "negritos," a term that has since been extended to similar groups throughout the region. There are three areas in which these populations survived into the present so as to become part of written history: the Philippines, the Malay Peninsula, and the Andaman Islands. All Philippine negritos speak Austronesian languages, and all Malayan negritos speak languages in the nuclear Mon-Khmer branch of Austroasiatic, but the linguistic situation in the Andamans is a world apart. Given prehistoric language shifts among both Philippine and Malayan negritos, the prospects of determining whether disparate negrito populations were once a linguistically or culturally unified community would appear hopeless. Surprisingly, however, some clues to a common negrito past do survive in a most unexpected way.
The number of dengue cases has been increasing on a global level in recent years, and particularly so in Malaysia, yet little is known about the effects of weather for identifying the short-term risk of dengue for the population. The aim of this paper is to estimate the weather effects on dengue disease accounting for non-linear temporal effects in Selangor, Kuala Lumpur and Putrajaya, Malaysia, from 2008 to 2010. We selected the weather parameters with a Poisson generalized additive model, and then assessed the effects of minimum temperature, bi-weekly accumulated rainfall and wind speed on dengue cases using a distributed non-linear lag model while adjusting for trend, day-of-week and week of the year. We found that the relative risk of dengue cases is positively associated with increased minimum temperature at a cumulative percentage change of 11.92% (95% CI: 4.41-32.19), from 25.4 °C to 26.5 °C, with the highest effect delayed by 51 days. Increasing bi-weekly accumulated rainfall had a positively strong effect on dengue cases at a cumulative percentage change of 21.45% (95% CI: 8.96, 51.37), from 215 mm to 302 mm, with the highest effect delayed by 26-28 days. The wind speed is negatively associated with dengue cases. The estimated lagged effects can be adapted in the dengue early warning system to assist in vector control and prevention plan.
Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.
In this day and age, with the ever-growing population and energy demand, we should take the renewable option route in our energy source. We should also keep in mind that said energy should not cause any lasting environmental damage, one of the perfect example being solar energy. A country that is hot and sunny all year long is the perfect contributor to solar energy, case in point, Malaysia. With that in mind Solar Tree is designed and developed to facilitate consumers who need electric power at any place, anytime, anywhere. The objective of this study is to assess a mini project in the likes of Solar Tree that can generate electricity without harming the environment, despite the weather. Intended specifically to be a mini project, it is understandable that electricity generated is limited, with only up to 500W in total. As a trial, two electronic devices were tested, specifically a mobile phone and a laptop, as both devices are used almost every day. The data collected is then tabulated and analysed. It was concluded the solar tree developed proved efficient in charging both devices and will continue to do so given enough sunlight.
Photovoltaic backsheets have considerable impact on the collective performance of solar cells. Material components should withstand certain temperatures and loads while maintaining high thermal stability under various weather conditions. Solar modules must demonstrate increased reliability, adequate performance, safety, and durability throughout the course of their lifetime. This work presents a novel solar module. The module consists of an innovative polyvinylidene fluoride-short sugar palm fiber (PVDF-SSPF) composite backsheet within its structure. It was electrically and thermally evaluated. The current-voltage characteristics (I-V) were obtained using the solar module analyzer, PROVA 210PV. A thermal evaluation was accomplished using a temperature device, SDL200. The thermal test consisted of two different assessments. The first targeted the surface and backsheet of the developed module to correlate their performance from within. The second assessment compared the thermal performance of the fabricated backsheet with the conventional one. Both tests were combined into a heatmap analysis to further understand the thermal performance. Results revealed that the developed module exhibited reasonable electrical efficiency, achieving appropriate and balanced I-V curves. PVDF-SSPF backsheets proved to be thermally stable by displaying less heat absorbance and better temperature shifts. Additional research efforts are highly encouraged to investigate other characteristics. To enhance performance, further analyses are needed such as the damp heat analysis, accelerated aging analysis, and heat dissipation phenomena.
Population increase and the demand for infrastructure development such as construction of highways and road widening are intangible, leading up to mass land clearing. As flat terrains become scarce, infrastructure expansions have moved on to hilly terrains, cutting through slopes and forests. Unvegetated or bare slopes are prone to erosion due to the lack of or insufficient surface cover. The combination of exposed slope, uncontrolled slope management practices, poor slope planning and high rainfall as in Malaysia could steer towards slope failures which then results in landslides under acute situation. Moreover, due to the tropical weather, the soils undergo intense chemical weathering and leaching that elevates soil erosion and surface runoff. Mitigation measures are vital to address slope failures as they lead to economic loss and loss of lives. Since there is minimal or limited information and investigations on slope stabilization methods in Malaysia, this review deciphers into the current slope management practices such as geotextiles, brush layering, live poles, rock buttress and concrete structures. However, these methods have their drawbacks. Thus, as a way forward, we highlight the potential application of soil bioengineering methods especially on the use of whole plants. Here, we discuss the general attributions of a plant in slope stabilization including its mechanical, hydrological and hydraulic effects. Subsequently, we focus on species selection, and engineering properties of vegetation especially rooting structures and architecture. Finally, the review will dissect and assess the ecological principles for vegetation establishment with an emphasis on adopting the mix-culture approach as a slope failure mitigation measure. Nevertheless, the use of soil bioengineering is limited to low to moderate risk slopes only, while in high-risk slopes, the use of traditional engineering measure is deemed more appropriate and remain to be the solution for slope stabilization.
Iran is situated in a very diverse environmental area. The climate of the region is varied and influencedby different patterns. In order to best describe the expected climate change impacts for the region,climate change scenarios and climate variables must be developed on a regional, or even site-specific,scale. The weather generator is one of the valid downscaling methods. In the current study, LARSWG(a weather generator) and the outputs from ECHO-G for present climate, as well as future timeslice of 2010-2039 based on A1 scenario, were used to evaluate LARS-WG as a tool at 13 synopticstations located in the north and northeast parts of Iran. The results obtained in this study illustratethat LARS-WG has a reasonable capability of simulating the minimum and maximum temperaturesand precipitation. In addition, the results showed that the mean precipitation decreased in Semnan, thesouth of Khorasan and Golestan. Meanwhile, the mean temperature during 2010-2039 would increaseby 0.5°C, especially in the cold season.
Mite foci were fenced above and below ground to prevent the entry of host animals and to prevent the migration of mites within the soil. Weekly counts were made over a period of thirty weeks with larvae being collected at the beginning and end of the study, but not during the intervening period of hot, dry weather. Post-larval forms can survive for long periods and mite foci can remain productive without being visited by the host animals. Mite foci may be missed by normal survey methods during hot, dry weather.
The Climatic performance of courtyard residential buildings needs to be
investigated if the assertion that courtyard is a microclimate modifier is to be
accepted. Therefore, this study seeks to examine the microclimatic performance
of two existing courtyard residential buildings with similar characteristics in
Kafanchan-Kaduna Nigeria, -the fully enclosed courtyard residential building and
the semi-enclosed courtyard residential building. The purpose of this research is
to investigate their microclimatic performances in other to establish the best
courtyard house. This study uses measurement to achieve its aim. The tool
employed for data collection is the Hobo Weather Data Loggers (HWDL). Three
HWDL were used to collect data in the two case-study, and the third one was
placed in the outside area as a benchmark. Only air temperature and relative
humidity were measured. This study revealed a tangible difference in the
microclimatic performance of the two case-study. The fully enclosed courtyard
residential building is seen to have air temperature difference of 1 oC to 3 oC, and
the relative humidity difference of 4 % to 8 %. In conclusion, the fully enclosed
courtyard house demonstrated a more favorable microclimatic performance than
the semi-enclosed, and further simulation studies towards its optimization are
required.
Lightning is a natural phenomenon that generates a high electric field during thunderstorm. It has been
reported that lightning strikes amid storms can occur around 100 times per second. The atmospheric
electric field is an imperative parameter during a thunderstorm. Therefore, monitoring the electric field
and its parameters is the best way for local lightning forecast. The electric field monitoring data can
validate the accuracy of weather prediction in a local area from meteorology department or by using
equipment specially designed to measure this electric field that exists when the phenomenon of lightning occurs. In this paper, the relationship between lightning, air humidity and temperature is discussed to understand the post lightning effect on these electric parameters. Additionally, the characteristics of the parameters are observed and analysed.