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Fulltext Extreme precipitation patterns in the Asia-Pacific region and its correlation with El Niño-Southern Oscillation (ENSO)

An D, Eggeling J, Zhang L, He H, Sapkota A, Wang YC, et al.

Sci Rep, 2023 Jul 08;13(1):11068.
PMID: 37422491 DOI: 10.1038/s41598-023-38317-0

In the Asia-Pacific region (APR), extreme precipitation is one of the most critical climate stressors, affecting 60% of the population and adding pressure to governance, economic, environmental, and public health challenges. In this study, we analyzed extreme precipitation spatiotemporal trends in APR using 11 different indices and revealed the dominant factors governing precipitation amount by attributing its variability to precipitation frequency and intensity. We further investigated how these extreme precipitation indices are influenced by El Niño-Southern Oscillation (ENSO) at a seasonal scale. The analysis covered 465 ERA5 (the fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts) study locations over eight countries and regions during 1990-2019. Results revealed a general decrease indicated by the extreme precipitation indices (e.g., the annual total amount of wet-day precipitation, average intensity of wet-day precipitation), particularly in central-eastern China, Bangladesh, eastern India, Peninsular Malaysia and Indonesia. We observed that the seasonal variability of the amount of wet-day precipitation in most locations in China and India are dominated by precipitation intensity in June-August (JJA), and by precipitation frequency in December-February (DJF). Locations in Malaysia and Indonesia are mostly dominated by precipitation intensity in March-May (MAM) and DJF. During ENSO positive phase, significant negative anomalies in seasonal precipitation indices (amount of wet-day precipitation, number of wet days and intensity of wet-day precipitation) were observed in Indonesia, while opposite results were observed for ENSO negative phase. These findings revealing patterns and drivers for extreme precipitation in APR may inform climate change adaptation and disaster risk reduction strategies in the study region.

Matched MeSH terms: Weather
Modern analytical and bioanalytical technologies and concepts for smart and precision farming

Tsong JL, Khor SM

Anal Methods, 2023 Jul 06;15(26):3125-3148.
PMID: 37376849 DOI: 10.1039/d3ay00647f

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.

Matched MeSH terms: Weather
Fulltext Forecasting HFMD Cases Using Weather Variables and Google Search Queries in Sabah, Malaysia

Jayaraj VJ, Hoe VCW

Int J Environ Res Public Health, 2022 Dec 15;19(24).
PMID: 36554768 DOI: 10.3390/ijerph192416880

HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r0-6weeks: 0.47-0.56), with temperature revealing weaker positive correlations (r0-3weeks: 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness.

Matched MeSH terms: Weather*
Fulltext Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network

Arora S, Sawaran Singh NS, Singh D, Rakesh Shrivastava R, Mathur T, Tiwari K, et al.

Comput Intell Neurosci, 2022;2022:9755422.
PMID: 36531923 DOI: 10.1155/2022/9755422

In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).

Matched MeSH terms: Weather
Fulltext A time-series prediction model of acute myocardial infarction in northern of Iran: the risk of climate change and religious mourning

Sharif Nia H, Gorgulu O, Naghavi N, Froelicher ES, Fomani FK, Goudarzian AH, et al.

BMC Cardiovasc Disord, 2021 11 23;21(1):563.
PMID: 34814834 DOI: 10.1186/s12872-021-02372-0

BACKGROUND: Although various studies have been conducted on the effects of seasonal climate changes or emotional variables on the risk of AMI, many of them have limitations to determine the predictable model. The currents study is conducted to assess the effects of meteorological and emotional variables on the incidence and epidemiological occurrence of acute myocardial infarction (AMI) in Sari (capital of Mazandaran, Iran) during 2011-2018.
METHODS: In this study, a time series analysis was used to determine the variation of variables over time. All series were seasonally adjusted and Poisson regression analysis was performed. In the analysis of meteorological data and emotional distress due to religious mourning events, the best results were obtained by autoregressive moving average (ARMA) (5,5) model.
RESULTS: It was determined that average temperature, sunshine, and rain variables had a significant effect on death. A total of 2375 AMI's were enrolled. Average temperate (°C) and sunshine hours a day (h/day) had a statistically significant relationship with the number of AMI's (β = 0.011, P = 0.014). For every extra degree of temperature increase, the risk of AMI rose [OR = 1.011 (95%CI 1.00, 1.02)]. For every extra hour of sunshine, a day a statistically significant increase [OR = 1.02 (95% CI 1.01, 1.04)] in AMI risk occurred (β = 0.025, P = 0.001). Religious mourning events increase the risk of AMI 1.05 times more. The other independent variables have no significant effects on AMI's (P > 0.05).
CONCLUSION: Results demonstrate that sunshine hours and the average temperature had a significant effect on the risk of AMI. Moreover, emotional distress due to religious morning events increases AMI. More specific research on this topic is recommended.

Matched MeSH terms: Weather*
Mapping flood by the object-based method using backscattering coefficient and interference coherence of Sentinel-1 time series

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
Fulltext Detecting dengue outbreaks in Malaysia using geospatial techniques

Nellis S, Loong SK, Abd-Jamil J, Fauzi R, AbuBakar S

Geospat Health, 2021 11 03;16(2).
PMID: 34730321 DOI: 10.4081/gh.2021.1008

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.

Matched MeSH terms: Weather
Fulltext New COVID-19 variant (B.1.1.7): Forecasting the occasion of virus and the related meteorological factors

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*
Large-scale distribution and composition of floating plastic debris in the transition region of the North Pacific

Miyazono K, Yamashita R, Miyamoto H, Ishak NHA, Tadokoro K, Shimizu Y, et al.

Mar Pollut Bull, 2021 Sep;170:112631.
PMID: 34175698 DOI: 10.1016/j.marpolbul.2021.112631

Floating plastic debris was investigated in the transition region in the North Pacific between 141°E and 165°W to understand its transportation process from Asian coast to central subtropical Pacific. Distribution was influenced primarily by the current system and the generation process of the high concentration area differed between the western and eastern areas. West of 180°, debris largely accumulated around nearshore convergent area and was transported by eddies and quasi-stationary jet from south to the subarctic region. The average was 15% higher than that previously reported in 1989, suggesting an increase in plastic debris in 30 years. East of 180°, debris concentrated in the calm water downstream of the Kuroshio Extension Bifurcation with considerably high concentration (505,032 ± 991,989 pieces km-2), due to the accumulation of small transparent film caused by calm weather conditions, suggesting a further investigation on small plastic (<1 mm) in the subsurface depth in the subtropical North Pacific.

Matched MeSH terms: Weather
Fulltext Residual malaria in Jazan region, southwestern Saudi Arabia: the situation, challenges and climatic drivers of autochthonous malaria

Al-Mekhlafi HM, Madkhali AM, Ghailan KY, Abdulhaq AA, Ghzwani AH, Zain KA, et al.

Malar J, 2021 Jul 13;20(1):315.
PMID: 34256757 DOI: 10.1186/s12936-021-03846-4

BACKGROUND: Saudi Arabia and Yemen are the only two countries in the Arabian Peninsula that are yet to achieve malaria elimination. Over the past two decades, the malaria control programme in Saudi Arabia has successfully reduced the annual number of malaria cases, with the lowest incidence rate across the country reported in 2014. This study aims to investigate the distribution of residual malaria in Jazan region and to identify potential climatic drivers of autochthonous malaria cases in the region.
METHODS: A cross-sectional study was carried out from 1 April 2018 to 31 January 2019 in Jazan region, southwestern Saudi Arabia, which targeted febrile individuals attending hospitals and primary healthcare centres. Participants' demographic data were collected, including age, gender, nationality, and residence. Moreover, association of climatic variables with the monthly autochthonous malaria cases reported during the period of 2010-2017 was retrospectively analysed.
RESULTS: A total of 1124 febrile subjects were found to be positive for malaria during the study period. Among them, 94.3 and 5.7% were infected with Plasmodium falciparum and Plasmodium vivax, respectively. In general, subjects aged 18-30 years and those aged over 50 years had the highest (42.7%) and lowest (5.9%) percentages of malaria cases. Similarly, the percentage of malaria-positive cases was higher among males than females (86.2 vs 13.8%), among non-Saudi compared to Saudi subjects (70.6 vs 29.4%), and among patients residing in rural rather than in urban areas (89.8 vs 10.2%). A total of 407 autochthonous malaria cases were reported in Jazan region between 2010 and 2017. Results of zero-inflated negative binomial regression analysis showed that monthly average temperature and relative humidity were the significant climatic determinants of autochthonous malaria in the region.
CONCLUSION: Malaria remains a public health problem in most governorates of Jazan region. The identification and monitoring of malaria transmission hotspots and predictors would enable control efforts to be intensified and focused on specific areas and therefore expedite the elimination of residual malaria from the whole region.

Matched MeSH terms: Weather*
Fulltext Seasonality of adult fragility fractures and association with weather: 12-year experience of a UK Fracture Liaison Service

Ogliari G, Ong T, Marshall L, Sahota O

Bone, 2021 Jun;147:115916.
PMID: 33737194 DOI: 10.1016/j.bone.2021.115916

PURPOSE: To investigate the monthly and seasonal variation in adult osteoporotic fragility fractures and the association with weather.
METHODS: 12-year observational study of a UK Fracture Liaison Service (outpatient secondary care setting). Database analyses of the records of adult outpatients aged 50 years and older with fragility fractures. Weather data were obtained from the UK's national Meteorological Office. In the seasonality analyses, we tested for the association between months and seasons (determinants), respectively, and outpatient attendances, by analysis of variance (ANOVA) and Tukey's test. In the meteorological analyses, the determinants were mean temperature, mean daily maximum and minimum temperature, number of days of rain, total rainfall and number of days of frost, per month, respectively. We explored the association of each meteorological variable with outpatient attendances, by regression models.
RESULTS: The Fracture Liaison Service recorded 25,454 fragility fractures. We found significant monthly and seasonal variation in attendances for fractures of the: radius or ulna; humerus; ankle, foot, tibia or fibula (ANOVA, all p-values <0.05). Fractures of the radius or ulna and humerus peaked in December and winter. Fractures of the ankle, foot, tibia or fibula peaked in July, August and summer. U-shaped associations were showed between each temperature parameter and fractures. Days of frost were directly associated with fractures of the radius or ulna (p-value <0.001) and humerus (p-value 0.002).
CONCLUSION: Different types of fragility fractures present different seasonal patterns. Weather may modulate their seasonality and consequent healthcare utilisation.

Matched MeSH terms: Weather
Fulltext Data Assessment on the relationship between typical weather data and electricity consumption of academic building in Melaka

Ngah Nasaruddin A, Tee BT, Mohd Tahir M, Md Jasman MES

Data Brief, 2021 Apr;35:106797.
PMID: 33614870 DOI: 10.1016/j.dib.2021.106797

Exposure to hot and humid weather conditions will often lead to consuming a vast amount of electricity for cooling. Heating, ventilation, and air conditioning (HVAC) systems are customarily known as the largest consumers of energy in institutions and other facilities which raises the question regarding the impact of the weather conditions to the amount energy consumed. The academic building is a perfect example where a constant fixed daily operating characteristic is measured by the hour, aside from the occasional semester break. Therefore, it can be assumed that the daily HVAC services on an academic facility will operate on a fixed schedule each day, having a similar pattern all year round. This article aims to present an analysis on the relationship between typical weather data by implying the test reference year (TRY) and academic building electricity consumption in an academic building located at Durian Tunggal, Melaka. Typical weather data were generated in representing the weather data between 2010 and 2018 using the Finkelstein-Schafer statistic (F-S statistic) in addition to a data set of electricity consumption. Descriptive analysis and correlation matrix analysis were conducted using JASP software for two sets of sample data; Set A and Set B, with data points of 12 and 108, respectively. The result showed an alternate result with a positive correlation between 1)mean temperature-electricity consumption, and 2)mean rainfall-electricity consumption for data Set A, and a negative correlation between 1)mean temperature-electricity consumption and 2)mean rainfall-electricity consumption for data Set B.

Matched MeSH terms: Weather
Fulltext SELAMAT: A New Secure and Lightweight Multi-Factor Authentication Scheme for Cross-Platform Industrial IoT Systems

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
Effect of tropical outdoor weathering on the surface roughness and mechanical properties of maxillofacial silicones

Rahman AM, Jamayet NB, Nizami MMUI, Johari Y, Husein A, Alam MK

J Prosthet Dent, 2021 Jan 17.
PMID: 33472753 DOI: 10.1016/j.prosdent.2020.07.026

STATEMENT OF PROBLEM: The climate of tropical Southeast Asia includes high humidity and ultraviolet radiation that reduce the lifespan of silicone prostheses by inducing changes in their mechanical properties and color stability. Studies on the surface roughness (SR) and mechanical properties of different silicone elastomers (SEs) subjected to the natural tropical weather of Southeast Asia are lacking.
PURPOSE: The purpose of this in vitro study was to evaluate the SR, tensile strength (TS), and percentage elongation (% E) of different SEs subjected to outdoor weathering in the Malaysian climate.
MATERIAL AND METHODS: Type-II dumbbell-shaped specimens (N-120) (nonweathered=15, weathered=15) were made from 3 room-temperature vulcanized (A-2000, A-2006, and A-103) and 1 heat-temperature vulcanized (M-511) silicone (Factor II). For 6 months, weathered specimens were subjected to outdoor weathering inside a custom exposure rack. Simultaneously, the nonweathered specimens were kept in a dehumidifier. Subsequently, the SR was measured with a profilometer; TS and % E were measured by using a universal testing machine. Two-way ANOVA was used to compare the means of the tested properties of the nonweathered and weathered specimens, and pairwise comparison was carried out between the silicones (α=.05).
RESULTS: After outdoor weathering, the SR, TS, and % E were adversely affected by weathering in the Malaysian environment. Among the silicone materials, A-2000 showed the least TS changes (2.51 MPa), while A-2006 demonstrated significant changes in percentage elongation after outdoor weathering (266.5%). M-511 exhibited the highest mean value (2.50 μm) for SR changes. In addition, A-103 SE showed statistically significant differences in most pairwise comparisons for all 3 dependent variables.
CONCLUSIONS: Based on the evaluation of mechanical properties, A-103 can be suggested as a suitable silicone for maxillofacial prostheses fabricated for tropical climates. However, A-2000 can be a suitable alternative, although significant changes to surface roughness were detected after outdoor weathering.

Matched MeSH terms: Weather
Fulltext Present practices and emerging opportunities in bioengineering for slope stabilization in Malaysia: An overview

Dorairaj D, Osman N

PeerJ, 2021;9:e10477.
PMID: 33520435 DOI: 10.7717/peerj.10477

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.

Matched MeSH terms: Weather
Fulltext Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers

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
Fulltext IoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato

Rezvani SM, Abyaneh HZ, Shamshiri RR, Balasundram SK, Dworak V, Goodarzi M, et al.

Sensors (Basel), 2020 Nov 12;20(22).
PMID: 33198414 DOI: 10.3390/s20226474

Optimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants' comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.

Matched MeSH terms: Weather
Experimental Amelioration of Harsh Weather Speeds Growth and Development in a Tropical Montane Songbird

Mitchell AE, Boersma J, Anthony A, Kitayama K, Martin TE

Am Nat, 2020 10;196(4):E110-E118.
PMID: 32970467 DOI: 10.1086/710151

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.

Matched MeSH terms: Weather*
El Niño driven haze over the Southern Malaysian Peninsula and Borneo

Khan MF, Hamid AH, Rahim HA, Maulud KNA, Latif MT, Nadzir MSM, et al.

Sci Total Environ, 2020 Aug 15;730:139091.
PMID: 32413602 DOI: 10.1016/j.scitotenv.2020.139091

The Southeast Asian (SEA) region is no stranger to forest fires - the region has been suffering from severe air pollution (known locally as 'haze') as a result of these fires, for decades. The fires in SEA region are caused by a combination of natural (the El Niño weather pattern) and manmade (slash-and-burn and land clearing for plantations) factors. These fires cause the emissions of toxic aerosols and pollutants that can affect millions of people in the region. Thus, this study aims to identify the impact of the SEA haze on the Southern region of the Malaysian Peninsula and Borneo region of East Malaysia using the entire air quality observation data at surface level in 2015. Overall, the concentration of PM10 was about two-fold higher during the haze period compared to non-haze period. The concentrations of CO, flux of CO and flux of BC were aligned with PM10 during the entire observation period. The wind field and cluster of trajectory indicated that the Southern Malaysian Peninsula and Borneo were influenced mainly from the wildfires and the combustion of peat soil in the Indonesian Borneo. This study finds that wildfires from Borneo impacted the Southern Malaysian Borneo more seriously than that from Sumatra region.

Matched MeSH terms: Weather
Fulltext Association between the spread of COVID-19 and weather-climatic parameters

Carta MG, Scano A, Lindert J, Bonanno S, Rinaldi L, Fais S, et al.

Eur Rev Med Pharmacol Sci, 2020 08;24(15):8226-8231.
PMID: 32767354 DOI: 10.26355/eurrev_202008_22512

OBJECTIVE: To explore whether the climate has played a role in the COVID-19 outbreak, we compared virus lethality in countries closer to the Equator with others. Lethality in European territories and in territories of some nations with a non-temperate climate was also compared.
MATERIALS AND METHODS: Lethality was calculated as the rate of deaths in a determinate moment from the outbreak of the pandemic out of the total of identified positives for COVID-19 in a given area/nation, based on the COVID-John Hopkins University website. Lethality of countries located within the 5th parallels North/South on 6 April and 6 May 2020, was compared with that of all the other countries. Lethality in the European areas of The Netherlands, France and the United Kingdom was also compared to the territories of the same nations in areas with a non-temperate climate.
RESULTS: A lower lethality rate of COVID-19 was found in Equatorial countries both on April 6 (OR=0.72 CI 95% 0.66-0.80) and on May 6 (OR=0.48, CI 95% 0.47-0.51), with a strengthening over time of the protective effect. A trend of higher risk in European vs. non-temperate areas was found on April 6, but a clear difference was evident one month later: France (OR=0.13, CI 95% 0.10-0.18), The Netherlands (OR=0.5, CI 95% 0.3-0.9) and the UK (OR=0.2, CI 95% 0.01-0.51). This result does not seem to be totally related to the differences in age distribution of different sites.
CONCLUSIONS: The study does not seem to exclude that the lethality of COVID-19 may be climate sensitive. Future studies will have to confirm these clues, due to potential confounding factors, such as pollution, population age, and exposure to malaria.

Matched MeSH terms: Weather*

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