Maximum Relative Humidity Research Articles

Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia

Accurate prediction of short-term water demand, especially, in the case of extreme weather conditions such as flood, droughts and storms, is crucial information for the policy makers to manage the availability of freshwater. This study develops a hybrid model for the prediction of monthly water demand using the database of monthly urban water consumption in Melbourne, Australia. The dataset consisted of minimum, maximum, and mean temperature (°C), evaporation (mm), rainfall (mm), solar radiation (MJ/m2), maximum relative humidity (%), vapor pressure (hpa), and potential evapotranspiration (mm). The dataset was normalized using natural logarithm and denoized then by employing the discrete wavelet transform. Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. Models were trained several times with different configuration (nodes in hidden layers) to achieve better accuracy. The final optimum learning algorithm was selected based on the performance values (regression; mean absolute, relative and maximum error) and Taylor diagram.

Analyses of Vineyard Microclimate in the Eastern Foothills of the Helan Mountains in Ningxia Region, China

Vineyard microclimate is a main determining factor for grape yield and quality. In the Eastern Foothills of the Helan Mountains in the Ningxia region, China, grape is a critical economic crop for wine making. The microclimate of vineyards in 2019 and 2020 was analyzed on the basis of observations from eight vineyards microclimate stations and a national reference station. The vineyard microclimate was compared with the larger-scale local weather conditions, and the vineyard microclimate was compared within and among different climatic zones. In addition, the climate was evaluated at the four study wine grape areas. The results showed that: (1) In the same wine-grape-growing area where the climate was similar, vineyard microclimate was affected by elevation, topography, and soil texture. (2) Excepting average wind velocity, there were no statistically significant (p < 0.05) differences in minimum and maximum air temperatures or relative humidity between the study field microclimate observation stations and the local national reference station. (3) Among the four climate-zone-based wine-grape-growing areas, vineyard microclimate was related to topography, geographical location, and soil type. In particular, the Yinchuan and Qingtongxia areas had an abundant heat resource, and the Hongsipu area had sufficient precipitation and high photosynthetically active radiation (PAR). By contrast, the Shizuishan area had a relatively low-to-moderate amount of heat, precipitation, and radiation resources for grape. (4) According to the climate assessment of agricultural products—wine grape (QX/T 557—2020), the climate was superior in 2019 than in 2020 for wine grapes. In particular, the climate for grape was better in the Yinchuan area than in other study areas. The results of this study are evidence-based and could be used to assist the local wine grape community in making decisions about cultivars, management practices, area expansions, marketing strategies, etc.

Predicting climate-change induced heat-related illness risk in Grand Canyon National Park visitors.

The climate crisis is the greatest public health threat of the 21st century. Excessive heat is responsible for more deaths than any other extreme weather event, and the frequency, intensity, and duration of extreme heat events are increasing globally due to climate change. Exposure to excessive heat can result in heat related illnesses (HRIs) and long-term poor health outcomes. Physical exertion, sudden exposure to excessive heat, and the lack of physical or behavioral adaptation resources are all associated with greater HRI risk, which is expected to increase for visitors to Grand Canyon National Park (GCNP) and other public lands as climate change worsens. Our objectives were to understand 1) the relationship between weather and HRI in GCNP visitors, 2) how future HRI rates may change, and 3) how land management agencies can update risk mitigation strategies to match changing risk and better manage an increased HRI burden. We utilized previously published data on HRI in GCNP visitors, and records of daily visitation, temperatures, and maximum and minimum daily humidity from the same study period to develop a model estimate for HRI risk. We then used future climate projections from the World Climate Research Programme's Coupled Model Intercomparison Project phase 5 multi-model dataset to model future HRI risk under different climate scenarios. The incidence of HRI was significantly associated with maximum daily temperature and minimum relative humidity, and was more common in the shoulder season months. We estimated that HRI will increase 29%-137% over 2004-2009 levels through 2100, assuming no change in visitation. Climate change will continue to increase HRI risk for GCNP visitors and poses risks to public land managers' mission to provide for safe recreation experiences for the benefit of this and future generations in places like GCNP. Excessive risk during the shoulder season months presents an opportunity to increase preventative search and rescue and education efforts to mitigate increased risk.

Relationship of Surface Temperature and Average Relative Humidity for Selected Governorates of Iraq ( Baghdad, Al-Mosul and Al-Muthna )

Abstract study and analysis the trends of the paths of the climatic elements (maximum and minimum temperatures and relative humidity) received great attention and evaluated their statistical importance in terms of assessing climate changes. In this research, data were taken for the maximum and minimum temperature and relative humidity for several different governorates in terms of location, namely (Baghdad, al-Al-Mosul and Al-Muthna), and the Sigma plot program was used to extract the different forms of the relationship between the maximum and minimum temperature and the average relative humidity for each month of the selected years in this study. The time series of temperature and relative humidity showed that the relationship between them is an inverse relationship, as well as the fluctuations that occur in the values of temperature and relative humidity between high and low in different months of the year, but clearly appeared in the spring and autumn seasons, and the reason for this is that these two seasons have transitional characteristics weather, especially temperature. Also, through the values of the correlation coefficient that was extracted for all months, and its results were mostly negative, which are mentioned in the attached tables, which indicate the inverse relationship between of them the two elements mentioned above. through the data and forms that have been reached and the observation of the difference in values between the governorates, starting from Mosul with low temperatures and high relative humidity, and down to Baghdad and then Al-Muthna, which begins with an increase in temperatures and a decrease in relative humidity, and this explains the importance of the geographical location of each governorate and the extent of its impact on the values of temperatures. Temperature, relative humidity and other climatic elements.

The synergistic effect of biochar and poly(2-ethyl-2-oxazoline)/poly(2-hydroxyethylmethacarylate)/chitosan) hydrogels on saline soil properties and carrot productivity

BackgroundSoil salinity is one of the most important factors limiting crop production. Furthermore, with the increasing population and saline soil worldwide there is no choice but to utilize saline soil to increase the agricultural regions. Therefore, to improve carrot productivity under saline conditions, it is necessary to provide good management such as applying hydrogels and biochar for improving soil properties.MethodologyHydrogels (PEtOx-HEMA-CS) were synthesized from poly (2-ethyl-2-oxazoline), 2-hydroxyethyl methacrylate (HEMA as crosslinker) and chitosan (CS) via exposure those to gamma irradiation dose; 30 kGy of dose rate 0.9 kGy/h and obtained three types of hydrogels according to concentration of chitosan used. The PEtOx-HEMA-CS hydrogels were enhanced water holding capacity for agriculture purposes. The chemical structures of obtained hydrogels were characterized by FTIR, XRD and SEM. The swelling (%) and gelation (%) were determined. Biochar (BC) as an active substance was physically mixed with those hydrogels at various ratios (0/100, 0.5/99.5, 1/99 and 100/0 (g/g) biochar/hydrogels). BC, PEtOx-HEMA-CS and the mixture of PEtOx-HEMA-CS-BC were mixed with saline soil at ratio 0.05% and 0.1% w/w of obtained materials/soil. A pot experiment was conducted to mitigate the salinity hazards on carrot productivity using biochar with and without hydrogels. Mean maximum temperature, minimum temperature, precipitation, relative humidity and wind speed from September to December in the studied region are 28.66 ℃, 15.76 ℃, 0.01 mm, 58.81%, 5.94 km/h, respectively.FindingsThe obtained data referred that there is a significant decrease in soil salinity and exchangeable sodium percentage and increase in organic matter, cation exchange capacity, field capacity, permanent wilting point and available water especially at (PEtOx-HEMA-CS5)0.1-BC1. The highest increment percentage of nitrogen, phosphorous and potassium were 36.36%, 70% and 72%, respectively. In addition, the relative increase of carrot productivity was 49.63% at the highest rates of biochar and hydrogels. However, the highest value of water use efficiency was observed at the mixture of biochar and hydrogels at (PEtOx-HEMA-CS5)0.1-BC1.ConclusionsFinally, applying biochar combined with (PEtOx-HEMA-CS5) could be recommended as a good approach to improve carrot productivity and water use efficiency under saline soil conditions.Graphical

Assessing the Variations in Canopy Temperature, Relative Humidity and Soil Temperature within the Canopy of Winter Maize in Bihar

Variations in canopy temperature, relative humidity and soil temperature within crop micro-environment exert full control over plant’s physiological processes and thereby over growth, development and yield of crop. These micro-meteorological parameters tend to significantly control the exchange of energy between the crop environment and the atmosphere. Keeping all this in view, a field experiment with winter maize ( var. Rajendra hybrid 4) was conducted with five dates of sowing viz., 1 November (D1), 10 November (D2), 20 November (D3), 30 November (D4) and 10 December (D5) and three spacings viz., 60 X 20 cm (S1), 75 cm X 20 cm (S2) and 45 cm X 20 cm (S3) at the University farm of Dr Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar during rabi season of 2021-22 to assess the variations in canopy temperature, relative humidity and soil temperature, as influenced by micro-environments and planting density. The experiment was laid out in split plot design with sowing dates as main-plots and spacings as split plots. Results revealed that canopy temperature was found to be the highest for the crop planted on D4 (30 November) with S2 (75cm X 20 cm) spacing, while the lowest was associated with the crop sown on D1 (1 November) with S3 (45 cm X 20 cm) spacing. Mean canopy temperature decreased from sowing to knee high stage, thereafter it increased with every phenological stage up to physiological maturity. Crop sown on D3 (20 November) having S3 (45 cm X 20 cm) spacing recorded maximum relative humidity. Highest soil temperature at 15 cm depth was recorded for D5 (10 December) sown and S2 (75cm X 20cm) spaced crop and the lowest for D2 (10 November) having S3 (45cm X 20cm) spacing. Information generated on the variation in canopy temperature, relative humidity and soil temperature at different phenophases of winter maize would be useful in managing abiotic stresses on crop production.

COMPARISON OF INNOVATIVE TREND ANALYSIS METHODS FOR HYDROMETEOROLOGICAL PARAMETERS IN THE KARASU SUB-BASIN

Türkiye, which has a complex climate structure, is shown among the countries that will be most affected by climate change with the effect of global warming. These effects will differ in different regions due to their topographic structure, location, and orographic characteristics. Trend analyzes are used to determine the direction and magnitude of this variability. In this study, trend analysis was carried out by using hydrometeorological data obtained from streamflow and meteorology stations located in the Karasu Sub-Basin located in the Euphrates-Tigris Basin. Three innovative trend methods, namely the Şen-Innovative Trend Analysis, Onyutha Trend Test, Combination of Wilcoxon Test and Scatter Diagram trend tests, were used together with the classical Mann-Kendall method in the monthly scale analysis of hydrometeorological data of the 1979-2020 period. Both graphical and statistical trend analysis can be done with innovative methods. The results obtained in the study, in which trend analyses were evaluated at the α=0.05 significance level, reveal significant and insignificant decreasing trends in the parameters of mean streamflow, maximum precipitation, total precipitation, mean and minimum relative humidity. Significant and insignificant increasing trends were determined in the maximum, minimum, and mean temperature, maximum relative humidity, and mean wind speed data. The trends obtained in the methods used in general are consistent with each other. The findings of this study could lead to a better knowledge of the region's hydrology and contribute sustainable water management. The trend analysis methods used in the study are thought to be quite helpful in the analysis of hydro-meteorological time series.

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment and can help to lessen the negative effects of climate change by lowering the level of emissions of greenhouse gas. This study developed thirteen different artificial intelligence models, including multivariate adaptive regression splines (MARS), extreme learning machine (ELM), Kernel extreme learning machine (KELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), outlier robust extreme learning machine (ORELM), deep extreme learning machine (DELM), and their versions combined with variational mode decomposition (VMD) as integrated models (VMD-DELM, VMD-ORELM, VMD-OPELM, VMD-OSELM, VMD-KELM, and VMD-ELM), for solar radiation estimation in Kurdistan region, Iraq. The daily meteorological data from 2017 to 2018 were used to implement suggested artificial models at Darbandikhan and Dukan stations, Iraq. The input parameters included daily data for maximum temperature (MAXTEMP), minimum temperature (MINTEMP), maximum relative humidity (MAXRH), minimum relative humidity (MINRH), sunshine duration (SUNDUR), wind speed (WINSPD), evaporation (EVAP), and cloud cover (CLOUDCOV). The results show that the proposed VMD-DELM algorithm considerably enhanced the simulation accuracy of standalone models’ daily solar radiation prediction, with average improvement in terms of RMSE of 13.3%, 20.36%, 25.1%, 27.1%, 34.17%, 38.64%, and 48.25% for Darbandikhan station and 5.22%, 10.01%, 10.26%, 21.01%, 29.7%, 35.8%, and 40.33% for Dukan station, respectively. The outcomes of this study reveal that the VMD-DELM two-stage model performed superiorly to the other approaches in predicting daily solar radiation by considering climatic predictors at both stations.

A Novel Machine Learning Approach for Solar Radiation Estimation

Solar irradiation (Rs) is the electromagnetic radiation energy emitted by the Sun. It plays a crucial role in sustaining life on Earth by providing light, heat, and energy. Furthermore, it serves as a key driver of Earth’s climate and weather systems, influencing the distribution of heat across the planet, shaping global air and ocean currents, and determining weather patterns. Variations in Rs levels have significant implications for climate change and long-term climate trends. Moreover, Rs represents an abundant and renewable energy resource, offering a clean and sustainable alternative to fossil fuels. By harnessing solar energy, we can actively reduce greenhouse gas emissions. However, the utilization of Rs comes with its own challenges that must be addressed. One problem is its variability, which makes it difficult to predict and plan for consistent solar energy generation. Its intermittent nature also poses difficulties in meeting continuous energy demand unless appropriate energy storage or backup systems are in place. Integrating large-scale solar energy systems into existing power grids can present technical challenges. Rs levels are influenced by various factors; understanding these factors is crucial for various applications, such as renewable energy planning, climate modeling, and environmental studies. Overcoming the associated challenges requires advancements in technology and innovative solutions. Measuring and harnessing Rs for various applications can be achieved using various devices; however, the expense and scarcity of measuring equipment pose challenges in accurately assessing and monitoring Rs levels. In order to address this, alternative methods have been developed with which to estimate Rs, including artificial intelligence and machine learning (ML) models, like neural networks, kernel algorithms, tree-based models, and ensemble methods. To demonstrate the impact of feature selection methods on Rs predictions, we propose a Multivariate Time Series (MVTS) model using Recursive Feature Elimination (RFE) with a decision tree (DT), Pearson correlation (Pr), logistic regression (LR), Gradient Boosting Models (GBM), and a random forest (RF). Our article introduces a novel framework that integrates various models and incorporates overlooked factors. This framework offers a more comprehensive understanding of Recursive Feature Elimination and its integrations with different models in multivariate solar radiation forecasting. Our research delves into unexplored aspects and challenges existing theories related to solar radiation forecasting. Our results show reliable predictions based on essential criteria. The feature ranking may vary depending on the model used, with the RF Regressor algorithm selecting features such as maximum temperature, minimum temperature, precipitation, wind speed, and relative humidity for specific months. The DT algorithm may yield a slightly different set of selected features. Despite the variations, all of the models exhibit impressive performance, with the LR model demonstrating outstanding performance with low RMSE (0.003) and the highest R2 score (0.002). The other models also show promising results, with RMSE scores ranging from 0.006 to 0.007 and a consistent R2 score of 0.999.

Short-Term Effects of Climate Variability on Childhood Diarrhoea in Bangladesh: Multi-Site Time-Series Regression Analysis.

The aim of this study was to estimate the effects of climate on childhood diarrhoea hospitalisations across six administrative divisions in Bangladesh and to provide scientific evidence for local health authorities for disease control and prevention. Fortnightly hospital admissions (August/2013-June/2017) for diarrhoea in children under five years of age, and fortnightly average maximum temperature, relative humidity and rainfall recordings for six administrative divisions were modelled using negative binomial regression with distributed lag linear terms. Flexible spline functions were used to adjust models for seasonality and long-term trends. During the study period, 25,385 diarrhoea cases were hospitalised. Overall, each 1 °C rise in maximum temperature increased diarrhoea hospitalisations by 4.6% (IRR = 1.046; 95% CI, 1.007-1.088) after adjusting for seasonality and long-term trends in the unlagged model. Using lagged effects of maximum temperature, and adjusting for relative humidity and rainfall for each of the six administrative divisions, the relationship between maximum temperature and diarrhoea hospitalisations varied between divisions, with positive and negative effect estimates. The temperature-diarrhoea association may be confounded by seasonality and long-term trends. Our findings are a reminder that the effects of climate change may be heterogeneous across regions, and that tailored diarrhoea prevention strategies need to consider region-specific recommendations rather than relying on generic guidelines.

Evaluation of meteorological time series trends in Southeastern Anatolia, Turkey

In this study, trend analyses of six climatic variables (mean, minimum, and maximum temperature, relative humidity, wind speed, and precipitation) for 1966-2020 are conducted for the Southeastern Anatolia Region, which is the main focus of the integrated development project in Turkey (Turkish acronym GAP). The trends for seasonal and annual periods are determined using the Mann-Kendall (MK) test, and Sen's slope method and regression analyses are used to find the trends' slopes. Moreover, Innovative Trend Analysis (ITA) is also used to find the time series changes for low, medium, and high values. As a result of the analyses, the mean, minimum, and maximum temperatures in the GAP region show increasing trends according to both methods. Significant trends are obtained at a limited number of stations for the precipitation, relative humidity, and wind speed with the MK test, while consistent decreasing trends are found at most stations with the ITA method.

Determining Meteorological Parameters Influencing Photovoltaic Solar Energy Generation in Quezon City Using Machine Learning Algorithms

One challenge in adapting to energy generation using solar photovoltaic (PV) modules is its variability with changing weather conditions. In this study, we aim to determine the effect of meteorological parameters that have the most effect on the variability of solar energy generation (SEG). Our study is conducted in Quezon City, part of the National Capital Region, Philippines. The maximum temperature, relative humidity, man temperature, and cloud opacity have the most effect on the variability of the SEG among the eight meteorological parameters that we consider in our study based on the principal component regressor (PCR) and random forest regressor (RFR) machine learning algorithms. The PCR model explains 55.5 and 49.2% variability in SEG of the training and test sets, respectively. On the other hand, the RFR model explains a 77.1% variation of the SEG in the training and 52.7% in the test set. Furthermore, the two models provided comparable predictions of SEG.

Correlation Between Climate Indicators and COVID-19 Pandemic in Nigeria, Ghana, and South Africa

This study examines the relationship between climatic indicators (maximum temperature, minimum temperature, precipitation, and relative humidity) and the spread of COVID-19 using weekly data of confirmed cases and death rates from 3/25/2020 to 12/30/2020 for Nigeria, South Africa, and Ghana. Using an ex-post research design and descriptive method of analysis, the results of the study confirm evidence of correlation between climatic variables and the spread of the COVID-19 virus among the three selected countries. However, the policy recommendation that emanates from this study is that climate mitigation policies can be promoted as pandemic prevention policies, thus making a stronger case for their implementation to forestall future reoccurrence.

Seasonal Occurrence of Greater Banded Hornet (Vespa tropica Linn.) in Western Honey Bee (Apis mellifera Linn.) Colonies under Terai Agro-ecological Region of West Bengal

The present study was conducted during September, 2018 to August, 2020 on the seasonal incidence of Vespa tropica Linn. on Apis mellifera Linn. under terai agro-ecological region of West Bengal, India. Ten numbers of A. mellifera colonies of similar strength was placed in the apiary unit of Uttar Banga Krishi Viswavidyalaya, West Bengal, India in which no pest management strategies were carried out. Incidence of V. tropica was recorded higher during July to October peaking in the month of July (251.50±22.17 visits day-1) with annual average success rate of 57.23%±1.16%. From December to March little or no incidence was recorded. Minimum abundance of hornets was recorded at 07:00–09:00 hours. After that incidence started increasing with maximum incidence recorded at 13:00–15:00 hours i.e., 43.52±11.97 visits to the bee colonies day-1 during this time gap. Both maximum and minimum temperature and relative humidity showed significantly positive correlation with the incidence of V. tropica. However, rainfall was found to be detrimental for their incidence as it showed negative and non-significant correlation with their incidence. As V. tropica has been considered as a major enemy of honey bees, so the results obtained from the study can be used for designing integrated pest management strategies against this pest that will allow the beekeepers to focus on that particular time period when this pest is more abundant.

Effect of growing season on some qualitative and functional attributes of different potato genotypes

AbstractPotato (Solanum tuberosum L.) is a staple food widely consumed in Tunisia and a source of nutrients beneficial for human health. The tubers are, indeed, important source of vitamins, minerals, and bioactive compounds, including carotenoids, polyphenols, and flavonoids. However, data regarding the functional quality of potato varieties grown and consumed in Tunisia are very scarce. For this reason, two field trials were conducted to focus on the effect of the growing season on the functional quality of four potato genotypes (Selena, El Beidha, Naima, and CL07F172.5): a first autumnal trial in Teboulba, Monastir (September 2015–February 2016), which is the late cropping season (LCS) and a second summer cropping season (SCS) in El ksour, El Kef region at an altitude > 1000 m (July 2015–October 2015). Total carotenoids, total phenolics, and flavonoids contents, as well as the hydrophilic radical scavenging activity and lipophilic radical scavenging activity (LRSA) were determined. The obtained results confirmed the importance of potato tubers as a source of bioactive compounds. All quality traits varied significantly among varieties and cropping seasons. Tubers grown under colder weather conditions (LCS: minimum and maximum averages temperatures and relative humidity (RH) ranging between 14.25 and 22.55°C and 25.46% and 83.07%, respectively) accumulated the highest total vitamin C content and exhibited the highest LRSA values. However, temperature and warmer growing conditions (SCS: minimum and maximum averages temperatures and RH ranging between 19.74 and 34.22°C and 35.46% and 87.51%, respectively) probably enhanced the synthesis and accumulation of total carotenoids, total phenolics, and total flavonoids, associated with higher LRSA values.

Extreme Water Uptake of Hygroscopic Hydrogels through Maximized Swelling-Induced Salt Loading.

Hygroscopic hydrogels are emerging as scalable and low-cost sorbents for atmospheric water harvesting, dehumidification, passive cooling, and thermal energy storage. However, devices using these materials still exhibit insufficient performance, partly due to the limited water vapor uptake of the hydrogels. Here, the swelling dynamics of hydrogels in aqueous lithiumchloride solutions, the implications on hydrogel salt loading, and the resulting vapor uptake of the synthesized hydrogel-salt composites are characterized. By tuning the salt concentration of the swelling solutions and the cross-linking properties of the gels, hygroscopic hydrogels with extremely high salt loadings are synthesized, which enable unprecedented water uptakes of 1.79 and 3.86gg-1 at relative humidity (RH) of 30% and 70%, respectively. At 30% RH, this exceeds previously reported water uptakes of metal-organic frameworks by over 100% and of hydrogels by 15%, bringing the uptake within 93% of the fundamental limit of hygroscopic salts while avoiding leakage problems common in salt solutions. By modeling the salt-vapor equilibria, the maximum leakage-free RH is elucidated as a function of hydrogel uptake and swelling ratio. These insights guide the design of hydrogels with exceptional hygroscopicity that enable sorption-based devices to tackle water scarcity and the global energy crisis.

Influence of Weather, Vector and Variety on Incidence of Yellow Leaf Disease in Sugarcane

Yellow leaf disease of sugarcane (Saccharum spp.) caused by the Sugarcane yellow leaf virus (SCYLV), vectored by aphids has attained epidemic proportions causing severe yield losses, ranging from 20 to 40 % in susceptible varieties. Yellow leaf spread by aphids depends on cultivar susceptibility as well as weather parameters and thus the present studies were conceptualized. The observations on meteorological parameters were taken from the observatory at the station. The data on incidence of vector(aphids) and yellow leaf disease were recorded at weekly interval during the entire crop growth period on three sugarcane varieties viz., 2005 A 128, 2001 A 63 and 2003 V 46. The data on vector and disease incidence was correlated with weather parameters. The aphid incidence on 2005 A 128 initiated (4.6 per leaf) at 30 SMW and gradually increased to 19.1 per leaf at 40 SMW. In the variety 2001 A 63, the aphid incidence was first observed (2.2 per leaf) at 27 SMW and reached the peak population of 19.1 per leaf at 38 SMW. The aphid incidence on 2003 V 46 initiated (2.1 per leaf) at 29 SMW and gradually increased to 18.1 per leaf at 42 SMW. With regard to yellow leaf disease the incidence was first observed (1.0 per cent) at 26 SMW and reached the peak (28.7 per cent) at 37 SMW in the variety 2005 A 128. In the variety 2001 A 63, the incidence of yellow leaf disease was 1.8 per cent at 26 SMW which later on increased up to 29.4 per cent by 46 SMW. The incidence of yellow leaf disease was 3.5 per cent at 28 SMW which later on increased up to 27.6 per cent by 44 SMW in the variety 2003 V 46.The observations on incidence of aphids and YLD in susceptible varieties revealed that aphids contribute significantly to the initial spread of YLD, from initial incidence of aphids and YLD in 29-30 SW up to 42-44 SMW when the aphids reach peak incidence and YLD also leaps to above 25 per cent. The aphid population exhibited positive correlation with maximum temperature (r2=0.62), minimum temperature (r2=0.55) and relative humidity I (r2=0.65), whereas, negative correlation with rainfall (r2=-0.63). The yellow leaf disease exhibited positive correlation with minimum temperature (r2=0.75) and relative humidity I (r2=0.67), whereas, negative correlation with rainfall (r2=-0.63). The leaf and aphid samples were collected at peak incidence of Yellow leaf disease and were tested and found positive for presence of virus using ELISA reader at 405 nm.

One piece of the puzzle: Modeling vector presence and environment reveals seasonality, distribution, and prevalence of sandflies and Leishmania in an expansion area

The recent geographic spread of Leishmania infantum along the borders of Argentina, Brazil and Paraguay has been highlighted. In our previous study, Lutzomyia longipalpis was found in 55 of 123 patches surveyed, and in some patches, sandflies were found at higher densities, forming hotspots. Based on the One Health approach, we investigated the seasonality of the vector, the presence of parasite DNA, and the environmental factors that contribute to vector and parasite dispersal in these previously described hotspots in Foz do Iguaçu, Brazil. Entomological surveys were conducted monthly for one year. Fourteen hotspots peridomicile and six intradomicile were sampled. PCR was used to assess the prevalence of Leishmania DNA in sandflies. Zero-inflated negative binomial regression was used to determine the association of micro- and mesoscale environmental variables with the occurrence and abundance of the three most abundant sandfly species sampled. A total of 3543 species were captured, with Lutzomyia longipalpis being the predominant species (71.78%) of the 13 species found. Evandromyia edwardsi, Expapillata firmatoi, Micropygomyia ferreirana and Pintomyia christenseni were reported for the first time in the region. NDVI, distance to water, precipitation, west-to-east wind, wind speed, maximum and minimum relative humidity, and sex were significant variables associated with vector presence/abundance in the environment. Vector presence/abundance in the peridomicile was associated with precipitation, altitude, maximum temperature, minimum and maximum relative humidity, west-to-east wind, wind speed, and sex. Leishmania DNA was detected in an average of 21% of Lu. longipalpis throughout the year. Vector abundance is concentrated in urban and peri-urban areas, with some specimens present in different parts of the city and some sites with high vector abundance. This distribution suggests that the risk of actual contact between humans and parasite vectors in urban areas during the epidemic period is associated with patches of peri-urban vegetation and then extends into urban areas.

Morpho-molecular characterization and bioecology of leaf folder, Pycnarmon cribrata (Fabricius) on nirgundi (Vitex negundo): an aromatic medicinal shrub from India

The occurrence of leaf folder, Pycnarmon cribrata (Fabricius, 1794) (Lepidoptera: Crambidae) on the medicinal plant nirgundi, Vitex negundo Linn. has been observed in West Bengal, India. The pest damaged the foliage by folding the leaflets, feeding within and ultimately making the foliage dry and papery. However, the identity of the pest species was confirmed through morphological characteristics of male and female genitalia as well as sequence amplified product of Cytochrome c oxidase subunit I (COI) gene (GenBank No. ON545929). Biological studies revealed that the entire life cycle of the moth completed in 29.90 ± 2.42 days on average, while the range was between 25-34 days. The peak larval incidence was noticed in 16th SMW (Standard Meteorological Week) and 24th SMW with population of 12 and 15 larvae per plant during 2021 and 2022, respectively. The percent plant incidence levels ranged from 15 to 100% during the first year i.e. 2021 and 30 to 100% during the second year i.e. 2022. The correlation of mean larval population (r = −0.66) and percent plant incidence (r = −0.71) with weather parameters indicated a highly significant negative association with the maximum temperature and a significant positive correlation with maximum and minimum relative humidity. The parasitization of Choeras sp. (Braconidae: Hymenoptera) (GenBank No. OP293230) was observed on old age larvae of P. cribrata and the natural parasitization was recorded as 26% during the study period. This study provides a comprehensive information on the occurrence, nature of damage, biology, morphometry, seasonal incidence and natural enemy fauna associated with P. cribrata which will be helpful for implementation of Integrated Pest Management (IPM) strategies to curtail the pest problem on V. negundo, a valuable medicinal plant.

Numerical Study on the Heating Effect of a Spring-Loaded Actuator—Part Ⅰ: Temperature and Humidity Distribution Characteristics

Inappropriate distributions of temperature and humidity will cause the failure of the spring-loaded actuators. Therefore, it is essential to understand the temperature and humidity distribution characteristics in typical spring-loaded actuators, to guarantee the safe operation of the spring-loaded actuators. In this work, a numerical simulation study on the temperature and humidity distribution characteristics in a spring-loaded actuator was conducted. The influence laws of ambient temperature, heater power, and heater size on the temperature and humidity distributions inside the spring-loaded actuator were analyzed. The practical empirical correlations for the spring-loaded actuators were fitted. The results show that the air temperature around and directly above the heater is the highest and the corresponding relative humidity is the lowest. Then, the air temperature gradually decreases, and the relative humidity increases with the lateral flow of air. When the ambient temperature increases from 233.15 K (−40 °C) to 313.15 K (40 °C), the minimum temperature inside the actuator is increased by 34%, the maximum humidity first increases and then decreases, and the maximum temperature on the heater surface is increased by 30%. When the heating power increases from 10 W to 150 W at ambient temperatures of 273.15 K and 298.15 K, the minimum temperature inside the actuator is increased by 3.40% and 3.61%, the maximum humidity is decreased by 51.97% and 58.63%, and the maximum temperature on the heater surface is increased by 30.33% and 33.25%, respectively. The influence of heater length, width, and height on the minimum temperature and maximum relative humidity inside the spring-loaded actuator is relatively small. Within the study range, the increase in heater length, width, and height makes the maximum temperature on the heater surface decrease by 9.15%, 7.59%, 4.63% at ambient temperatures of 273.15 K, and 10.74%, 9.01%, 4.73% at ambient temperature of 298.15 K, respectively. The results may provide a reference for predicting temperature and humidity distributions inside general spring-loaded actuators and provide a calculation basis for the design of their heaters.