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In order to accurately and easily predict the variation of the average temperature of the concrete rectangular hollow pier. Firstly, the temperature distribution of the concrete rectangular hollow pier of Changjiahe Special Bridge was observed for 212 days. Then, based on the observation data, the functional relationship between the average concrete hollow pier temperature and the outside air temperature, and the air temperature inside the hollow pier was studied. Finally, based on this functional relationship, a prediction model for the range of variation of the mean temperature of the hollow pier was given and verified. The results of the study show that: the change of external shade temperature can be regarded as the superposition of different cyclic changes and random changes; the change rule of the average temperature of the concrete hollow pier is the same as that of the air temperature, both presenting day-by-day cyclic and step changes; the linear correlation coefficients between the daily maximum and daily minimum average hollow pier temperature and the daily average air temperature are R = 0.980 and R = 0.973, respectively; the daily average air temperature, the daily average air temperature inside the pier and daily average hollow pier temperature are R = 0.980, R = 0.998; the daily variation of the average hollow pier temperature and the daily variation of the air temperature are approximately linear, with a correlation coefficient of R = 0.899; assuming that the average temperature of the concrete hollow pier is a folding change, based on the above relationship, a method of predicting the time-by-time average temperature of the concrete hollow piers is proposed, as well as two methods of predicting the average temperature of the test piers. Average temperature change range method. Comparing the predicted values with the measured values, it is found that the predicted values are in good agreement with the measured values. [ABSTRACT FROM AUTHOR]

In this study, we report observations of propagating radial carpet beams (RCBs) through a turbulent atmosphere at ground level with a 120 m path length. RCBs are a class of nondiffracting, accelerating, self-healing, and self-amplifying beams, and generated in the diffraction of a plane wave from amplitude/phase radial gratings having different spoke numbers. Observations were made at different times of the day. The intensity profile of an RCB becomes complicated when the number of grating spokes used to generate the beam is large, and includes high intensity spots, called main intensity spots (MISs), which are symmetrically placed at the central area around the beam axis and whose number is equal to (twice) the number of spokes of the amplitude (phase) grating used to generate the beam. With the aid of a telescope and a CCD camera, successive frames of the intensity pattern of the RCBs having different levels of structural complexity are recorded at the end of the path. For the data recorded at different times of the day, we calculate the variance of displacements of MISs along the radial direction. We observe that displacements of the MISs increase with increasing mean temperature of the air; on the other hand, as the complexity of the beam intensity pattern increases, the displacements of the MISs decrease. In order to compare the resilience of different RCBs and a well-known structured beam against atmospheric turbulence, we investigate deformation of the intensity profiles of a Laguerre–Gaussian (LG) beam having a topological charge 20 and different RCBs at the end of the path. It is shown that under the same turbulence condition, highly complex RCBs are more resilient to the destructive effects of the atmospheric turbulence. In particular, for the RCBs generated with gratings having 30 spokes and more, the number of MISs of the received intensity patterns is changed by less than 1% even when the turbulence strength is high. But for the LG beam, its intensity ring is clearly broken in different places, which makes it impossible to follow its maximum intensity in the radial direction [ABSTRACT FROM AUTHOR]

Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study's objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone's reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone's reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations. [ABSTRACT FROM AUTHOR]

Color centers in wide band-gap semiconductors, which have superior quantum properties even at room temperature and atmospheric pressure, have been actively applied to quantum sensing devices. Characterizing the quantum properties of the color centers in the semiconductor materials and ensuring that these properties are uniform over a wide area are key issues for developing quantum sensing devices based on color centers. In this article, we have developed an optics design protocol optimized for evaluating the quantum properties of color centers and have used this design approach to develop a new microscopy system called columnar excitation fluorescence microscope (CEFM). The essence of this system is to maximize the amount of fluorescence detection of polarized color centers, which is achieved by large-volume and uniform laser excitation along the sample thickness with sufficient laser power density. This laser excitation technique prevents undesirable transitions to undesirable charge states and undesirable light, such as unpolarized color center fluorescence, while significantly increasing the color center fluorescence. This feature enables fast measurements with a high signal-to-noise ratio, making it possible to evaluate the spatial distribution of quantum properties across an entire mm-size sample without using a darkroom, which is difficult with typical confocal microscope systems. [ABSTRACT FROM AUTHOR]

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951–31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy than other compared models. The hybrid CNN-LSTM model significantly reduces the forecasting error compared to the models for the one month time step ahead. For instance, the RMSE values of the ANN, RNN, LSTM, CNN, and CNN-LSTM models for monthly average atmospheric temperature in the forecasting stage are 2.0669, 1.4416, 1.3482, 0.8015 and 0.6292 °C, respectively. The findings of climate simulations shows the potential of CNN-LSTM models to improve climate forecasting. Climate prediction will contribute to meteorological disaster prevention and reduction, as well as flood control and drought resistance. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach to visibility modelling, focusing on PM1 concentration, its chemical composition, and meteorological conditions in two distinct Polish cities, Zabrze and Warsaw. The analysis incorporates PM1 concentration measurements as well as its chemical composition and meteorological parameters, including visibility data collected during summer and winter measurement campaigns (120 samples in each city). The developed calculation procedure encompasses several key steps: formulating a visibility prediction model through machine learning, identifying data in clusters using unsupervised learning methods, and conducting global sensitivity analysis for each cluster. The multi-layer perceptron methods developed demonstrate high accuracy in predicting visibility, with R values of 0.90 for Warsaw and an RMSE of 1.52 km for Zabrze. Key findings reveal that air temperature and relative humidity significantly impact visibility, alongside PM1 concentration and specific heavy metals such as Rb, Vi, and Cd in Warsaw and Cr, Vi, and Mo in Zabrze. Cluster analysis underscores the localized and complex nature of visibility determinants, highlighting the substantial but previously underappreciated role of heavy metals. Integrating the k-means clustering and GSA methods emerges as a powerful tool for unravelling complex mechanisms of chemical compound changes in particulate matter and air, significantly influencing visibility development. [ABSTRACT FROM AUTHOR]

Possible local influence of an extreme marine heatwave is investigated on unprecedentedly hot summer around northern Japan in 2023. Sea-surface temperatures (SSTs) and subsurface ocean temperatures around northern Japan were also unprecedentedly high in the summer. This was especially the case off the east coast of Japan, where cool Oyashio water was replaced with much warmer water due to a striking poleward meander of the Kuroshio Extension persistent from the spring. Particularly amplified near-surface air temperature anomalies and even stronger warm anomalies in the subsurface ocean suggest that the marine heatwave acted to sustain the atmospheric heatwave. Anomalous upward of latent and sensible heat fluxes from the warmed sea surface are indicative of local oceanic impact. The warm SST anomalies reduced the lower-tropospheric stratification to maintain unfavourable condition for low-level cloud formation, which in turn led to increased surface insolation for further SST warming as positive feedback. The increased moisture in the warmed lower troposphere contributed to the enhanced surface downward longwave radiation. This enhanced greenhouse effect acted not only as positive feedback on the warm SST anomalies that increased evaporation but also as a contributor for the extreme warmth over northern Japan landmass. [ABSTRACT FROM AUTHOR]

This article examines the effects of different storage conditions on selected physicochemical properties of three types of agro-biomass pellets: sunflower husks, wheat straw and hemp hurds, and wood pellets. The tests were carried out in a climatic chamber, which allows simulation of real storage conditions, i.e. conditions with high air humidity and variable (±) ambient air temperatures. The results showed higher degradability of agro-biomass pellets compared to woody biomass. The pellets degraded to a less extent at varying ± temperatures than at high humidity (90% RH). After complete moisture saturation, durability decreases for agro-pellets by an average of 9%, while after freezing and defreezing for sunflower husk pellets and woody pellets durability decreases by 2%, and for hemp hurd pellets by 11%. In contrast, strength-by-dropping index for agro-pellets decreased by 20% after being in the environment (30 °C and 90%RH) and 15% under varying temperature conditions. No change in the energy parameters of all pellets in the dry matter was noted. On the other hand, an increase in the moisture content of pellets when they are stored under different environmental conditions results in a decrease in calorific value. [ABSTRACT FROM AUTHOR]

Birds maintain some of the highest body temperatures among endothermic animals. Often deemed a selective advantage for heat tolerance, high body temperatures also limits birds' thermal safety margin before reaching lethal levels. Recent modelling suggests that sustained effort in Arctic birds might be restricted at mild air temperatures, which may require reductions in activity to avoid overheating, with expected negative impacts on reproductive performance. We measured within-individual changes in body temperature in calm birds and then in response to an experimental increase in activity in an outdoor captive population of Arctic, cold-specialised snow buntings (Plectrophenax nivalis), exposed to naturally varying air temperatures (− 15 to 36 °C). Calm buntings exhibited a modal body temperature range from 39.9 to 42.6 °C. However, we detected a significant increase in body temperature within minutes of shifting calm birds to active flight, with strong evidence for a positive effect of air temperature on body temperature (slope = 0.04 °C/ °C). Importantly, by an ambient temperature of 9 °C, flying buntings were already generating body temperatures ≥ 45 °C, approaching the upper thermal limits of organismal performance (45–47 °C). With known limited evaporative heat dissipation capacities in these birds, our results support the recent prediction that free-living buntings operating at maximal sustainable rates will increasingly need to rely on behavioural thermoregulatory strategies to regulate body temperature, to the detriment of nestling growth and survival. [ABSTRACT FROM AUTHOR]

Diffuse light is produced by clouds and aerosols in the atmosphere. Exploring the effects of diffuse light on ecosystem productivity is important for understanding the terrestrial carbon (CO2) cycle. Here, 2 years of gross ecosystem primary productivity (GEP) from a (winter) wheat cropland in China was assessed using eddy covariance technology to explore the effects of diffuse photosynthetic active radiance (PAR) on wheat GEP. Wheat GEP increased significantly and positively along with diffuse PAR. In addition, wheat GEP was significantly affected by total PAR, air temperature, and vapor press deficit in different diffuse PAR fraction (fDIF) change stages. Because significant autocorrelations existed among the controlling factors, a path analysis was used to quantify the effects of diffuse light on GEP. Diffuse PAR was the primary and secondary importance factors affecting GEP with direct path coefficients of 0.54 and 0.48, respectively, in different fDIF change stages. A multilayer canopy model revealed that the middle and lower canopy levels intercepted more light when diffuse PAR increased. This resulted in the photosynthetic enhancement of middle and lower canopy levels, which contributed approximately 65% and 35%, respectively, to the increase in photosynthesis for the entire canopy (~ 30.5%). Overall, our study provided new evidence regarding the importance of diffuse light for CO2 uptake in agroecosystems, which is important for predicting the responses of ecosystem CO2 budgets to future climate-related light changes. [ABSTRACT FROM AUTHOR]

We applied a perfect prognosis approach to downscale four meteorological variables that affect photovoltaic (PV) power output using four machine learning (ML) algorithms. In addition to commonly investigated variables, such as air temperature and precipitation, we also focused on wind speed and surface solar radiation, which are not frequently examined. The downscaling performance of the four variables followed the order of: temperature > surface solar radiation > wind speed > precipitation. Having assessed the dependence of the downscaling accuracy on the scaling factor, we focused on a super-resolution downscaling. We found that the convolutional neural network (CNN) generally outperformed the other linear and non-linear algorithms. The CNN was further able to reproduce extremes. With the rapid transition from coal to renewables, the need to evaluate low solar output conditions at a regional scale is expected to benefit from CNNs. Because weather affects PV power output in multiple ways, and future climate change will modify meteorological conditions, we focused on obtaining exemplary super-resolution application by evaluating future changes in PV power outputs using climate simulations. Our results confirmed the reliability of the CNN method for producing super-resolution climate scenarios and will enable energy planners to anticipate the effects of future weather variability. [ABSTRACT FROM AUTHOR]

Taking gas and the heat transfer process between gas and plate as the research object, the mathematical model of heat transfer in one working cycle by moving variable temperature air mass under the action of sound field is established, which provides a new idea for understanding thermoacoustic effect. The influence factors in the model are analyzed and it is found that the amplitude of the air mass has a significant influence on the heat transfer, and the heat transfer of the air mass in one working cycle is proportional to the square of the amplitude. In a certain working environment, the thermoacoustic refrigerator has a critical operating frequency, and only when the operating frequency is greater than the critical frequency can refrigeration be realized. The critical operating frequency is independent of the amplitude and increases with the increase of the stack temperature gradient. With the pressure belly point as the reference position, the greater the distance from the reference position, the greater the critical operating frequency. On this basis, the idea of short plate overlapping is put forward and the formation mechanism of temperature difference between two ends of plate overlapping is explained. [ABSTRACT FROM AUTHOR]

Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes. [ABSTRACT FROM AUTHOR]

Climate change is closely monitored and numerous studies reports increasing air temperature and weather extremes across the globe. As a direct consequence of the increase of global temperature, the increased heat stress is becoming a global threat to public health. While most climate change and epidemiological studies focus on air temperature to explain the increasing risks, heat strain can be predicted using comprehensive indices such as Universal Thermal Climate Index (UTCI). The Asia–Pacific region is prone to thermal stress and the high population densities in the region impose high health risk. This study evaluated the air temperature and UTCI trends between 1990 and 2019 and found significant increasing trends for air temperature for the whole region while the increases of UTCI are not as pronounced and mainly found in the northern part of the region. These results indicate that even though air temperature is increasing, the risks of heat stress when assessed using UTCI may be alleviated by other factors. The associations between El Niño Southern Oscillation (ENSO) and heat stress was evaluated on a seasonal level and the strongest regional responses were found during December-January (DJF) and March–May (MAM). [ABSTRACT FROM AUTHOR]

The sinter cooler, essential for cooling hot sintered ore to a specific temperature, has seen recent advancements with the introduction of a vertical sinter cooling furnace. This innovation aims to enhance energy efficiency, reduce emissions, and improve waste heat recovery. Despite significant research, a quantitative analysis of factors impacting its cooling and heat transfer efficiency is lacking. This study utilizes the Euler model and local non-equilibrium thermodynamic theory to identify key factors affecting the gas–solid cooperative cooling process in the vertical cooler. Through an orthogonal experimental approach, the paper determines the optimal structural and operational parameters for the furnace. Key findings include that a gas–solid ratio of 1200m^3/t, inlet air temperature of 50 ℃, cooling section height of 6m, and diameter of 13.25m maximize efficiency, achieving a weighted range normalization value of 0.962. This configuration meets sintered ore cooling requirements while optimizing waste heat recovery. The study reveals that the impact on heat transfer efficiency is influenced primarily by the gas–solid ratio, followed by the cooling section's height, diameter, and inlet air temperature. These insights are crucial for enhancing the vertical sinter cooler's design, contributing to more energy-efficient and environmentally friendly sintering processes. [ABSTRACT FROM AUTHOR]

As an important woody oilseed species in China, Triadica sebifera is not only concerned with the substitution of traditional energy sources, but also plays a considerable role in coping with energy shortages. Accurately predicting the potential geographic distribution of Triadica sebifera in China and understanding its ecological needs are crucial for alleviating the energy crisis and effectively implementing energy substitution strategies. In this study, the potential geographic distribution of Triadica sebifera in China at contemporary and future periods was predicted based on the distribution data of Triadica sebifera in China and the environmental factor variables by Maxent model and ArcGIS software. The combination of important factors governing the potential geographic distribution of Triadica sebifera was assessed by the contribution of environmental factor variables. The accuracy of Maxent model's predictions was assessed by AUC values, TSS values and Kappa statistics. The results show that: High AUC and TSS values indicate high accuracy and performance of the model. The crucial environmental factors limiting the potential geographic distribution of Triadica sebifera are the temperature factor (mean air temperature of the driest quarter), precipitation factor (precipitation of the coldest quarter, precipitation of the wettest month), and the intensity of human activities (hf). The total suitable area for Triadica sebifera is 233.64 × 104 km2, primarily located in Yunnan, Sichuan, Hubei, Guizhou, Jiangxi, Guangdong province and Guangxi Zhuang Autonomous Region; its high suitability area is 30.89 × 104 km2, accounting for 13.22% of the total suitable area, mainly distributed in Jiangxi, Sichuan and Hunan provinces in the shape of a cake. Under the four typical greenhouse gas emission concentration patterns in the 2050s and 2070s, the areas of high and medium suitable areas for Triadica sebifera will increase, while the area of its low suitable area will decrease. However, the total suitable area will remain relatively unchanged. Its potential suitable habitats show a trend of shifting towards lower latitudes and southeast regions. The study predicted the pattern of Triadica sebifera under different climate change conditions, which can provide guidance for future cultivation of Triadica sebifera as well as for biofuel development and utilization. [ABSTRACT FROM AUTHOR]

The phase in which precipitation falls—rainfall, snowfall, or sleet—has a considerable impact on hydrology and surface runoff. However, many weather stations only provide information on the total amount of precipitation, at other stations series are short or incomplete. To address this issue, data from 40 meteorological stations in Poland spanning the years 1966–2020 were utilized in this study to classify precipitation. Three methods were used to differentiate between rainfall and snowfall: machine learning (i.e., Random Forest), daily mean threshold air temperature, and daily wet bulb threshold temperature. The key findings of this study are: (i) the Random Forest (RF) method demonstrated the highest accuracy in rainfall/snowfall classification among the used approaches, which spanned from 0.90 to 1.00 across all stations and months; (ii) the classification accuracy provided by the mean wet bulb temperature and daily mean threshold air temperature approaches were quite similar, which spanned from 0.86 to 1.00 across all stations and months; (iii) Values of optimized mean threshold temperature and optimized wet bulb threshold temperature were determined for each of the 40 meteorological stations; (iv) the inclusion of water vapor pressure has a noteworthy impact on the RF classification model, and the removal of mean wet bulb temperature from the input data set leads to an improvement in the classification accuracy of the RF model. Future research should be conducted to explore the variations in the effectiveness of precipitation classification for each station. [ABSTRACT FROM AUTHOR]

The etiology of Bell's palsy (BP) is currently unknown, and the findings from previous studies examining the association between seasonal or meteorological factors and BP have been inconsistent. This research aims to clarify this relationship by analyzing a larger dataset and employing appropriate statistical methods. Data from 5387 patients with BP treated at Zhejiang Provincial Hospital of Traditional Chinese Medicine in Hangzhou, Zhejiang Province, from May 1, 2018, to June 30, 2023, was gathered. We assessed the temporal distribution of meteorological factors and the incidence of BP across seasons and months. A distributed lag non-linear model was used to further investigate the lagged and overall effects of temperature and air pressure on the onset of BP. The temporal distribution of BP incidence revealed the highest average number of cases occurring in December and the lowest in June. A correlation existed between BP episodes and temperature or air pressure. The model revealed a higher relative risk during periods of low temperature and high air pressure, characterized by a time lag effect. This correlation was notably more pronounced in female patients and individuals in the young and middle-aged groups. Our findings suggest that exposure to low temperatures and high air pressure constitute risk factors for BP development. [ABSTRACT FROM AUTHOR]

Under climate change, increasing air temperature average and variability pose substantial thermal challenges to animals. While plasticity in thermoregulatory traits could potentially attenuate this impact, whether thermal acclimatisation can occur quickly enough to track weather variability in hot climates is unknown in any endotherm, and sex differences have never been tested. We investigated acclimatisation responsiveness of male and female wild zebra finches to short-term (< 2 weeks) summer temperature fluctuations in the Australian desert. Hotter weather before respirometry trials triggered a typical acclimatisation response (especially at chamber temperature Tchamb ≥ 40). However, acclimatisation occurred remarkably rapidly: metabolic rate responded within just one day, while body temperature (Tb) and evaporative cooling capacity (EHL/MHP) were best predicted by weather on the trial day; whereas evaporative water loss responded more slowly (1 week). Nonetheless, rapid acclimatisation only occurred in males, and females had higher Tb and lower EHL/MHP than males, potentially increasing hyperthermia risk. Furthermore, acclimatisation did not translate into greater acute heat tolerance (i.e. ability to tolerate Tchamb = 46 °C). Our results therefore reveal surprisingly rapid acclimatisation and even anticipatory adjustments to heat. However, with no changes in acute heat tolerance, and in females, phenotypic flexibility may provide only limited buffering against the detrimental impact of heatwaves. [ABSTRACT FROM AUTHOR]

Heating and cooling degree hours (HDH and CDH) are weather-based technical indexes designed to describe the need for energy requirements of buildings. Their calculation is the simplest method to estimate energy demand, providing the pattern of internal temperature variations in a building in response to weather conditions. The aim of the study is HDH and CDH prediction for Wrocław, Poland, based on outdoor air temperature using machine learning methods: artificial neural networks and support vector regression (ANN and SVR). The key issues raise in the study are: a detailed analysis of the most significant temperature lags (from 1 to 24 past hours) serving as predictors for modelling and an assessment of the impact of the database clustering on its accuracy. The best results are obtained with the clustering approach. The best predictor is the outdoor temperature observed 1 and 24 h before forecast demand (R2 = 0.981 and 0.904 for heating degree and cooling degree hours indices, respectively). Models with the highest quality are created using ANN, and the lowest with SVR. Prediction of heating/cooling degree hour indices provides building demand in advance, does not require knowledge about its characteristics, and expresses the possible impact of regional climate modifications. [ABSTRACT FROM AUTHOR]

In the months of March-June, India experiences high daytime temperatures (Tmax), which sometimes lead to heatwave-like conditions over India. In this study, 10 different machine learning models are evaluated for their ability to predict the daily Tmax anomalies 10 days ahead in the months of March-June. Several model experiments were carried out to identify an optimal model to predict daily Tmax anomalies over India. The results indicate that the AdaBoost regressor with Multi-layer Perceptron as the base estimator is an optimal model to predict the Tmax anomalies over India in the months of March-June. The optimal model predictions are benchmarked against 10-day persistence predictions and the predictions from the Climate Forecast System (CFS) reforecast. The results indicate that the machine learning model skill is higher than persistence and comparable to CFS reforecast 10-day predictions in April and May. In March and June, the machine learning models have low skill scores and perform no better than persistence. These results indicate that the machine learning models are promising tools to predict the surface air maximum temperature anomalies over India in April and May and can complement predictions from more sophisticated numerical models. [ABSTRACT FROM AUTHOR]

The colloidal stability of air nanobubbles (NBs) was studied at different temperatures (0–30 °C) and in the presence of sulfates, typically found in mining effluents, in a wide range of Na2SO4 concentrations (0.001 to 1 M), along with the effect of surfactants (sodium dodecyl sulfate), chloride salts (NaCl), and acid/base reagents at a pH range from 4 to 9. Using a nanobubble generator based on hydrodynamic cavitation, 1.2 × 108 bubbles/mL with a typical radius of 84.66 ± 7.88 nm were generated in deionized water. Multiple evidence is provided to prove their presence in suspension, including the Tyndall effect, dynamic light scattering, and nanoparticle size analysis. Zeta potential measurements revealed that NBs are negatively charged even after two months (from − 19.48 ± 1.89 to − 10.13 ± 1.71 mV), suggesting that their stability is due to the negative charge on their surface. NBs were found to be more stable in alkaline solutions compared to acidic ones. Further, low amounts of both chloride and sulfate dissolved salts led to a reduction of the size of NBs. However, when high amounts of dissolved salts are present, NBs are more likely to coalesce, and their size to be increased. Finally, the investigation of the stability of air NBs at low temperatures revealed a non-monotonic relationship between temperature and NBs upon considering water self-ionization and ion mobility. This research aims to open a new frontier towards the application of the highly innovative NBs technology on the treatment of mining, mineral, and metal processing effluents, which are challenging aqueous solutions containing chloride and sulfate species. [ABSTRACT FROM AUTHOR]

Pyrolysis in an inert atmosphere is a widely applied route to convert tannery wastes into reusable materials. In the present study, the Cr(III) conversion into the toxic hexavalent form in the pyrolyzed tannery waste referred to as KEU was investigated. Ageing experiments and leaching tests demonstrated that the Cr(III)–Cr(VI) inter-conversion occurs in the presence of air at ambient temperature, enhanced by wet environmental conditions. Microstructural analysis revealed that the Cr-primary mineral assemblage formed during pyrolysis (Cr-bearing srebrodolskite and Cr-magnetite spinel) destabilized upon spray water cooling in the last stage of the process. In the evolution from the higher to the lower temperature mineralogy, Cr is incorporated into newly formed CrOOH flakes which likely react in air forming extractable Cr(VI) species. This property transforms KEU from an inert waste to a hazardous material when exposed to ordinary ambient conditions. [ABSTRACT FROM AUTHOR]

A comprehensive assessment of the spatial and temporal patterns of the most common indicators of climate change and variability in the Arab world in the past four decades was carried out. Monthly maximum and minimum air temperature and precipitation amount data for the period 1980–2018 were obtained from the CHELSA project with a resolution of 1 km2, which is suitable for detecting local geographic variations in climatic patterns. This data was analyzed using a seasonal-Kendall metric, followed by Sen's slope analysis. The findings indicate that almost all areas of the Arab world are getting hotter. Maximum air temperatures increased by magnitudes varying from 0.027 to 0.714 °C/decade with a mean of 0.318 °C/decade while minimum air temperatures increased by magnitudes varying from 0.030 to 0.800 °C/decade with a mean of 0.356 °C/decade. Most of the Arab world did not exhibit clear increasing or decreasing precipitation trends. The remaining areas showed either decreasing or increasing precipitation trends. Decreasing trends varied from −0.001 to −1.825 kg m−2/decade with a mean of −0.163 kg m−2/decade, while increasing trends varied from 0.001 to 4.286 kg m−2/decade with a mean of 0.366 kg m−2/decade. We also analyzed country-wise data and identified areas of most vulnerability in the Arab world. [ABSTRACT FROM AUTHOR]

Ganoderma lucidum is a promising medicine with a high amount of antioxidants and calcium. The selection of appropriate drying process methods in food science has a chief role to reach the best final characteristics. This study aimed to investigate the effects of air velocity and temperature in the convective dryer, sample distance, and infrared power in infrared dryers on the drying kinetics and quality of Ganoderma lucidum slices. In addition, Response Surface Methodology based on central composition design was used to optimize and analyze drying conditions. The ranges of temperature and air velocity were 40–60 °C and 0.5–1.5 m/s, respectively in the convective drying process while the range of distance and infrared power was 4–16 cm and 500–1500 W, respectively in the infrared drying process. It is worth mentioning that antioxidant and calcium contents were greatly enhanced during the drying procedures. Moreover, the values of the total color difference ranged between 8.21 and 19.66 for the convective dryer and 8.14 and 28.85 for the infrared dryer. A kinetic study indicated that dried samples by the infrared dryer could rapidly reach equilibrium moisture content due to exposure to IR radiation. Consequently, the results indicated that the infrared dryer has better performance than the convective dryer regarding drying time, energy consumption, and amount of calcium and antioxidant. [ABSTRACT FROM AUTHOR]

In the outdoor environment of Qena city, located in southern Egypt, 222Rn concentrations were continuously measured from January 2015 to December 2015 using Alpha-Guard radon monitor, PQ 2000 PRO. Meteorological parameters (air temperature AT, relative humidity RH, and evaporation rate EV) and gaseous pollutants [ozone O3, nitrogen oxide NOx, and particulate matter that has a diameter of less than 10 µm (PM10)] data for the same period were collected from the meteorological station. This work aims to study the potential relationships between 222Rn and each of the meteorological parameters and gaseous pollutants. According to observational data, the yearly average concentrations of 222Rn were 19.35 ± 1.58 Bq/m3, 31.68 ± 1.97 µg/m3 for O3, 9.97 ± 2.27 µg/m3 for NOx, and 109.95 ± 6.48 µg/m3 for PM10. The concentration of 222Rn is positively correlated with relative humidity, nitrogen oxide, and particulate matter, whereas it is negatively correlated with air temperature, evaporation rate, and ozone. Descriptive statistical analysis revealed various distribution patterns for 222Rn, meteorological parameters, and gaseous pollutants. [ABSTRACT FROM AUTHOR]

Accurate estimates of current and future habitat suitability are needed for species that may require assistance in tracking a shifting climate. Standard species distribution models (SDMs) based on occurrence data are the most common approach for evaluating climatic suitability, but these may suffer from inaccuracies stemming from disequilibrium dynamics and/or an inability to identify suitable climate regions that have no analogues within the current range. An alternative approach is to test performance with experimental introductions, and model suitability from the empirical results. We used this method with the Haleakalā silversword (Argyroxiphium sandwicense subsp. macrocephalum), using a network of out-plant plots across the top of Haleakalā volcano, Hawaiʻi. Over a ~ 5-year period, survival varied strongly across this network and was effectively explained by a simple model including mean rainfall and air temperature. We then applied this model to estimate current climatic suitability for restoration or translocation activities, to define trends in suitability over the past three decades, and to project future suitability through 2051. This empirical approach indicated that much of the current range has low suitability for long-term successful restoration, but also identified areas of high climatic suitability in a region where plants do not currently occur. These patterns contrast strongly with projections obtained with a standard SDM, which predicted continued suitability throughout the current range. Under continued climatic shifts, these results caution against the common SDM presumption of equilibrium between species' distributions and their environment, even for long-established native species. [ABSTRACT FROM AUTHOR]

This study evaluates the validity of forecasting air temperature ranges in 2100 using the SimCLIM climate projection model at spatial and temporal scales within the Southern Levantine basin. The model utilized historical air temperature data from 2000 to 2016, collected at seven southeastern Mediterranean stations, as well as 74 climate pattern ensembles integrated within SimCLIM. A combination of 40 global climate models (GCMs) and IPCC AR5 greenhouse gas emissions scenarios embedded in SimCLIM was employed to forecast mean, minimum, and maximum temperatures for 2100.The findings reveal that the average temperature increase in 2100, relative to the representative concentration pathways 2.6, 4.5, 6.0, and 8.5, will range between 0.8–1.17 °C, 1.48–2.0 °C, 2.1–3.8 °C, and 3.9–4.6 °C, respectively. Due to its acceptable accuracy, the SimCLIM model, incorporating 40 GCMs and 74 climate pattern ensembles, is highly recommended for forecasting future climate conditions. The model was evaluated using available temperature records in the study area, yielding a prediction percentage error of 2%, which strongly supports the use of SimCLIM. [ABSTRACT FROM AUTHOR]

Lemongrass is a fragrant herb with lengthy, thin leaves that contains myrcene (an aromatic compound) as well as citral and geraniol (antimicrobial compounds). Therefore, identifying an appropriate drying method for this plant is crucial for maintaining aromatic and antimicrobial compounds and enhancing the shelf life of the product. This investigation seeks to assess the influence of various drying tactics involving hot air at temperatures of 40, 50, and 60 °C, infrared radiation at intensities of 0.5, 0.6, and 0.8 W / cm 2 , sequential hot-air/infrared, as well as simultaneous hot air-infrared, on the drying mechanism, color, appearance, yield, and essential oil constituents of lemongrass leaves, with the objective of enhancing the marketability of the product. The essential oils of lemongrass were extracted through the process of hydro-distillation, and subsequently, the volatile compounds present were analyzed using Gas Chromatography—Mass Spectrometry (GC–MS). The findings indicated: (a) The most appropriate technique for preserving optimal color quality of lemongrass leaves was through the application of hot air drying solely at a temperature of 60 °C; (b) To optimize the retention and amplification of the essential oil content in lemongrass, our study recommends the employment of a simultaneous hybrid drying technique involving hot air drying at a temperature of 50 °C in conjunction with infrared drying set at a radiation intensity level of 0.6 W / cm 2 ; and (c) The data analysis demonstrated that in order to achieve elevated levels of volatile compounds, specifically neral and geranial, infrared drying with a radiation intensity of 0.6 and 0.8 W / cm 2 , respectively, was found to be optimal. [ABSTRACT FROM AUTHOR]

In this study, we aimed to investigate the relationship between out-of-hospital natural death (OHND) and ambient temperature and examine the seriousness of the impact of the coronavirus disease-2019 (COVID-19) pandemic on this relationship. We used data from the Osaka Prefectural Office of Medical Examiners between 2018 and 2022 and performed a retrospective observational study. A Poisson regression model was applied to examine the relationship between OHND and temperature in Osaka City. The relative risk of OHND at 5 °C and 32 °C compared to the minimum mortality temperature increased from 1.81 in the pre-COVID-19 period to 2.03 in the post-COVID-19 period at 5 °C and from 1.29 in the pre-COVID-19 period to 1.60 in the post-COVID-19 period at 32 °C. The increase in relative risk per 1 °C increase from the pre- to post-COVID-19 period was 1.0551 (rate ratio [RR], p = 0.003) in the hot environment and 1.0233 (RR, p = 0.013) in the cold environment, which was larger than that in the hot environment. Although the risk of OHND increased at both temperatures, the change in OHND risk during post-COVID-19 was larger in the hot environment than in the cold environment, implicating the effect of pandemics in the current scenario of global warming. [ABSTRACT FROM AUTHOR]

The present research shows the application of Taguchi's design of experiment approach to optimize the process parameters for the removal of phenol onto surface of Saccharum officinarum biomass activated carbon (SBAC) from an aqueous solution to maximize adsorption capacity of SBAC. The effect of adsorption parameters viz. adsorbent dose (m), temperature (T), initial concentration (C0) and mixing time (t) on response characteristics i.e., adsorption capacity (qt) has been studied at three levels by using L9 orthogonal array (OA) which further analyzed by variance analysis (ANOVA) for adsorption data and signal/noise (S/N) ratio data by using 'larger the better' characteristics. Using ANOVA, the optimum parameters are found to be m = 2 g/L, C0 = 150 mg/L, T = 313 K and t = 90 min, resulting in a maximum adsorption capacity of 64.59 mg/g. Adopting ANOVA, the percentage contribution of each process parameter in descending order of sequence is adsorbent dose 59.97% > initial phenol concentration 31.70% > contact time 4.28% > temperature 4.04%. The phenol adsorption onto SBAC was best fitted with the pseudo-second-order kinetic model and follows the Radke-Prausnitz isotherm model. Thermodynamic parameters suggested a spontaneous, exothermic nature and the adsorption process approaches physisorption followed by chemisorption. Hence the application of Taguchi orthogonal array design is a cost-effective and time-efficient approach for carrying out experiments and optimizing procedures for adsorption of phenol and improve the adsorption capacity of SBAC. [ABSTRACT FROM AUTHOR]

Low-cost particulate matter (PM) sensors have been widely used following recent sensor-technology advancements; however, inherent limitations of low-cost monitors (LCMs), which operate based on light scattering without an air-conditioning function, still restrict their applicability. We propose a regional calibration of LCMs using a multivariate Tobit model with historical weather and air quality data to improve the accuracy of ambient air monitoring, which is highly dependent on meteorological conditions, local climate, and regional PM properties. Weather observations and PM2.5 (fine inhalable particles with diameters ≤ 2.5 μm) concentrations from two regions in Korea, Incheon and Jeju, and one in Singapore were used as training data to build a visibility-based calibration model. To validate the model, field measurements were conducted by an LCM in Jeju and Singapore, where R2 and the error after applying the model in Jeju improved (from 0.85 to 0.88) and reduced by 44% (from 8.4 to 4.7 μg m−3), respectively. The results demonstrated that regional calibration involving air temperature, relative humidity, and other local climate parameters can efficiently correct the bias of the sensor. Our findings suggest that the proposed post-processing using the Tobit model with regional weather and air quality data enhances the applicability of LCMs. [ABSTRACT FROM AUTHOR]

Intra-Annual Density Fluctuations (IADFs) are an important wood functional trait that determine trees' ability to adapt to climatic changes. Here, we use a large tree-ring database of 11 species from 89 sites across eight European countries, covering a climatic gradient from the Mediterranean to northern Europe, to analyze how climate variations drive IADF formation. We found that IADF occurrence increases nonlinearly with ring width in both gymnosperms and angiosperms and decreases with altitude and age. Recently recorded higher mean annual temperatures facilitate the formation of IADFs in almost all the studied species. Precipitation plays a significant role in inducing IADFs in species that exhibit drought tolerance capability, and a growth pattern known as bimodal growth. Our findings suggest that species with bimodal growth patterns growing in western and southern Europe will form IADFs more frequently, as an adaptation to increasing temperatures and droughts. [ABSTRACT FROM AUTHOR]

Recently, ammonia (NH3), which has a higher energy density than hydrogen, has gained attention for zero-carbon emission goals in the transportation sector. However, in a conventional internal combustion engine (ICE), NH3 combustion mechanism is still under investigation. In this paper, to further expand the knowledge on the adoption of NH3 in ICEs, authors conducted NH3/gasoline co-combustion experiments in a modified, 17.7:1 compression ratio, naturally aspirated spark-assisted CI engine with sub-chamber. The sub-chamber was chosen in order to enhance the combustion speed of NH3. In addition, the sub-chamber was equipped with glow and spark plugs to overcome the high auto-ignition temperature of NH3. Engine performance and NOX emissions were studied under three different intake air temperatures. During the experiments, NH3 content was increased gradually where the engine was run under lean conditions. Although higher NH3 content was achieved compared to our previous work, increasing the intake air temperature resulted in decreased charging efficiency. In addition, corrosion was found on the piston ring after 120 h of operation, negatively affecting the engine performance. Furthermore, NH3/gasoline co-combustion duration was shortened drastically with the influence of the sub-chamber, where the longest combustion duration under the present conditions was found to be 17°CA. [ABSTRACT FROM AUTHOR]

In this paper, the removal efficiency of Cerium (Ce(ΙΙΙ)) and lanthanum (La(ΙΙΙ)) ions from aqueous solution using Amberlite XAD-7 resin impregnated with DEHPA(XAD7-DEHPA) was studied in the batch system. The adsorbent (XAD7–DEHPA) was characterized by SEM–EDX, FTIR and BET analysis Techniques. The response surface methodology based on the central composite design was applied to model and optimize the removal process and evaluate operating parameters like adsorbent dose (0.05–0.065), initial pH (2–6) and temperature (15–55). Variance analysis showed that the adsorbent dose, pH and temperature were the most effective parameters in the adsorption of Ce(ΙIΙ)and La(IΙI) respectively. The results showed that the optimum adsorption condition was achieved at pH = 6, the optimum amount of absorbent and the equilibrium time equal to 0.6 gr and 180 min, respectively. According to the results, the adsorption percentage of Ce(ΙIΙ) and La(ΙΙΙ) ions onto the aforementioned resin were 99.99% and 78.76% respectively. Langmuir, Freundlich, Temkin and sips isotherm models were applied to describe the equilibrium data. From the results, Langmuir isotherm (R2 (Ce) = 0.999, R2 (La) = 0.998) was found to better correlate the experimental rate data. The maximum adsorption capacity of the adsorbent (XAD7–DEHPA) for both Ce(IΙI) and La(III) was found to be 8.28 and 5.52 mg g−1 respectively. The kinetic data were fitted to pseudo-first-order, pseudo-second-order and Intra particle diffusion models. Based on the results, the pseudo-first-order model and Intra particle diffusion model described the experimental data as well. In general, the results showed that (XAD7–DEHPA) resin is an effective adsorbent for the removal of Ce(IΙI) and La(III) ions from aqueous solutions due to its high ability to selectively remove these metals as well as its reusability. [ABSTRACT FROM AUTHOR]

Most subterranean habitats, especially caves, are considered extreme environments, mainly because of the limited and erratic food supply and constant darkness. In temperate regions, many climatic conditions, such as temperature and air humidity, are periodically less adverse or even more favourable in caves than the harsh seasonal weather on the surface. Accordingly, many animal species search for hibernacula in caves. These overwintering, non-specialized subterranean species (non-troglobionts) show various modes of dormancy and ongoing development. Since they do not feed, they all undergo periodic starvation, a preadaptation, which might evolve in permanent starvation hardiness, such as found in most specialized subterranean species (troglobionts). To this end, we performed a comparative analysis of energy-supplying compounds in eleven most common terrestrial non-troglobiont species during winter in central European caves. We found highly heterogeneous responses to starvation, which are rather consistent with the degree of energetic adaptation to the habitat than to overwintering mode. The consumption of energy-supplying compounds was strongly higher taxa-dependant; glycogen is the main energy store in gastropods, lipids in insects, and arachnids rely on both reserve compounds. We assume that permanent starvation hardiness in specialized subterranean species might evolved in many different ways as shown in this study. [ABSTRACT FROM AUTHOR]

Lizards are considered vulnerable to climate change because many operate near their thermal maxima. Exposure to higher temperatures could reduce activity of these animals by forcing them to shelter in thermal refugia for prolonged periods to avoid exceeding lethal limits. While rising temperatures should reduce activity in tropical species, the situation is less clear for temperate-zone species where activity can be constrained by both low and high temperatures. Here, we measure the effects of natural variation in environmental temperatures on activity in a temperate grassland lizard and show that it is operating near its upper thermal limit in summer even when sheltering in thermal refuges. As air temperatures increased above 32 °C, lizard activity declined markedly as individuals sought refuge in cool microhabitats while still incurring substantial metabolic costs. We estimate that warming over the last two decades has required these lizards to increase their energy intake up to 40% to offset metabolic losses caused by rising temperatures. Our results show that recent increases in temperature are sufficient to exceed the thermal and metabolic limits of temperate-zone grassland lizards. Extended periods of high temperatures could place natural populations of ectotherms under significantly increased environmental stress and contribute to population declines and extinction. [ABSTRACT FROM AUTHOR]

Warming has been and is being enhanced at high latitudes or high elevations, whereas the quantitative estimation for warming from altitude and latitude effects has not been systematically investigated over Antarctic Ice Sheet, which covers more than 27 degrees of latitude and 4000 m altitude ranges. Based on the monthly surface air temperature data (1958–2020) from ERA5 reanalysis, this work aims to explore whether elevation-dependent warming (EDW) and latitude-dependent warming (LDW) exist. Results show that both EDW and LDW have the cooperative effect on Antarctic warming, and the magnitude of EDW is stronger than LDW. The negative EDW appears between 250 m and 2500 m except winter, and is strongest in autumn. The negative LDW occurs between 83 °S and 90 °S except in summer. Moreover, the surface downward long-wave radiation that related to the specific humidity, total cloud cover and cloud base height is a major contributor to the EDW over Antarctica. Further research on EDW and LDW should be anticipated to explore the future Antarctic amplification under different emission scenarios. [ABSTRACT FROM AUTHOR]

Present knowledge on spawning seasonality of freshwater fishes in tropical Asia and their relationship with environmental factors remains limited. Three Southeast Asian Cypriniformes fishes, Lobocheilos ovalis, Rasbora argyrotaenia and Tor Tambra, found in rainforest streams in Brunei Darussalam were studied on a monthly basis for a period of 2 years. To assess spawning characteristics, seasonality, gonadosomatic index and reproductive phases were examined from 621 L. ovalis, 507 R. argyrotaenia and 138 T. tambra. This study also examined environmental factors such as rainfall, air temperature, photoperiod and lunar illumination that may influence the timing of spawning of these species. We found that L. ovalis, R. argyrotaenia and T. tambra were reproductively active throughout the year but did not find that spawning in these species were associated with any of the investigated environmental factors. Our study showed that the non-seasonal reproductive ecology found in the tropical cypriniform species is distinctly different from that of temperate cypriniforms, which are known to follow spawning seasonality, suggesting an evolutionary adaptation to ensure their survival in an unstable environment. The reproductive strategy and ecological responses found in the tropical cypriniforms might be shifted in response to climate change scenarios in the future. [ABSTRACT FROM AUTHOR]

Overwintering is a critical part of the annual cycle for species that live in temperate, polar, and alpine regions. Consequently, low-temperature biology is a key determinant of temperate species distribution. Termites are distributed predominantly in tropical regions, and a limited number of species are found in the temperate zone. Here, in the termite Reticulitermes speratus, we report the discovery of an underground chamber that protects kings and queens to survive the winter, which is separate from the one they used during the warmer breeding season. In the spring, the royals inhabited decayed logs on the ground, then moved to their underground chamber located in the roots of stumps in the fall. The winter minimum temperature measured in the royal chamber was higher than that in the logs on the ground. In overwintering termites, the kings and queens had higher cold tolerance than workers and soldiers. Air temperatures dropped below the critical temperature multiple times, as evidenced from the past 140 years of weather records in Kyoto. These results demonstrated the survival strategies of reproductives to overcome the environment at the latitudinal limits. This study helps further the understanding of the termite's seasonal phenology, long-term survivorship, and life cycle. [ABSTRACT FROM AUTHOR]

The present study tests the accuracy of four models in estimating the hourly air temperatures in different agroecological regions of the country during two major crop seasons, kharif and rabi, by taking daily maximum and minimum temperatures as input. These methods that are being used in different crop growth simulation models were selected from the literature. To adjust the biases of estimated hourly temperature, three bias correction methods (Linear regression, Linear scaling and Quantile mapping) were used. When compared with the observed data, the estimated hourly temperature, after bias correction, is reasonably close to the observed during both kharif and rabi seasons. The bias-corrected Soygro model exhibited its good performance at 14 locations, followed by the WAVE model and Temperature models at 8 and 6 locations, respectively during the kharif season. In the case of rabi season, the bias-corrected Temperature model appears to be accurate at more locations (21), followed by WAVE and Soygro models at 4 and 2 locations, respectively. The pooled data analysis showed the least error between estimated (uncorrected and bias-corrected) and observed hourly temperature from 04 to 08 h during kharif season while it was 03 to 08 h during the rabi season. The results of the present study indicated that Soygro and Temperature models estimated hourly temperature with better accuracy at a majority of the locations situated in the agroecological regions representing different climates and soil types. Though the WAVE model worked well at some of the locations, estimation by the PL model was not up to the mark in both kharif and rabi seasons. Hence, Soygro and Temperature models can be used to estimate hourly temperature data during both kharif and rabi seasons, after the bias correction by the Linear Regression method. We believe that the application of the study would facilitate the usage of hourly temperature data instead of daily data which in turn improves the precision in predicting phenological events and bud dormancy breaks, chilling hour requirement etc. [ABSTRACT FROM AUTHOR]

The Aedes aegypti mosquito is the main vector of dengue and is a synanthropic insect and due to its anthropophilic nature, it has specific reproductive needs. In addition to that, it also needs tropical regions that provide climate-prone conditions that favor vector development. In this article, we propose the cross-correlation analysis between the climatic variables air temperature, relative humidity, weekly average precipitation and dengue cases in the period from 2017 to early 2021 in the municipality of Alagoinhas, Bahia, Brazil. To do so, we apply the trend-free cross-correlation, ρ D C C A , being a generalization of the fluctuation analysis without trend, where we calculate the cross correlation between time series to establish the influence of these variables on the occurrence of dengue disease. The results obtained here were a moderate correlation between relative humidity and the incidence of dengue cases, and a low correlation for relative air temperature and precipitation. However, the predominant factor in the incidence of dengue cases in the city of Alagoinhas is relative humidity and not air temperature and precipitation. [ABSTRACT FROM AUTHOR]

The climate is changing. At this stage, it is important to specify an 'extreme' climate and identify patterns that indicate its potential harm worldwide, including the coastal zones. Herein, we considered extremes based on the "Peaks Over Threshold" method from the "Extreme Value Theory". We looked after geographical patterns of surface air temperature (SAT) extremes (e.g., Tmax, Tmin, daily temperature range (DTR), and inter-daily temperature range) over the last 40 years throughout the Brazilian coast. Overall, we found a trend increase in intensity and frequency, but the duration was barely affected. The latitudinal pattern of extremes and the temperatures considered extremes followed the settled perception that areas in higher latitudes will be more affected by the extent of warming. Additionally, the seasonal pattern of DTR demonstrated to be a good approach to make inferences about air mass changes, but joint analyses on extremes with other atmospheric variables are desirable. Given the potential effects of extreme climates on society and natural systems over the world, our study highlights the urge for action to mitigate the effects of the increase in SAT in coastal zones. [ABSTRACT FROM AUTHOR]

Hibernation is one of the most important behaviours of bats of the temperate zone. During winter, when little food or liquid water is available, hibernation in torpor lowers metabolic costs. However, the timing of emergence from hibernation is crucial for the resumption of the reproductive process in spring. Here, we investigate the spring emergence of six bat species or pairs of bat species of the genera Myotis and Plecotus at five hibernation sites in Central Europe over 5 years. Using generalized additive Poisson models (GAPMs), we analyze the influence of weather conditions (air and soil temperature, atmospheric pressure, atmospheric pressure trend, rain, wind, and cloud cover) as predictors of bat activity and separate these extrinsic triggers from residual motivation to emerge from hibernation (extrinsic factors not studied; intrinsic motivation). Although bats in a subterranean hibernaculum are more or less cut off from the outside world, all species showed weather dependence, albeit to varying degrees, with air temperature outside the hibernaculum having a significant positive effect in all species. The residual, potentially intrinsic motivation of species to emerge from their hibernacula corresponds to their general ecological adaptation, such as trophic specialization and roosting preferences. It allows the definition of three functional groups (high, medium and low residual activity groups) according to the degree of weather dependence of spring activity. A better knowledge of the interplay of extrinsic triggers and residual motivation (e.g., internal zeitgebers) for spring emergence will help to understand the flexibility of a species to adapt to a changing world. [ABSTRACT FROM AUTHOR]

Accurate estimation of evaporation is of great significance for understanding regional drought, and managing and applying limited water resources in dryland. However, the application of the traditional estimation approaches is limited due to the lack of required meteorological parameters or experimental conditions. In this study, a novel hybrid model was proposed to estimate the monthly pan Ep in dryland by integrating long short-term memory (LSTM) with grey wolf optimizer (GWO) algorithm and Kendall-τ correlation coefficient, where the GWO algorithm was employed to find the optimal hyper-parameters of LSTM, and Kendall-τ correlation coefficient was used to determine the input combination of meteorological variables. The model performance was compared to the performance of other methods based on the evaluation metrics, including root mean squared error (RMSE), the normalized mean squared error (NMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE), and Nash–Sutcliffe coefficient of efficiency (NSCE). The results indicated that the optimal input meteorological parameters of the hybrid Kendall-τ-GWO-LSTM models are the monthly average temperature, the minimum air temperature, the maximum air temperature, the minimum values of RMSE, NMSE, MAE, and MAPE are 38.28, 0.20, 26.62, and 19.96%, and the maximum NSCE is 0.89, suggesting that the hybrid Kendall-τ-GWO-LSTM exhibit better model performance than the other hybrid models. Thus, the hybrid Kendall-τ-GWO-LSTM model was highly recommended for estimating pan Ep with limited meteorological information in dryland. The present investigation provides a novel method to estimate the monthly pan Ep with limited meteorological variables in dryland by coupling a deep learning model with meta-heuristic algorithms and the data preprocessing techniques. [ABSTRACT FROM AUTHOR]

In this study, cellulose hydrogels were simply fabricated by the chemical dissolution method using LiCl/dimethylacetamide as a new method, and the hydrogel produced was investigated for removing Direct Blue 86 (DB86) dye from the aquatic environment. The produced cellulose hydrogel (CAH) was characterized by FTIR, XRD, SEM, and TGA analyses. The removal efficiency of DB86 dye using CAH was achieved via a batch equilibrium process. The impact of pH, time of contact, CAH dosage, starting concentration of DB86 dye, and absorption temperature were scanned. The optimum pH for absorption of DB86 dye was determined to be 2. The absorption results obtained were scanned by Langmuir (LIM), Temkin (TIM), Freundlich (FIM), and Dubinin-Radushkevich (DRIM) isotherm models (IMs) and chi-square error (X2) function used to identify the best-fit IMs. The CAH had 53.76 mg/g as a maximum absorption capacity (Qm) calculated from the LIM plot. The TIM was the best fitted to the CAH absorption results. Kinetic absorption results were investigated by pseudo-first-order (PFOM), Elovich (EM), pseudo-second-order (PSOM), film diffusion (FDM), and intraparticle diffusion (IPDM) models. A PSOM with a high R2 (> 0.99) accounted for the majority of the control over the absorption rate. The findings indicate that CAH can potentially remove the DB86 dye from wastewater. [ABSTRACT FROM AUTHOR]

Taking into account that the transport of grains can be carried out over long distances and that the mass of grains during transport often has high moisture content, there may be risks of heat and moisture transfer and heating of the grains mass, proving quanti-qualitative losses. Thus, this study aimed to validate a method with probe system for real-time monitoring of temperature, relative humidity and carbon dioxide in the grain mass of corn during transport and storage to detect early dry matter losses and predict possible changes on the grain physical quality. The equipment consisted of a microcontroller, system's hardware, digital sensors to detect air temperature and relative humidity, a non-destructive infrared sensor to detect CO2 concentration. Real-time monitoring system determined early and satisfactorily in an indirect way the changes in the physical quality of the grains confirming by the physical analyses of electrical conductivity and germination. The equipment in real-time monitoring and the application of Machine Learning was effective to predict dry matter loss, due to the high equilibrium moisture content and respiration of the grain mass on the 2-h period. All machine learning models, except support vector machine, obtained satisfactory results, equaling the multiple linear regression analysis. [ABSTRACT FROM AUTHOR]

During foraging honeybees are always endothermic to stay ready for immediate flight and to promote fast exploitation of resources. This means high energetic costs. Since energy turnover of foragers may vary in a broad range, energetic estimations under field conditions have remained uncertain. We developed an advanced model, combining the benefits of mechanistic and correlative models, which enables estimation of the energy turnover of stationary foragers from measurements of body surface temperature, ambient air temperature and global radiation. A comprehensive dataset of simultaneously measured energy turnover (ranging from 4 to 85 mW) and body surface temperature (thorax surface temperature ranging from 33.3 to 45 °C) allowed the direct verification of model accuracy. The model variants enable estimation of the energy turnover of stationary honeybee foragers with high accuracy both in shade and in sunshine, with SD of residuals = 5.7 mW and R2 = 0.89. Its prediction accuracy is similar throughout the main range of environmental conditions foragers usually experience, covering any combination of ambient air temperature of 14–38 °C and global radiation of 3–1000 W m−2. [ABSTRACT FROM AUTHOR]

In this study, Pea Peels-Activated Carbon (PPAC), a novel biochar, was created from leftover pea peels (Pisum sativum) by wet impregnation with ZnCl2 and subsequent heating to 600, 700, and 800 °C in a CO2 atmosphere. Investigated how the newly acquired biochar affected the capacity to extract the AY11 dye from the aqueous solution. Through the use of FTIR, XRD, SEM, BJH, BET, DSC, EDX, and TGA studies, the prepared PPAC was identified. It was found that a pH of 2 is optimum for the AY11 dye elimination. The highest removal percentage of AY11 dye was 99.10% using a beginning AY11 dye concentration of 100 mg/L and a 1.0 g/L dose of PPAC. The highest adsorption capacity (Qm) of the PPAC was 515.46 mg/g. Freundlich (FIM), Halsey (HIM), Langmuir (LIM), Tempkin (TIM), and Gineralize (GIM) isotherm models were useful in examining the adsorption results. A variety of error functions, including the average percent errors (APE), root mean square errors (RMS), Marquardt's percent standard deviation (MPSD), hybrid error function (HYBRID), Chi-square error (X2) and a sum of absolute errors (EABS) equations, were also applied to test the isotherm models data. The PPAC experimental data were best suited by the HIM and FIM isotherm models. Elovich (EM), Pseudo-first-order (PFOM), Intraparticle diffusion (IPDM), Pseudo-second-order (PSOM), and Film diffusion (FDM) models were applied to study the kinetic adsorption results. The PSOM had a strong correlation coefficient (R2 > 0.99), and it was principally responsible for controlling the adsorption rate. Anions are typically absorbed during the adsorption mechanism of AY11 dye by PPAC owing to attractive electrostatic forces created with an increase in positively charged areas at acidic pH levels. The regenerated PPAC was used in six successive adsorption/desorption cycles. This study's outcomes show that PPAC successfully removes the AY11 dye from the aqueous solution; as a result, PPAC can be used repeatedly without experiencing considerable loss in effectiveness. [ABSTRACT FROM AUTHOR]

The quasi-periodic signals in the earth system could be the predictability source for sub-seasonal to seasonal (S2S) climate prediction because of the connections among the lead-lag time of those signals. The Madden–Julian Oscillation (MJO) is a typical quasi-periodic signal, which is the dominant S2S variability in the tropics. Besides, significantly periodic features in terms of both intensity and location are identified in 10–40 days for the concurrent variation of the subtropical and polar jet streams over Asia in this study. So far, those signals contribute less and are not fully applied to the S2S prediction. The deep learning (DL) approach, especially the long-short term memory (LSTM) networks, has the ability to take advantage of the information at the previous time to improve the prediction after then. This study presents the application of the DL in the postprocessing of S2S prediction using quasi-periodic signals predicted by the operational model to improve the prediction of minimum 2-m air temperature over Asia. With the help of deep learning, it finds the best weights for the ensemble predictions, and the quasi-periodic signals in the atmosphere can further benefit the S2S operational prediction. [ABSTRACT FROM AUTHOR]