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Land surface‐atmosphere coupling and soil moisture memory are shown to combine into a distinct temporal pattern for wildfire incidents across the western United States. We investigate the dynamic interplay of observed soil moisture, vegetation water content, and atmospheric dryness in relation to fuel loading, fire ignition and post‐fire recovery. We find that positive soil moisture anomalies around 5 months before fire ignition increase biomass growth in the subsequent months, thereby shaping fire‐prone vegetation conditions. Then, concurrent decrease in soil moisture, vegetation dehydration, and atmospheric dryness collectively contribute to the occurrence of fire ignition events. This is followed by a rapid recovery in both soil and atmospheric moisture within several weeks after the fire incidents. Our findings provide insights into understanding of wildfire ignition dynamics, supporting fire modeling and enabling improved fire predictions, early warning systems, and mitigation strategies. Plain Language Summary: We find that wildfire are part of a distinct temporal pattern of soil moisture, vegetation water content and atmospheric dryness dynamics that begin about 5 months before the incidents. We analyze anomalies in soil moisture, vegetation water content, vapor pressure deficit and precipitation before and after large wildfires during the warm season across various US climate regions. We document distinct patterns in soil aridity and atmospheric dryness, fuel load, and vegetation dehydration that contribute to the occurrence of wildfires. Our observation‐based analyses show that soil moisture anomalies significantly control the growth of biomass several months before the fire occurrence, which in turn affects the availability of fire fuel. Notable dry anomalies with fuel accumulation in the dry western climate regions are observed approximately 4 to 5 months before the fires, followed by a quick recovery trend several weeks after the fire in wet regions. Our results indicate that in the majority of the climate regions, there are significant dry anomalies preceding the occurrence of wildfires. Understanding the dynamics of these environmental variables can be the basis for improved wildfire hazard predictions and mitigation strategies. Key Points: Wetter‐than‐average soil moisture 4–5 months before wildfires results in increased aboveground biomass accumulationSoil moisture decrease and atmospheric drying after peak biomass lead to dry fuel availability at the time of wildfire ignitionThe temporal pattern is evident in mapped data on soil moisture, vegetation water content and wildfire incidents across western United States [ABSTRACT FROM AUTHOR]

Adaptation is key to minimizing heatwaves' societal burden; however, our understanding of adaptation capacity across the socioeconomic spectrum is incomplete. We demonstrate that observed heatwave trends in the past four decades were most pronounced in the lowest‐quartile income region of the world resulting in >40% higher exposure from 2010 to 2019 compared to the highest‐quartile income region. Lower‐income regions have reduced adaptative capacity to warming, which compounds the impacts of higher heatwave exposure. We also show that individual contiguous heatwaves engulfed up to 2.5‐fold larger areas in the recent decade (2010–2019) as compared to the 1980s. Widespread heatwaves can overwhelm the power grid and nullify the electricity dependent adaptation efforts, with significant implications even in regions with higher adaption capacity. Furthermore, we compare projected global heatwave exposure using per‐capita gross domestic product as an indicator of adaptation capacity. Hypothesized rapid adaptation in high‐income regions yields limited changes in heatwave exposure through the 21st century. By contrast, lagged adaptation in the lower‐income region translates to escalating heatwave exposure and increased heat‐stress inequality. The lowest‐quartile income region is expected to experience 1.8‐ to 5‐fold higher heatwave exposure than each higher income region from 2060 to 2069. This inequality escalates by the end of the century, with the lowest‐quartile income region experiencing almost as much heatwave exposure as the three higher income regions combined from 2090 to 2099. Our results highlight the need for global investments in adaptation capabilities of low‐income countries to avoid major climate‐driven human disasters in the 21st century. Plain Language Summary: We show that heatwave exposure has disproportionately increased in the lowest‐income regions globally compared to the highest‐income regions over the past four decades. We also show that emerging heat hazards (e.g., shock heatwaves—the first heatwave of the season—and widespread contiguous extreme heat events) intensified in the past four decades and are expected to increase in the future across the globe, jeopardizing adaptation efforts even in wealthy countries. We use climate projections to evaluate future changes in heat‐stress inequality globally. In doing so, we incorporate the fact that high‐income countries have greater institutional and individual capacity to rapidly adapt to climate change than low‐income countries. Our findings demonstrate continued increases in heatwave exposure inequality because of delays in adaptation capacity in the developing world, compounded by a higher emergence of warming in low‐latitude areas where most of the low‐income countries occur. These results highlight the urgency to scale adaptation efforts in the low‐income regions to minimize heat‐stress inequality. Key Points: Heatwave exposure in the recent decade was >40% higher in the lowest‐quartile income region of the world than the highest‐quartileRapid adaptation capacity in higher‐income regions can buffer climate change‐driven increases in heatwave exposureLagged adaptation in the lower‐income regions translates to escalating impacts and increasing heat‐stress inequality [ABSTRACT FROM AUTHOR]

The article discusses the increasing concerns surrounding floods, fires, and droughts as extreme climate events and the urgent need to improve risk assessment and management abilities. It emphasizes the importance of conducting comprehensive climate risk assessments to develop effective strategies for mitigating the impacts of these disasters, particularly in marginalized and vulnerable communities. The article highlights the importance of both prospective and retrospective risk assessment methods/tools in understanding climate impacts and managing current and future climate change risks. However, it also identifies several major knowledge gaps in the field, such as the lack of locally relevant climate risk indicators and tools, limited understanding of socioeconomic impacts, and the need for standardized metrics and comprehensive consideration of vulnerability. The article calls for further research and the development of innovative tools to increase climate-related risk assessment and management, incorporating participatory community research and leveraging technological advancements. It also emphasizes the importance of cross-sectional collaboration to ensure that climatic risk assessments are incorporated into policy and decision-making processes. [Extracted from the article]

Summary: The Internet of Things (IoT) interconnects billions of physical objects to collect and exchange information and makes available various applications. Despite all the advantages of the IoT, some of its applications are not feasible because of the existing restrictions in the IoT sensors. To overcome these restrictions, cloud computing has been developed in recent years. Although cloud computing has removed some of these restrictions, the cloud itself has faced some other challenges. One of these challenges is the distance between the cloud and the end‐devices, which is not ideal for delay‐sensitive applications and also leads to high communication costs and security problems. To handle these challenges, the fog computing has been introduced in literature, which places the resources and services at the edge of the network, close to the end‐devices. On the other hand, fog nodes suffer from heterogeneity, uncertainty, and dispersion of resources, and also processing, storage, and memory limitations. Thus, there is need for a proper task scheduling approach to use these resources optimally. In this study, the task scheduling algorithms proposed by different researchers for the cloud‐fog environment, their advantages and disadvantages, and also various tools and issues regarding the scheduling methods and their restrictions were discussed. The results indicated that about 58% of the scheduling algorithms use static scheduling and that the other 42% use dynamic scheduling, and the delay metric is the most noteworthy parameter considered in most studies with a share of nearly 17%. Finally, we identified the open issues related to the task scheduling based on cloud‐fog computing and provided some directions for future works. [ABSTRACT FROM AUTHOR]

While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance‐based K‐nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree‐based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., Ca2+,Mg2+,Na+,K+,HCO3−,CO32−,SO42−,andCl− concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na+ and Ca2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources. Article impact statement: This work relates unstructured grid generation to the dynamics of finite‐difference groundwater flow models for the first time. [ABSTRACT FROM AUTHOR]

The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO3) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin‐Shahr plain, Iran. Twenty‐two groundwater samples were collected from wells in the study area, and their respective NO3 concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO3 concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon–GA–DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R2 of 0.22, the Wilcoxon–GA–DRASTIC obtained a maximum R2 value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R2 value to the highest degree. Article impact statement: The results showed that both linear and nonlinear methods are useful in modifying the ratings and weights of the DRASTIC method for assessing the groundwater vulnerability. [ABSTRACT FROM AUTHOR]

In this paper, we report the numerical analysis of a nanostructured photonic crystal fiber for making an efficient dispersion-compensating medium. For our computational studies, the core of the proposed structure is made up of As2Se3, and the cladding structure is a mixture of air holes and holes filled with silica. Our simulations indicate that the chromatic dispersion and the confinement loss at the wavelength of 1.55 µm is −3280 ps/nm/km and 1.71 × 10−6 dB/m respectively. Moreover, the relative dispersion slope at the wavelength of 1.55 µm is computed to be 0.00357 nm−1 that closely matches that of the conventional fibers at the third window. The effect of changing the dimensions of the holes in the first ring and also the distance between the adjacent holes ( Λ) in the same ring on chromatic dispersion are also studied. The presented structure is an efficient dispersion-compensating medium. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Physical, milling and cooking attributes of the main and ratoon crops in three rice cultivars, namely Hashemi, Alikazemi and Tarom, were compared in a factorial layout based on randomized complete block design in five replications. The results revealed that for each tested cultivar, the length and slenderness ratio of the main rice grains was significantly ( P < 0.01) higher than those of the ratoon crop. The means of head rice yield and whiteness degree in the main crop (76.01 and 46.45%, respectively) were significantly different ( P < 0.01) with the corresponding values in the ratoon rice (80.27 and 40.10%, respectively), however, the effect of crop type on the total milled rice yield was not significant. Among the qualities evaluated, the amylase content and alkali spreading value in the main rice crop (21.97% and 4.27, respectively) was significantly lower than those of the ratoon rice (22.93% and 5.50, respectively). Practical Applications Rice quality features such as milling and cooking quality is the main factors for its market acceptability. Since ratoon rice as re-harvested of the main crop can be considered for improving economic status of the farmers because of its low production cost and to encourage the farmers to develop ratoon cultivation in rice fields, it is a need to determine such properties as physical dimensions, head rice yield, milling recovery, whiteness, amylose content, alkali spreading value and other cooking quality as compared to the main rice crop. [ABSTRACT FROM AUTHOR]