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Harmful Algal Blooms (HABs) persist in many water bodies around the world and pose adverse health and economic impacts to the affected communities. Small Unmanned Aerial Vehicles (UAVs) have recently been applied as a cost‐effective tool for HABs monitoring. In this study, HABs in two small lakes in Southern Illinois (Carbondale Reservoir and the Campus Lake of Southern Illinois University) were monitored using UAVs and biomass concentrations in lake waters. By analyzing vegetation indices derived from multispectral UAV images and chlorophyll‐a concentrations in the two lakes, statistical regression models were established for each waterbody. The model relates spectral characteristics of the lake water to its algae biomass. It was found that normalized difference vegetation index (NDVI) and blue‐to‐green band ratio are the best‐fit indices to the variation in chlorophyll‐a in Carbondale Reservoir and the Campus Lake, respectively. The findings in this study can be used for monitoring HABs using UAVs in these lakes in the future. [ABSTRACT FROM AUTHOR]

Inducing endogenous cardiomyocyte proliferation and heart regeneration is a promising strategy to treat ischemic heart failure. The immune response has recently been considered critical in cardiac regeneration. Thus, targeting the immune response is a potent strategy to improve cardiac regeneration and repair after myocardial infarction. Here we reviewed the characteristics of the relationship between the postinjury immune response and heart regenerative capacity and summarized the latest studies focusing on inflammation and heart regeneration to identify potent targets of the immune response and strategies in the immune response to promote cardiac regeneration. • Acute immune response is mandatory for the initiation of cardiac regeneration in neonates. • Transplantation of pro-regenerative immune cells improves the outcome of cardiac repair. • IL-6 family cytokines actively regulate myocardium repair and are potent targets for cardiac regeneration. [ABSTRACT FROM AUTHOR]

With social media ubiquitous in our daily communication, local governments have increasingly relied on this new media platform for communicating and interacting with their citizens. It is still unclear, however, how to assess the effectiveness of social media communication efforts by local governments. Based on the Lasswell communication framework, this study proposes a new indicator, the Social Media Communication Index (SMCI), that can be used to compare and evaluate the degree of social media communication effectiveness among different cities. The index was then applied to the social media platforms used by the top growing U.S. cities. The results show that Orlando and North Charleston exhibit top communication effectiveness in social media. The study culminates in a research finding that the SMCI is positively correlated with the population growth rate and education levels. [ABSTRACT FROM AUTHOR]

Delineating accurate flowlines using digital elevation models is a critical step for overland flow modeling. However, extracting surface flowlines from high‐resolution digital elevation models (HRDEMs) can be biased, partly due to the absence of information on the locations of anthropogenic drainage structures (ADS) such as bridges and culverts. Without the ADS, the roads may act as "digital dams" that prevent accurate delineation of flowlines. However, it is unclear what variables for terrain‐based hydrologic modeling can be used to mitigate the effect of "digital dams." This study assessed the impacts of ADS locations, spatial resolution, depression processing methods, and flow direction algorithms on hydrologic connectivity in an agrarian landscape of Nebraska. The assessment was conducted based on the offset distances between modeled drainage crossings and actual ADS on the road. Results suggested that: (a) stream burning in combination with the D8 or D‐Infinity flow direction algorithm is the best option for modeling surface flowlines from HRDEMs in an agrarian landscape; (b) increasing the HRDEM resolution was found significant for facilitating accurate drainage crossing near ADS locations; and (c) D8 and D‐Infinity flow direction algorithms resulted in similar patterns of drainage crossing at ADS locations. This research is expected to result in improved parameter settings for HRDEMs‐based hydrologic modeling. [ABSTRACT FROM AUTHOR]

Recent success in the many applications of information and communications technologies (ICTs) in urban settings largely defines our understanding of sustainable development in the context of smart societies and communities. The footprints of such smart transformation barely imprint in the countryside, however, where rural communities face tremendous barriers, such as the absence of ICT infrastructure, geographic isolation from technological advances, and various social inequalities. The conventional wisdom of smart societies and communities built on well-established broadband access and ICT applications in urban hubs might not be suitable for decentralized rural regions. We contend that a new form of digital divide in the context of recent ICT advancement—a smart divide—is evolving into a new challenge to smart societies and communities. This study aims to characterize the smart divide, an emerging type of social inequality. To address this aim, we conduct a literature review with a focus on the technological aspects of the smart divide in rural regions and identify potential gaps in smart infrastructure and smart applications and services between urban and rural America. The article culminates in a synthesis of potential strategies for bridging the smart divide in rural America. The study is expected to benefit scientists, planners, entrepreneurs, and policymakers interested in smart rural development. [ABSTRACT FROM AUTHOR]

As a cross-disciplinary scientific domain, a great deal of research on landscape geochemistry has been conducted since its establishment. However, the used methods lack the ability of revealing the relationships of landscape geochemistry with other sciences, and the obtained knowledge is thus fragmented and isolated. In this study, the state-of-the-art regarding the applications of relatively new geospatial technologies including fractal theory, geographic information system and remote sensing to landscape geochemistry was reviewed and analyzed to provide deep insights of current research and a roadmap for furthering the development of landscape geochemistry as a cross-disciplinary discipline. The results showed that substantial research on the applications of fractal theory, GIS and RS technologies for analyzing the processes and data of landscape geochemistry has been conducted by using the advantages of the geospatial technologies. However, the great challenges still exist when the geospatial technologies were individually utilized due to the limitations of the technologies themselves and the complexity of landscape geochemistry. In the end, opportunities and challenges for advancing the further studies of landscape geochemistry were discussed in detail and new directions of studying landscape geochemistry using multidisciplinary or integrated approaches to enhance understanding the relationships among the relevant disciplines were suggested. [ABSTRACT FROM AUTHOR]

Widely blended in the transportation fuels, biofuels have been generally regarded as indispensable components in the U.S. national energy portfolio. Although the current production of cellulosic biofuels has been staggering at pilot scales, it is expected that large-scale cultivation of cellulosic biofuel crops may occur when techniques for cost-effective bioconversion of lignocellulosic biomass to ethanol are available. However, it is still unclear if enough land is in existence to accommodate potential large-scale lignocellulosic crops development without negative effects on the environment and food market. The appropriate land use for the next-generation biofuel crops should be planned and allocated in a manner that the competitive use of land dedicated to food and energy production and other side effects could be minimized. This research proposes an approach of identifying the potential available land for planting switchgrass in the U.S. Midwest using a GIS-based multi-criteria analysis that combines soil and water constraint factors. The results showed that land areas of approximately 13.6 million ha are unsuitable for profitable food crops but available for switchgrass cultivation in 7 U.S. Great Plains states. The findings can assist decision-makers in formulating land use policies and related environmental management strategies. [ABSTRACT FROM AUTHOR]

Determination of the nature and extent of the connection between groundwater and surface water is of paramount importance to managing water supplies. The development of analyses that detail the surface water-groundwater system may lead to more effective utilization of available water. A tool was developed to help determine the effects of groundwater and surface water interactions. The software tool includes two graphic user interfaces to allow full compatibility with numerical MODFLOW groundwater models. This case study shows the tool, in conjunction with MODFLOW groundwater models and carefully designed scenarios, can successfully calculate the rates of stream-groundwater interactions, thereby providing the basis for designating management areas with the most significant hydrologic impact. This tool can be applied in other regions with similar settings and needs for integrated water management. [ABSTRACT FROM AUTHOR]

Summary The conventional numerical method is computationally intensive and prone to numerical noises for stream depletion analyses using MODFLOW. In this study, a new MODFLOW package has been developed to improve the computational efficiency and reduce the noises for each simulation. Using the assumption of unchanged flow coefficients between the baseline and scenario runs, the nonlinear groundwater flow system is linearized for solving the flow equations. The new package has been successfully applied to a regional groundwater model in Nebraska. The results show that the numerical noises, commonly identified in conventional approach, have been significantly reduced and a twenty-fold speedup has been achieved for a regional groundwater model in Nebraska. The results suggest this package can be adapted to be a component of optimization tools for water management scenario analyses especially when a large number of scenario model runs are involved. [ABSTRACT FROM AUTHOR]

The digital elevation model data from traditional stereo photogrammetric methods are inadequate in providing accurate vertical parameters to feed hydrologic models for low-lying, extremely flat areas. High-resolution light detection and ranging (Li DAR) data provide the robust capability of capturing small variations in low-relief playa wetlands. The Rainwater Basin in south-central Nebraska includes a complex of seasonally shallow playa wetlands that attract millions of migratory waterfowl every spring and fall. This research focuses on the development of a procedure with applicable protocols to produce Li DAR-derived three-dimensional wetland maps and to extract the critical surface parameters (i.e., watershed boundaries, flow direction, flow accumulation, and drainage lines) for playa wetlands. The topo-hydrologic conditions of playa wetlands were evaluated at the watershed level. The results show that in the Rainwater Basin, 70.7% of the historic hydric soil footprints identified in the Soil Survey Geographic ( SSURGO) database were not functioning as topographically depressional wetlands. This finding was confirmed by a recent five-year Annual Habit Survey showing that 69.8% of the historic hydric soil footprints did not function during the spring migratory bird seasons between 2004 and 2009. The majority of playa wetlands' topographic conditions have been substantially changed and the SSURGO data cannot fully reflect current topographic reality in the Rainwater Basin. [ABSTRACT FROM AUTHOR]

The biofuel-related land use in the USA and the EU has significantly expanded during the last decade; models have been used to estimate land availability and demand in these regions. This paper provides an overview of different land-use modeling practices applicable to first- and second-generation biofuels. We review the importance of different land categories for biofuels, modeling approaches (top-down/bottom-up) and their integration, data availability for calibration and validation, model scale, and uncertainty. Possible future changes of biofuel land use and research gaps and limitations are synthesized. Key issues are the lack of data for independent validation and the need for better integration of dynamic bottom-up models into the top-down policy analysis. More research is needed to deal with the large-scale introduction of second-generation biofuel crops required. The paper culminates in describing how models can help to meet the challenge of supplying more fuel from lignocellulosic crops ( LCC) in ways that reduce indirect land-use change ( iLUC) and how such transition could be implemented in policy and practice. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd [ABSTRACT FROM AUTHOR]

Abstract: Modeling groundwater vulnerability to pollution is critical for implementing programs to protect groundwater quality. Most groundwater vulnerability modeling has been based on current hydrogeology and land use conditions. However, groundwater vulnerability is strongly dependent on factors such as depth-to-water, recharge and land use conditions that may change in response to future changes in climate and/or socio-economic conditions. In this research, a modeling framework, which employs three sets of models linked within a geographic information system (GIS) environment, was used to evaluate groundwater pollution risks under future climate and land use changes in North Dakota. The results showed that areas with high vulnerability will expand northward and/or northwestward in Eastern North Dakota under different scenarios. GIS-based models that account for future changes in climate and land use can help decision-makers identify potential future threats to groundwater quality and take early steps to protect this critical resource. [Copyright &y& Elsevier]

Abstract: With the increasing biofuel demands in recent years, the cultivated lands for biofuel crops, i.e., corn and soybeans, the major sources of ethanol and biodiesel, have been greatly expanding in the northern Great Plains states of the United States. Simulating the spatio-temporal dynamics of biofuel croplands can provide critical information required for assessing the impacts of land-use change on wildlife conservation and water quality. But, yearly agricultural practices such as crop rotations often complicate the spatially explicit modeling of specific crops’ expansion. Our research focused on developing a geospatial modeling framework that is able to distinguish long-term, regional changes in croplands from short-term, local fluctuations (such as rotations), using geographic information systems (GIS) and the land transformation model (LTM). The USDA Cropland Data Layers (CDLs) of North Dakota for 1999, 2000, 2004, 2005, 2010, and 2011 were spatially and temporally aggregated to generate a series of biofuel cropland maps. The historical cropland data for 1999/2000 and 2004/2005, together with a collection of environmental factors (i.e., topography, soil fertility, and climate), were used to calibrate the neural network embedded in the LTM. Validation analysis was then conducted by simulating the biofuel cropland change during the period of 2004/2005–2010/2011 using the calibrated LTM and comparing the simulation result with the observed change for the same time period, resulting in 6.3% allocation disagreement (0% quantity disagreement) and 27.4% figure of merit. Future forecast for 2020 showed that biofuel croplands would be expanding northwestward from southeastern North Dakota. [Copyright &y& Elsevier]

• A hidden Markov model with the monitoring data of hydro-turbine runner cracks from actual operations is proposed for the life cycle assessment (LCA). • The new maintenance interval is formulated and the inspection times are defined based on LCA. • The optimal maintenance plans are developed by analyzing the influence of cracks being in different states leading to a hazardous condition. Life cycle assessment (LCA) has been an emerging feature of modern structural health monitoring techniques that aims at evaluating equipment degradation process and it can be used for development of dynamic maintenance plans. A novel framework based on LCA is proposed in this paper for dynamic maintenance planning of hydro-turbines. Using a Hidden Markov Model (HMM) and the inspection data of hydro-turbine runner cracks from actual operations, the transition probability matrix and sojourn time of different crack states are obtained. This is utilized for predicting the expected remaining useful life (RUL) and the formulation of maintenance intervals. Moreover, maintenance plans are developed from the influence of cracks in different states leading to critical conditions. The proposed method, as demonstrated in the case study, efficiently updates the maintenance intervals and reduces the likelihood of failure events. The results of this paper provide a valid maintenance framework for achieving higher operational safety of hydropower stations with minimal resource losses. [ABSTRACT FROM AUTHOR]

Dependence on groundwater throughout the Central Valley of California has resulted in severe and unsustainable groundwater depletion. In this research, we used the Central Valley hydrologic model (CVHM) to model how crop types and drought scenarios impact groundwater storage throughout the Central Valley. Simulations that decreased the abundance of water-intensive crops, such as fruits and nuts, observed the largest recovery in groundwater storage. Two drought scenarios, which respectively decreased surface water availability by 25% and 75%, were also tested. Combined drought and crop type scenarios were explored to understand the relative influence of either surface condition on groundwater storage. The results showed that water-intensive crops can decrease groundwater storage by over 20 m under water deficit conditions, whereas water-saving crops can recover groundwater storage and alleviate the impact of droughts. This research suggests that under drought conditions, groundwater storage will decline substantially to compensate for the need of water-intensive crops due to loss of surface water. Policy and management implications from this research suggest decreasing water demands, such as decreasing crop water intensity, can mitigate groundwater depletion. [ABSTRACT FROM AUTHOR]

Groundwater plays a critical supporting role in agricultural production in the California Central Valley (CV). Recent prolonged droughts (notably 2007–2009 and 2012–2016) caused dramatic depletion of groundwater, indicating the susceptibility of the CV's water supply to climate change. To assess the impact of climate change on groundwater storage in the CV, we combined integrated surface water and groundwater models with climate projections from 20 global climate models and thereby explore the vulnerability of CV groundwater under two climate scenarios RCP4.5 and RCP8.5. We found that groundwater has been declining over the past decades (3 km3/year on average during 1950–2009). In the absence of future mitigating measures, this decline will continue, but at a higher rate due to climate change (31% and 39% increase in loss rate under RCP4.5 and RCP8.5). The greatest loss (more than 80% of the total) will occur in the semi-arid southern Tulare region. We performed computational experiments to quantify the relative contribution of future crop water use and headwater inflows to total groundwater storage change. Our results show that, without management changes, continuing declines in future groundwater storage will mainly be attributable to ongoing overuse of groundwater. However, future changes in the seasonality of streamflow into the CV, (small) changes in annual inflows, and increased crop water use in a warmer climate will lead to 40–70% more annual groundwater losses than the current annual average, up to approximately 5 km3/year. [ABSTRACT FROM AUTHOR]

Groundwater vulnerability assessment is an effective tool for groundwater pollution prevention and control. However, very few methods are available to address the vulnerability assessment of confined aquifers, though the risk of confined aquifer pollution has been increasing. This paper describes a method that integrates the vulnerability of the unconfined aquifer (V), extraction rate (E), aquitard thickness (Bt), head difference between unconfined and confined aquifers (H), aquitard media (At), and transmissivity of the confined aquifer (Tc), i.e., VEBHAT, to assess the vulnerability of confined aquifers. Hydraulic connections between the unconfined and confined aquifers, the vulnerability of unconfined aquifers, and the properties of confined aquifers were explicitly considered in this method. Moreover, the FO-TOPSIS method was used, which integrates fuzzy set theory (FST), along with an optimization model of multi-objective programming (OMP) and the classical TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, to determine the weights of assessment factors, and to reduce subjectivity of the weight assignment. To illustrate the applicability and reliability of the VEBHAT and FO-TOPSIS methods, they were applied to assess the vulnerability of confined aquifers in Zhaoyuan County, Heilongjiang Province, northeastern China. The Pearson correlation coefficient between the ammonium concentration data and the vulnerability assessment values was used to verify the validity of the methods. The results showed that the VEBHAT method and FO-TOPSIS method could be used to effectively assess the vulnerability of confined aquifers. [ABSTRACT FROM AUTHOR]

Traditional parametric methods for classification of land use and land cover (LULC) types using remote sensing imagery assume a global distribution model and fail to consider local variation of categorical variables. Differently, non-parametric methods do not make any statistical assumptions but are typically sensitive to the sample sizes of training sample data that usually require a high cost to collect in the field. Geostatistical classifiers, such as indicator kriging and simulation, are local variability-based methods that exhibit great potential for image-based classification of LULC types. However, variogram models required are highly sensitive to the spatial configuration of training samples as well as sample size given a study area. Moreover, when a large number of spectral variables are considered into kriging systems, modeling the variograms and cross-variograms would be problematic. To circumvent these issues, this study extended the geostatistical methods from a 2-dimensional geographic space to a m -dimensional image feature space to derive feature-space indicator variograms (FSIVs). Moreover, a novel stochastic simulation classification algorithm, Feature-Space Indicator Simulation (FSIS), was proposed and examined for classification of LULC types in Duolun County located in Inner Mongolia and in Huang-Feng-Qiao (HFQ) forest farm, Hunan of China. In Duolun, six LULC types were involved and in HFQ a complicated forest landscape consisting of nine forest types plus water, built-up area, and agricultural/bare soil, was classified. The classification results of FSIS were compared with another feature-space geostatistical classifier – feature-space indicator kriging (FSIK), a traditional parametric method – maximum likelihood (ML), a widely used nonparametric method – support vector machine (SVM), and a recently popular method – random forest (RF). The results showed that compared with ML, SVM and RF, in both study areas FSIS statistically significantly increased the accuracy of the classifications by 10.0–29.9% for percentage correct and 19.0–47.6% for Kappa statistic. Compared with FSIK, FSIS also improved the classification accuracy but the accuracy increases were relatively smaller with the percentages correct of 3.5% and 7.6% and the Kappa values of 4.6% and 8.6% for Duolun and HFQ, respectively. Moreover, FSIS led to the spatial uncertainties of the classification estimates as the quality measure of the estimates. In addition, the results also demonstrated that FSIVs were sensitive to the within-class heterogeneity but not very much to the size of training samples. Overall, FSIS exhibited the greater potential to improve the classification accuracy of LULC and forest types using remote sensing image. [ABSTRACT FROM AUTHOR]

Particle discrimination technology is widely used in multiple fields. Phoswich detectors are detectors based on pulse shape discrimination technology that combine two or more scintillators with different time characteristics to achieve particle discrimination. This study focuses on a phoswich detector composed of BGO/EJ-260 and uses machine learning algorithms to classify pulses to achieve β / γ classification. Experiments were conducted using the 137Cs radioactive source and three different models were trained: Gaussian mixture model, support vector machine, and convolutional neural network. The classification capabilities of the three models were tested and the results were discussed. The calculation results show that all three models achieved pulse data classification and accurately marked most pulses to the correct category. The classification ability of low-amplitude pulses by the Gaussian mixture model and support vector machine is limited by data processing, while the convolutional neural network model avoids this problem. For higher amplitude pulses, all three models showed that high classification accuracy, with the convolutional neural network model achieving a classification accuracy of 96.1% in the training set, achieves the expected goal. [ABSTRACT FROM AUTHOR]

Herein, a straightforward and efficient route for the construction of dual α,β-C(sp3)–H functionalized cyclic N-aryl amines using a combination of electrocatalysis and iron catalysis is disclosed. This method is achieved by multiple single electron oxidations and is carried out in an operationally simple manner with high chemo- and site-selectivity, setting the stage for the challenging multiple site selective C–H functionalization. The mechanism is supported by a variety of experimental probes and theoretical investigations, including cyclic voltammetry tests, a bipolar ultramicroelectrode (BUME) methodology combined with nano-electrospray ionization mass spectrometry, and density functional theory calculations. [ABSTRACT FROM AUTHOR]

Groundwater vulnerability assessment has been regarded as the initial step to understand and evaluate the susceptibility of the subsurface to contamination. As one of the most widely used models, the DRASTIC has been applied worldwide. However, problems associated with the model, such as the subjectivity in rating and weighting schemes, have led to modified versions of this model for better representing the vulnerability of the aquifer. In this study, a modified DRASTIC model was formulated by adjusting the rating and weighting scores based on Wilcoxon rank-sum test and Analytic Hierarchy Process. The modified model was then applied to the evaluation of groundwater vulnerability in Changli County, China in a GIS environment. The validity of the model was verified by analysing the correlation coefficient between the vulnerability index and the nitrate concentration. The results revealed that the modified DRASTIC model presents stronger correlation with the nitrate contamination than the traditional DRASTIC model. [ABSTRACT FROM AUTHOR]

Developing a ceramic scintillator operating in a phoswich detector is a feasible way for pulse shape discrimination. A YAG:Ce transparent ceramic scintillator has been synthesized by using the vacuum sintering method. In this work, we present a new phoswich detector mainly developed for the determination of low radioactivity levels. The system was constructed by combining a transparent ceramic scintillator, for the detection of α particles, and a CsI:Tl crystal, for the detection of γ-rays. With the device proposed here, the particle identification in a mixed α–γ field has been achieved with the rise time discrimination method. Furthermore, the figure of merit can also be improved using the phoswich detector. The higher reliability, simplicity, and low cost of the design that combines ceramics and crystals make the YAG ceramic have extremely good application prospects. [ABSTRACT FROM AUTHOR]

The Einstein Probe (EP) is a small scientific satellite committed to time-domain astronomy and high-energy astrophysics. One of the main payloads in the EP is the Follow-up X-ray Telescope (FXT). To get a quantity of in-orbit data as a reference, data from the Insight-Hard X-ray Modulation Telescope (Insight-HXMT) were adopted. The Insight-HXMT is dedicated to perform hard x-ray all-sky surveys, which has a parallel in-orbit environment with the EP. In this paper, we simulated the in-orbit background of an FXT by the Particle and Heavy-Ion Transport code System, which is the first general-purpose heavy ion transport Monte Carlo code, which has a simulated range from 0.01 meV to 1 TeV. The influence from protons in the EP orbital environment and the secondary rays caused by the shielding materials around the detector have been mainly considered. The Super Monte Carlo Simulation Program for nuclear and radiation Processes has been used to define the materials of the shielding structure. An isotropic source of protons and a complete shielding structure have been constructed in this simulation. First, the spectrum of background between 0.5 and 15 keV has been calculated. Second, we simulated the types of reactions and the fluxes of secondary particles, and all of them were calculated with the same model in the first step. Finally, we considered the effectiveness of the outermost shielding with different materials. Among the calculated spectra of background, we found two conspicuous energy responses with high counts, separately located in 7.345 keV (5 eV) and 8.045 keV (5 eV). Compared with the in-orbit data from the low energy x-ray telescope in the Insight-HXMT, the simulated results are credible. Considering the combination of the simulated types of reactions and fluxes of secondary particles, we could mostly judge that the secondary photons brought the photoelectric effects, which caused the two conspicuous responses in the spectrum of background. In addition, copper was the most suitable material for the outermost shielding in this simulation. [ABSTRACT FROM AUTHOR]

Frequent outbreaks of harmful algal blooms (HABs) have brought adverse impacts on human health, economic viability, and recreational activities in many communities in the United States. Cyanobacteria (or blue‐green algae) blooms are the most common type of HABs in surface water. Current bactericides for controlling the blooms are disadvantageous due to the recycling difficulty. In this study, an innovative magnetic nanomaterial―γFe2O3/TiO2 nanoparticle—was used to inactivate toxic cyanobacteria species found in a lake in Southern Illinois that frequently experienced HABs. Cyanotoxin genes of mcy, nda, cyr, and sxt were used for targeting microcystin‐, nodularin‐, cylindrospermopsin‐, and saxitoxin‐producing cyanobacteria, respectively, by quantitative polymerase chain reaction (PCR) method. It was found that the concentration of chlorophyll a presents a strong correlation (R2 = 0.6024) with the gene copy obtained from 16S rRNA targeted for all cyanobacteria, but not with that from individual toxigenic cyanobacteria. The inactivation efficiencies of the nanomaterials under visible light were as high as 5‐log and 1‐log for cyanobacteria species containing mcyE/ndaF and sxtA genes, respectively, an improvement over the treatment under darkness. These nanomaterials can be recycled by their magnetic properties for reuse. Communities susceptible to HAB outbreaks are expected to benefit from the developed method for mitigating the blooms. Practitioner points: Lab‐made γFe2O3/TiO2 nanoparticles can be used to inactivate microcystin/nodularin‐ and saxitoxin‐producing cyanobacteria species.qPCR method can be used for targeting toxic cyanobacteria; Chl a cannot be used as a standalone indicator for HABs.Better inactivation efficiency under visible light indicated possible application of the technology under sunlight for HAB mitigation from surface water. [ABSTRACT FROM AUTHOR]

With the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. It combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses. [ABSTRACT FROM AUTHOR]

An integrated assessment of crop-energy-water (CEW) nexus is critical to understand the tradeoffs and synergies for better management of sustainable agricultural systems. In this study, we evaluate the historic evolution of CEW interactions in the Central Valley, California, a critical agricultural region that produces approximately 50% of US fruits, nuts and vegetables. Specifically, we consider three nexus elements, including water use for irrigation (blue water), energy use for groundwater pumping, and crop yield (for all crops aggregated, almond and cotton). To quantify the interactions between CEW elements, we estimate the water use for cropping (water footprint) and energy use for cropping (energy footprint). We conduct the analyses for four historical periods, i.e., 2007–2009 (Drought 1), 2010–2011 (Post-drought 1), 2012–2015 (Drought 2) and 2016–2018 (Post-drought 2). We find that the southern regions (San Joaquin and Tulare) are susceptible to greater stress on energy and water, especially during droughts. The groundwater footprint (GWF) has been continuously increasing due to greater crop water use and a shift from row crops to profitable water-intensive tree crops. The GWF in Tulare during Drought 2 was around 60% higher than Drought 1, where the GWF in Tulare was almost twice that of Sacramento. The energy and water uses for almond production have increased during the recent periods, whereas their uses have mostly decreased for cotton. On average, energy and water footprints under almond crop scenario are around 3–3.5 times as much as the footprints under all crops scenario. [ABSTRACT FROM AUTHOR]

Intensive use of military vehicles in military installations create conditions favorable for gully formation. Gullies impede the access of vehicle, restrict the continuation of training, and lead to significant damage to vehicle and risk the life of soldiers. Therefore, it is critical to correctly identify the locations of gullies for continuous training mission. In this study, Fort Riley (FR) military installation was chosen as the study area. LiDAR derived 1 m resolution digital elevation model (DEM) acquired on 2010 was used to map the gullies. A procedure that measures local topographic position, i.e., difference from mean elevation (DFME) along with its integration to the land surface having high surface curvature values was employed. Two high spatial resolution WorldView-2 images of 2010 and field gully data collected in 2010 were utilized for accuracy assessment. Results showed that: (1) A total of 237 small and 166 large gullies were detected and most of them dominated the central west and northwest parts of the installation; (2) Based on the visual interpretation in the WorldView-2 images, there was no statistically significant difference between the detected and observed numbers of gullies; (3) Gullies measured in the field were well detected with an overall accuracy of 78%. [ABSTRACT FROM AUTHOR]

Understanding soil moisture spatial variability (SMSV) at regional scales is of great value for various purposes; however, relevant studies are still limited and have yielded inconsistent findings about the primary controls on regional SMSV. To further address this issue, long-term soil moisture data were retrieved from two large scale monitoring networks located in the continental United States, including the Michigan Automated Weather Network and the Oklahoma Mesonet. To evaluate different controls on SMSV, supporting datasets, which contained data on climate, soil, topography, and vegetation, were also compiled from various sources. Based on temporal stability analysis, the results showed that the mean relative difference ( MRD ) of soil moisture was more correlated with soil texture (e.g., negative correlations between MRD and sand fraction, and positive ones between MRD and silt and clay fractions) than with meteorological forcings in both regions, which differed from the traditional notion that meteorological forcings were the dominant controls on regional SMSV. Moreover, the results revealed that contrary to the previous conjecture, the use of soil moisture temporal anomaly did not reduce the impacts of static properties (e.g., soil properties) on soil moisture temporal dynamics. Instead, it was found that the magnitude of soil moisture temporal anomaly was mainly negatively correlated with sand fraction and positively with silt and clay fractions in both regions. Finally, the relationship between the spatial average and standard deviation of soil moisture as well as soil moisture temporal anomaly was investigated using the data from both networks. The field data showed that the relationship for both soil moisture and soil moisture temporal anomaly was more affected by soil texture than by climatic conditions (e.g., precipitation). The results of this study provided strong field evidence that local factors (e.g., soil properties) might outweigh regional factors (e.g., meteorological forcings) in controlling regional SMSV. [ABSTRACT FROM AUTHOR]

To realize the discrimination of β particles and γ photons in complex environments, a phoswich detector based on the pulse shape discrimination method was designed. The scintillators EJ-260 and CsI(Tl) are selected for deposition of β particles and γ photons, respectively. The size of the detector was determined by simulating the detector by the Monte Carlo program G eant 4. The detector was assembled for testing, and the pulses were collected using a high-frequency digital capture card. After smoothing the pulse and correcting the rise time, the respective energy spectra of β particles and γ photons in the mixed environment were obtained. The figure of merit value of phoswich detector was 6.35, which can effectively discriminate β particles and γ photons. Results indicate the phoswich detector can be used for the β − γ discrimination in the mixed fields and obtain the respective energy spectrum. • A phoswich detector based on the pulse shape discrimination method was designed. • The size of the detector was determined by simulating the detector by the Monte Carlo program GEANT4. • The respective energy spectra of β particles and γ photons in the mixed environment were obtained. [ABSTRACT FROM AUTHOR]

This study examined shifts in climate regimes over the global land area using the Köppen–Trewartha (K–T) climate classification by analyzing observations during 1900–2010, and simulations during 1900–2100 from twenty global climate models participating in Phase 5 of the Coupled Model Inter-comparison Project (CMIP5). Under the Intergovernmental Panel on Climate Change Representative Concentration Pathways 8.5 (RCP8.5) scenario, the models projected a 3°–10°C warming in annual temperature over the global land area by the end of the twenty-first century, with strong (moderate) warming in the high (middle) latitudes of the Northern Hemisphere and weaker warming in the tropics and the Southern Hemisphere. The projected changes in precipitation vary considerably in space and present greater uncertainties among the models. Overall, the models are consistent in projecting increasing precipitation over the high-latitude of the Northern Hemisphere, and reduced precipitation in the Mediterranean, southwestern North America, northern and southern Africa and Australia. Based on the projected changes in temperature and precipitation, the K–T climate types would shift toward warmer and drier climate types from the current climate distribution. Regions of temperate, tropical and dry climate types are projected to expand, while regions of polar, sub-polar and subtropical climate types are projected to contract. The magnitudes of the projected changes are stronger in the RCP8.5 scenario than the low emission scenario RCP4.5. On average, the climate types in 31.4% and 46.3% of the global land area are projected to change by the end of the twenty-first century under RCP4.5 and RCP8.5 scenarios, respectively. Further analysis suggests that changes in precipitation played a slightly more important role in causing shifts of climate type during the twentieth century. However, the projected changes in temperature play an increasingly important role and dominate shifts in climate type when the warming becomes more pronounced in the twenty-first century. [ABSTRACT FROM AUTHOR]