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Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy of the produced maps. Challenges such as limited data, uncertainty due to confidence bounds, and the overfitting problem are critical areas for improving accurate models. We focus on the uncertainty in susceptibility mapping, mainly when there is a significant variation in the predictive relevance of the predictor factors. It is also noted that the receiver operating characteristic (ROC) curve may not accurately depict the sensitivity of the resulting susceptibility map to overfitting. Therefore, reducing the overfitting problem was targeted to increase accuracy and improve processing time in flood prediction. This study created a spatial repository to test the models, containing data from historical flooding and twelve topographic and geo-environmental flood conditioning variables. Then, we applied random forest (RF) and extreme gradient boosting (XGB) algorithms to map flood susceptibility, incorporating a variable drop-off in the empirical loop function. The results showed that the drop-off loop function was a crucial method to resolve the model uncertainty associated with the conditioning factors of the susceptibility modelling and methods. The results showed that approximately 8.42% to 9.89% of Marib City and 9.93% to 15.69% of Shibam City areas were highly vulnerable to floods. Furthermore, this study significantly contributes to worldwide endeavors focused on reducing the hazards linked to natural disasters. The approaches used in this study can offer valuable insights and strategies for reducing natural disaster risks, particularly in Yemen. [ABSTRACT FROM AUTHOR]

Microwave-assisted chemical reactions have been widely used, but the overheating effect limits further applications. The aim of this paper is to investigate the coupling degree of the electromagnetic field and thermal field in microwave-heating chemical reactions whose polarization changes as the reactions proceed. First, the entropy-balance equation of microwave-heating polar-molecule reactions is obtained. Then, the coupling degree of the electromagnetic field and the thermal field in microwave-heating polar-molecule reactions is derived, according to the entropy-balance equation. Finally, the effects of reaction processes on the degree of coupling are discussed. When the time scale of the component-concentration variation is much greater than the wave period during the chemical processes, the degree of coupling is sufficiently small, and the electric and thermal fields are considered as weakly coupled. On the other hand, the degree of coupling may change during the reactions. If the absolute value of the coupling degree becomes larger, due to the change in component concentration, this will lead to a transition from weak coupling to strong coupling. [ABSTRACT FROM AUTHOR]

The Songliao Plain is the largest maize (Zea mays L.) cropland area in China and, thus, is most influenced by water stress. To mitigate the adverse impact of water stress on maize yield and quality, various agricultural irrigation strategies have been implemented. Based on land surface temperature and an enhanced vegetation index, this study constructed the temperature vegetation dryness index (TVDI) and combined the Hurst index and Sen trend to analyze the spatiotemporal characteristics of drought and waterlogging. From the correlation between TVDI and gross primary productivity, the weight coefficients of different growth cycles of maize were derived to determine the drought and waterlogging stresses on maize in Songliao Plain for 2000–2020. The drought hazard on the western side of Songliao Plain was high in the west and low in the east, whereas the waterlogging hazard was high in the east. Waterlogging likely persisted according to the spatiotemporal trends and patterns of drought and waterlogging. During the second growth cycle, maize was most severely affected by water stress. There was a spatial heterogeneity in the severity of the hazards and the stress degree of maize. For the reason that precipitation in the study area was concentrated between mid-late July and early August, maize was susceptible to drought stress during the first two growth stages. Irrigation concentrated in the early and middle stages of maize growth and development in the western part of the Songliao Plain reduced the drought stress-induced damage. Spatiotemporally-detected drought and waterlogging couplings and hazards for maize in the Songliao Plain for 2000–2020 provide actionable insights into the prevention and mitigation of such disasters and the implementation of water-saving irrigation practices at the regional scale. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-channel photonic crystal fiber sensor, which adopts dual-polarization and multiple materials to effectively reduce the mutual interference between channels and enhance the surface plasmon resonance, thus achieving simultaneous detection of a multi-channel with low interference. Four channels are polished around the cylindrical fiber, and then different metal films (gold or silver) and plasmonic materials (titanium dioxide, thallium pentoxide, or graphene) are added to the sensing area of each channel. All channels detect refractive indices in the range of 1.34 to 1.42. The sensing performance of the fiber optic sensor was numerically investigated using the full vector finite element method. After the optimization of structural parameters, the maximum wavelength sensitivity of channel-1, channel-2, channel-3, and channel-4 are 49,800 nm/RIU, 49,000 nm/RIU, 35,900 nm/RIU, and 36,800 nm/RIU, respectively. We have theoretically analyzed the sensor's capabilities for partial bio-detection and simulated its detection capability with a wavelength sensitivity of 11,500 nm/RIU for normal red blood cells and 12,200 nm/RIU for MCF-7 cancerous cells. Our proposed sensor has a novel design, can detect multiple channels simultaneously, has strong anti-interference capability and high sensitivity, and has good sensing characteristics. [ABSTRACT FROM AUTHOR]

Hydrological connectivity affects the material cycling and energy transfer of ecosystems and is an important indicator for assessing the function of aquatic ecosystems. Therefore, clarification of hydrologic connectivity and its optimization methods is essential for basin water resources management and other problems; however, most of the current research is focused on intermittently flooded areas, especially in terms of optimization, and on hydrological regulation within mature water structures, while research on hydrological connectivity in dry, low rainfall plain areas remains scarce. Based on the graph and binary water cycle theories, this study assessed and hierarchically optimized the structural hydrological connectivity of the Dongliao River Basin (DRB), integrating artificial and natural connectivity, and explored the hydrological connectivity optimization method in the arid plain region at the basin scale to increase connectivity pathways. The spatial analysis and evaluation of hydrological connectivity was also carried out based on the results of the hierarchical optimization, and provided three scenarios for the construction of hydrological connectivity projects in the basin. The hierarchical optimization yielded a total of 230 new water connectivity paths, and the overall hydrological connectivity increased from 5.07 to 7.64. Our results suggest a large spatial correlation in hydrological flow obstruction in the DRB. The center of gravity of circulation obstruction shifted to the south after optimization for different levels of connectivity. With the increase in the optimization level of hydrological connectivity, the national Moran index rose and then fell. The magnitude of the increase in hydrological connectivity effects varied at different optimization levels, and there were sudden points' increase points. From an application point of view, Scenario 1 is necessary and the most cost effective is Scenario 2, which provides a scientific basis for guiding the construction of future ecological projects in the DRB. [ABSTRACT FROM AUTHOR]

To achieve high performance and wide range detection, we propose an ultra-wide range high sensitivity plasmonic fiber optic sensor with a gold (Au) nanowire group array, which has both propagating surface plasmon resonance (PSPR) and local surface plasmon resonance (LSPR) sensing characteristics. The PSPR, LSPR, and PSPR+LSPR are presented as Au thin layers, Au spheres (or Au nanowires), and Au nanowire group arrays, respectively, and their respective properties are analyzed from theoretical, simulated, and numerical aspects. When detection is performed, the presence of both evanescent wave and electric field forces in the Au nanowire group array combines to significantly improve the sensor's detection capability. Detection simulation analysis was performed using COMSOL Multiphysics software. The range of refractive indices that can be detected is 1.08 to 1.37 in the optical band from 1210 nm to 2140 nm. In the detection range, the maximum sensitivity of the detected wavelength is 13,000 nm/RIU. Our proposed sensor has a broad range, high sensitivity, and low refractive index detection, and has good research value and application prospects. [ABSTRACT FROM AUTHOR]

Based on coupled-mode theory (CMT) and the finite-difference time-domain (FDTD) approach, we propose a graphene metasurface-based and multifunctional polarization beam splitter that is dynamically tunable. The structure, comprising two graphene strips at the top and bottom and four triangular graphene blocks in the center layer, can achieve triple plasma-induced transparency (PIT). In a single polarization state, the computational results reveal that synchronous or asynchronous six-mode electro-optical switching modulation may be performed by modifying the Fermi levels of graphene, with a maximum modulation degree of amplitude (MDA) of 97.6% at 5.148 THz. In addition, by varying the polarization angle, a polarization-sensitive, tunable polarization beam splitter (PBS) with an extinction ratio and insertion loss of 19.6 dB and 0.35 dB at 6.143 THz, respectively, and a frequency modulation degree of 25.2% was realized. Combining PIT with polarization sensitivity provides a viable platform and concept for developing graphene metasurface-based multifunctional and tunable polarization devices. [ABSTRACT FROM AUTHOR]

Flooding is one of the catastrophic natural hazards worldwide that can easily cause devastating effects on human life and property. Remote sensing devices are becoming increasingly important in monitoring and assessing natural disaster susceptibility and hazards. The proposed research work pursues an assessment analysis of flood susceptibility in a tropical desert environment: a case study of Yemen. The base data for this research were collected and organized from meteorological, satellite images, remote sensing data, essential geographic data, and various data sources and used as input data into four machine learning (ML) algorithms. In this study, RS data (Sentinel-1 images) were used to detect flooded areas in the study area. We also used the Sentinel application platform (SNAP 7.0) for Sentinel-1 image analysis and detecting flood zones in the study locations. Flood spots were discovered and verified using Google Earth images, Landsat images, and press sources to create a flood inventory map of flooded areas in the study area. Four ML algorithms were used to map flash flood susceptibility (FFS) in Tarim city (Yemen): K-nearest neighbor (KNN), Naïve Bayes (NB), random forests (RF), and eXtreme gradient boosting (XGBoost). Twelve flood conditioning factors were prepared, assessed in multicollinearity, and used with flood inventories as input parameters to run each model. A total of 600 random flood and non-flood points were chosen, where 75% and 25% were used as training and validation datasets. The confusion matrix and the area under the receiver operating characteristic curve (AUROC) were used to validate the susceptibility maps. The results obtained reveal that all models had a high capacity to predict floods (AUC > 0.90). Further, in terms of performance, the tree-based ensemble algorithms (RF, XGBoost) outperform other ML algorithms, where the RF algorithm provides robust performance (AUC = 0.982) for assessing flood-prone areas with only a few adjustments required prior to training the model. The value of the research lies in the fact that the proposed models are being tested for the first time in Yemen to assess flood susceptibility, which can also be used to assess, for example, earthquakes, landslides, and other disasters. Furthermore, this work makes significant contributions to the worldwide effort to reduce the risk of natural disasters, particularly in Yemen. This will, therefore, help to enhance environmental sustainability. [ABSTRACT FROM AUTHOR]

Rain-fed agriculture is easily affected by meteorological disasters, especially drought. As an important factor of risk formation, actively carrying out agricultural drought vulnerability assessments is conducive to improving food security and reducing economic losses. In this study, an SE-DEA model with regional exposure and drought risk as input factors and the maize yield reduction rate and drought-affected area as output factors is established. The aim is to evaluate and zone the drought vulnerability of the maize belt in the Songliao Plain. The results show the following: (1) From 2000 to 2019, the drought vulnerability of maize showed a fluctuating increasing trend. The vulnerability in Harbin and central Jilin Province is high, which is extremely unfavorable for maize production. (2) Comparing the historical disaster data with the drought vulnerability map generated using the SE-DEA model, it could be found that the results obtained using the SE-DEA model are reliable. (3) The Tobit model shows that the proportion of the effective irrigated area is more important to alleviate vulnerability. For drought vulnerability zoning using a cluster analysis, we suggest that regulated deficit irrigation should be actively developed in high-vulnerability areas to ensure maize yield while improving water efficiency. The results of this study can provide a basis for the development of drought mitigation and loss reduction strategies, and they provide new ideas for future research. [ABSTRACT FROM AUTHOR]

The negative impact of high-temperature disaster on agricultural production is becoming more and more serious, and reducing the vulnerability to high-temperature disaster is fundamental to achieving sustainable agricultural development. This study is mainly focused on the vulnerability to agricultural high-temperature disaster in Shaanxi Province, China. Firstly, 15 indicators were selected from the perspectives of exposure, sensitivity, and adaptability. Secondly, the combined weighting method (Critic-G1 model) was used to determine the weight of each index. Based on the aforementioned procedures, the Kullback–Leibler (KL)-distance-improved TOPSIS model was utilized to evaluate the vulnerability. Lastly, the obstacle model was used to analyze the influencing factors and to make recommendations for disaster prevention and mitigation. The results show that: (1) The improved TOPSIS model was closer to the results of the synthetical index method. (2) The northern and southern area of Shaanxi is more vulnerable to high-temperature disaster, especially in Ankang and Tongchuan. Low values are distributed in the Guanzhong Plain. (3) Sensitivity is the biggest obstacle to reducing the vulnerability to high-temperature disaster. Among the influencing factors, the meteorological yield reduction coefficient of variation, multiple cropping index and per capita net income of rural residents of the obstacle are high. Decreasing sensitivity should be accompanied by increasing adaptability to improve regional disaster preparedness and mitigation. The results of this study can provide a basis for the development of agricultural high-temperature disaster mitigation and loss reduction strategies and provide new ideas for future research. [ABSTRACT FROM AUTHOR]

Refractory ventricular tachycardia (VT) often occurs in the context of organic heart disease. It is associated with significantly high mortality and morbidity rates. Antiarrhythmic drugs and catheter ablation represent the two main treatment options for refractory VT, but their use can be associated with inadequate therapeutic responses and procedure-related complications. Stereotactic body radiotherapy (SBRT) is extensively applied in the precision treatment of solid tumors, with excellent therapeutic responses. Recently, this highly precise technology has been applied for radioablation of VT, and its early results demonstrate a favorable safety profile. This review presents the potential value of SBRT in refractory VT. [ABSTRACT FROM AUTHOR]

Drought is a major natural disaster that causes a reduction in rain-fed maize yield. Agricultural drought risk assessment is conducive to improving regional disaster management ability, thereby reducing food security risks and economic losses. Considering the complexity of risk assessment research, an increasing number of researchers are focusing on the multiple-criteria decision-making (MCDM) method. However, the applicability of the MCDM method to agro-meteorological disaster risk assessments is not clear. Therefore, this study comprehensively evaluated hazard, exposure, vulnerability, and emergency response and recovery capability using the TOPSIS and VIKOR models to generate a maize drought risk map in mid-western Jilin Province and ranked the drought risk of each county. The results showed that: (1) maize drought risk in the middle and west of Jilin province showed an increasing trend. Spatially, the risk diminished from west to east. The drought risks faced by Tongyu, Changchun, and Dehui were more severe; (2) the evaluation results of the two models were verified using the yield reduction rate. The VIKOR model was found to be more suitable for agrometeorological drought risk assessments; (3) according to the damage degree of drought disaster to maize, the cluster analysis method was used to divide the study area into three sub-regions: safe, moderate drought, and severe drought. Combined with the characteristics of different regions, suggestions on disaster prevention and mitigation are proposed. The results of this study can provide a basis for formulating strategies to alleviate drought, reduce losses, and ensure the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

Sulfur dioxide (SO2) is a major atmospheric pollutant and abiotic stressor. Although physiological studies on abiotic stressors have focused on fully expanded leaves, the resistance of leaf functional traits to SO2 during individual leaf development has not been studied. Thus, this study aimed to conduct SO2 static artificial fumigation experiments to evaluate changes in leaf functional traits and resistance to SO2 for three common landscape tree species (Syringa oblata Lindl. (S. oblata), Prunus cerasifera var. atropurpurea Jack. (P. cerasifera), and Ulmus pumila 'Jinye' (U. pumila)) in Changchun City and ontogeny under SO2 stress. Samples were collected on three days in autumn (1 September, 9 September, and 19 September 2019) for two different leaf stages (10 days and 40 days). In addition, remote sensing data were combined to explore the resistance mechanisms of broadleaf forests to different SO2 concentration classes during different seasons on a large scale. The results showed that the chlorophyll content, leaf temperature, green-peak reflectance, and Fv/Fm (maximal photochemical efficiency) at 10 days were significantly lower than that at 40 days, regardless of sampling date or SO2 concentration. Additionally, in general the SO2 resistance for 10 days leaves was consistently smaller than those for 40 days leaves in 3 tree species. On 9 September, 10 days leaves of the three tree species showed different leaf resistance performances under different SO2 concentrations in the order: P. cerasifera > S. oblata > U. pumila. Lastly, the extent of resistance decreased with increasing ρ(SO2) classes in different seasons, and the SO2 resistance was affected by season. We conclude that mature leaves are more resistant to SO2 stress than young leaves are. These results will provide scientific guidance on artificial plant community construction and prevention of future vegetation degradation. [ABSTRACT FROM AUTHOR]

Drought is one of the key limiting factors for potato yield in the northern single cropping area (NSCA) in China. To analyze the impact of drought on potato yield in the NSCA, this study first analyzed the variation of dry/wet conditions in the plantable areas on a seasonal scale using the standardized precipitation evapotranspiration index (SPEI). Secondly, the changes in yield structure in the last 36 years were systematically analyzed and divided the total yield change into planting area contribution and climate yield contribution. Finally, a regression model of the seasonal drought index and contributing factors of total yield change in different administrative regions was constructed. The results showed that the main factors affecting the total potato yield of the NSCA began to change from yield to planting area in the 1990s, while the barycenter of the output structure and population moved to the southwest, with grassland being the main source; dry/wet conditions (year i) had varying degrees of effect on contributing factors (year i, year i + 1) of total yield change in different administrative regions that were not limited to the growing season; the non-overlap of high-yield area, high-adaptability area and planting area was the urgent problem to be solved for the NSCA. The results of this study can provide a scientific basis for NSCA crop management and communication with farmers, providing new ideas for sustainable production in other agricultural regions in the world. [ABSTRACT FROM AUTHOR]

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions. [ABSTRACT FROM AUTHOR]

Under the background of global warming, the frequent occurrence and long-term persistence of drought events have substantial negative effects on agricultural production. As the main maize production area in midwestern Jilin Province, frequent drought and a shortage of irrigation water pose substantial threats to the production of maize. We analyzed the balance of water supply and demand in each growth period and the degree of maize yield affected by drought. The results indicate that the FIO-ESM climate model can effectively simulate the changes in temperature and precipitation, and was highly applicable to the study area. From 1980 to 2020, the drought risk indices for the sowing to jointing, jointing to tasseling, tasseling to milk-ripe, and milk-ripe to maturity stages were 0.62, 0.52, 0.48, and 0.60, respectively. In the future, the chances of a RCP8.5 scenario drought risk and an enhanced RCP4.5 scenario have eased. Spatially, the high-risk areas shift in a "west−central−southwest" pattern. Effective precipitation will decrease in the future, while the increasing water requirement of maize increases the dependence on irrigation water. The irrigation requirement index is more than 70% for all periods, particularly in the milk-ripe to maturity stage. The relative meteorological yields were positively correlated with the CWDI of the whole growth period, with the rate of reduction in maize yield and the yield reduction coefficient of variation at a high level of risk between 1980 and 2020. In the future, the negative impact of drought risk on the yield of maize lessened with no obvious trend in production. In particular, the rate of reduction and reduction coefficient of variation for the RCP8.5 scenario were 1.24 and 1.09, respectively. [ABSTRACT FROM AUTHOR]

This research assessed the changes in spatial patterns and the seasonal trends in temperature, precipitation, and relative humidity over 36 years (1979–2014) using Climate Forecast System Reanalysis (CFSR) datasets. The evaluation of climate deviations was the prime objective of this research. The augmented Dickey–Fuller Test (ADF) was used to scrutinize whether the data was either stationary or non-stationary. The results of the ADF test showed that all the datasets were found to be stationary at lag order 3. To observe undulations in the time series data, trend analyses were done using Sen's slope (SS), Mann–Kendall (MK), and Cox and Stuart (CS) tests. For all the statistical analyses, we considered the 5% significance level (α = 0.05) and p < 0.05 to be statistically significant. We observed significant (p < 0.05) trends in spring (MAM) and autumn (SON) for minimum temperature (Tmin) in Punjab. We also noted a significant (p < 0.05) trend in precipitation during autumn (SON). Annually, all the variables showed a non-significant (p > 0.05) trend for Punjab, Pakistan, during the period 1979–2014. Climate variability, such as a decrease in precipitation, higher temperature, and relative humidity fluctuations, were the reasons for the imbalance in the sustainability of Punjab, Pakistan. [ABSTRACT FROM AUTHOR]

Land use change is an important driving force factor affecting the river water environment and directly affecting water quality. To analyze the impact of land use change on water quality change, this study first analyzed the land use change index of the study area. Then, the study area was divided into three subzones based on surface runoff. The relationship between the characteristics of land use change and the water quality grade was obtained by grey correlation analysis. The results showed that the land use types changed significantly in the study area since 2000, and water body and forest land were the two land types with the most significant changes. The transfer rate is cultivated field > forest land > construction land > grassland > unused land > water body. The entropy value of land use information is represented as Area I > Area III > Area II. The shift range of gravity center is forest land > grassland > water body > unused land > construction land > cultivated field. There is a strong correlation between land use change index and water quality, which can be improved and managed by changing the land use type. It is necessary to establish ecological protection areas or functional areas in Area I, artificial lawns or plantations shall be built in the river around the water body to intercept pollutants from non-point source pollution in Area II, and scientific and rational farming in the lower reaches of rivers can reduce non-point source pollution caused by farming. [ABSTRACT FROM AUTHOR]

Tea trees are the main economic crop in Zhejiang Province. However, spring cold is a frequent occurrence there, causing frost damage to the valuable tea buds. To address this, a regional frost-hazard early-warning system is needed. In this study, frost damage area was estimated based on topography and meteorology, as well as longitude and latitude. Based on support vector machine (SVM) and artificial neural networks (ANNs), a multi-class classification model was proposed to estimate occurrence of regional frost disasters using tea frost cases from 2017. Results of the two models were compared, and optimal parameters were adjusted through multiple iterations. The highest accuracies of the two models were 83.8% and 75%, average accuracies were 79.3% and 71.3%, and Kappa coefficients were 79.1% and 67.37%. The SVM model was selected to establish spatial distribution of spring frost damage to tea trees in Zhejiang Province in 2016. Pearson's correlation coefficient between prediction results and meteorological yield was 0.79 (p < 0.01), indicating consistency. Finally, the importance of model factors was assessed using sensitivity analysis. Results show that relative humidity and wind speed are key factors influencing accuracy of predictions. This study supports decision-making for hazard prediction and defense for tea trees facing frost. [ABSTRACT FROM AUTHOR]

Plant phenology depends largely on temperature, but temperature alone cannot explain the Northern Hemisphere shifts in the start of the growing season (SOS). The spatio–temporal distribution of SOS sensitivity to climate variability has also changed in recent years. We applied the partial least squares regression (PLSR) method to construct a standardized SOS sensitivity evaluation index and analyzed the combined effects of air temperature (Tem), water balance (Wbi), radiation (Srad), and previous year's phenology on SOS. The spatial and temporal distributions of SOS sensitivity to Northern Hemisphere climate change from 1982 to 2014 were analyzed using time windows of 33 and 15 years; the dominant biological and environmental drivers were also assessed. The results showed that the combined sensitivity of SOS to climate change (SCom) is most influenced by preseason temperature sensitivity. However, because of the asymmetric response of SOS to daytime/night temperature (Tmax/Tmin) and non-negligible moderating of Wbi and Srad on SOS, SCom was more effective in expressing the effect of climate change on SOS than any single climatic factor. Vegetation cover (or type) was the dominant factor influencing the spatial pattern of SOS sensitivity, followed by spring temperature (Tmin > Tmax), and the weakest was water balance. Forests had the highest SCom absolute values. A significant decrease in the sensitivity of some vegetation (22.2%) led to a decreasing trend in sensitivity in the Northern Hemisphere. Although temperature remains the main climatic factor driving temporal changes in SCom, the temperature effects were asymmetric between spring and winter (Tems/Temw). More moisture might mitigate the asymmetric response of SCom to spring/winter warming. Vegetation adaptation has a greater influence on the temporal variability of SOS sensitivity relative to each climatic factor (Tems, Temw, Wbi, Srad). More moisture might mitigate the asymmetric response of SCom to spring/winter warming. This study provides a basis for vegetation phenology sensitivity assessment and prediction. [ABSTRACT FROM AUTHOR]

Urban flooding has been an alarming issue in the past around the globe, particularly in South Asia. Pakistan is no exception from this situation where urban floods with associated damages are frequently occurring phenomena. In Pakistan, rapid urbanization is the key factor for urban flooding, which is not taken into account. This study aims to identify flood sensitivity and coping capacity while assessing urban flood resilience and move a step toward the initialization of resilience, specifically for Peshawar city and generally for other cities of Pakistan. To achieve this aim, an attempt has been made to propose an integrated approach named the "urban flood resilience model (UFResi-M)," which is based on geographical information system(GIS), remote sensing (RS), and the theory of analytical hierarchy process (AHP). The UFResi-M incorporates four main factors—urban flood hazard, exposure, susceptibility, and coping capacity into two parts, i.e., sensitivity and coping capacity. The first part consists of three factors—IH, IE, and IS—that represent sensitivity, while the second part represents coping capacity (ICc). All four indicators were weighted through AHP to obtain product value for each indicator. The result showed that in the Westzone of the study area, the northwestern and central parts have very high resilience, whereas the southern and southwestern parts have very low resilience. Similarly, in the East zone of the study area, the northwest and southwest parts have very high resilience, while the northern and western parts have very low resilience. The likelihood of the proposed model was also determined using the receiver operating characteristic (ROC) curve method; the area under the curve acquired for the model was 0.904. The outcomes of these integrated assessments can help in tracking community performance and can provide a tool to decision makers to integrate the resilience aspect into urban flood management, urban development, and urban planning. [ABSTRACT FROM AUTHOR]

Erlong Lake is considered one of the largest lakes in midwest Jilin, China, and one of the drinking water resources in neighboring cities. The present study aims to explore the usage of Landsat TM5, ETM7, and OLI8 images to assess water quality (V-phenol, dissolved oxygen (DO), NH4-N, NO3-N) in Erlong Lake, Jilin province, northeast China. Thirteen multispectral images were used in this study for May, July, August, and September in 2000, 2001, 2002, and October 2020. Radiometric and atmospheric corrections were applied to all images. All in situ water quality parameters were strongly correlated to each other, except DO. The in situ measurements (V-phenol, dissolved oxygen, NH4-N, NO3-N) were statistically correlated with various spectral band combinations (blue, green, red, and NIR) derived from Landsat imagery. Regression analysis reported that there are strong relationships between the estimated and retrieved water quality from the Landsat images. Moreover, in calibrations, the highest value of the coefficient of determination (R2) was ≥0.85 with (RMSE) = 0.038; the lowest value of R2 was >0.30 with RMSE= 0.752. All generated models were validated in different statistical indices; R2 was up to 0.95 for most cases, with RMSE ranging from 1.390 to 0.050. Finally, the empirical algorithms were successfully assessed (V-phenol, dissolved oxygen, NH4-N, NO3-N) in Erlong Lake, using Landsat images with very good accuracy. Both in situ and model retrieved results showed the same trends with non-significant differences. September of 2000, 2001, and 2002 and October of 2020 were selected to assess the spatial distributions of V-phenol, DO, NH4-N, and NO3-N in the lake. V-phenol, NH4-N, and NO3-N were reported low in shallow water but high in deep water, while DO was high in shallow water but low in deep water of the lake. Domestic sewage, agricultural, and urban industrial pollution are the most common sources of pollution in the Erlong Lake. [ABSTRACT FROM AUTHOR]

The bottlenecks in enhancing regional green development are resource shortages, environmental pollution, and ecological degradation. Taking the Dongliao River Basin (DRB) of Jilin Province as an example, this study explored green development from a multidimensional perspective. Based on the dimension evaluation results of REECC (resources, environment, and ecological carrying capacity), PLES (production–living–ecological space), and ER (ecological redline), the coupling coordination degree model and spatial autocorrelation model were constructed to explore the coupling coordination degree and spatial distribution of green development. The results showed that REECC had significant spatial differences, and the REECC index showed an increasing trend from northwest to southeast. In 2018, the overall level of green development in the DRB has obvious spatial dependence, but there were spatial differences, with a more obvious polarization from northwest to southeast. The spatial distribution of the coupling degree and coupling coordination degree is roughly the same, and there is a clustering distribution. The conclusions have practical significance for future environmental protection and economic production in the DRB. [ABSTRACT FROM AUTHOR]

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data. [ABSTRACT FROM AUTHOR]

Herein, we propose a tunable plasmonic sensor with Fano resonators in an inverted U-shaped resonator. By manipulating the sharp asymmetric Fano resonance peaks, a high-sensitivity refractive index sensor can be realized. Using the multimode interference coupled-mode theory and the finite element method, we numerically simulate the influences of geometrical parameters on the plasmonic sensor. Optimizing the structure parameters, we can achieve a high plasmonic sensor with the maximum sensitivity for 840 nm/RIUand figure of merit for 3.9 × 105. The research results provide a reliable theoretical basis for designing high sensitivity to the next generation plasmonic nanosensor. [ABSTRACT FROM AUTHOR]

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing. [ABSTRACT FROM AUTHOR]

Land use/cover change (LUCC) is one of the causes of global climate and environmental change. Understanding rapid LUCC in urbanized areas is vital for natural resources management for sustainable development. This study primarily considered Vientiane, the capital of Laos, which experienced rapid LUCC due to both natural and anthropogenic factors. The study used geographical information system (GIS) combined with ERDAS and TerrSet technologies to objectively process the ground surveyed and remotely obtained data in order to investigate the historical LUCC as well as predict future LUCC in the study area during the periods of 1995–2018 and 2030–2050, respectively. A comprehensive list of assessment factors comprised of both natural and anthropogenic factors was used for analysis using the cellular automata–Markov (CA–Markov) model. The results show a historical loss of intact forest of 24.36% and of bare land of 1.01%. There were also tremendous increases in degraded forest (11.36%), agricultural land (8.91%), built-up areas (4.49%) and water bodies (1.16%). Finally, the LUCC prediction results indicate the conversion of land use from one type to another, particularly from natural to anthropogenic use, in the near future. These changes demonstrate that the losses associated with ecosystem services will destructively impact human wellbeing in the city and other areas of the country. The study results provide the basic scientific knowledge for LUCC planners, urban designers and natural resources managers. They serve as a decision-making support tool for the establishment of sustainable land resource utilization policies in Vientiane and other cities of similar conditions. [ABSTRACT FROM AUTHOR]

Residents in industrial cities may be exposed to potentially toxic elements (PTEs) in soil that increase chronic disease risks. In this study, six types of PTEs (Zn, As, Cr, Ni, Cu, and Pb) in 112 surface soil samples from three land use types—industrial land, residential land, and farmland—in Tonghua City, Jilin Province were measured. The geological accumulation index and pollution load index were calculated to assess the pollution level of metal. Meanwhile, the potential ecological risk index, hazard index, and carcinogenic risk were calculated to assess the environmental risks. The spatial distribution map was determined by the ordinary kriging method, and the sources of PTEs were identified by factor analysis and cluster analysis. The average concentrations of Zn, As, Cr, Ni, Cu, and Pb were 266.57, 15.72, 72.41, 15.04, 20.52, and 16.30 mg/kg, respectively. The results of the geological accumulation index demonstrated the following: Zn pollution was present in all three land use types, As pollution in industrial land cannot be neglected, Cr pollution in farmland was higher than that in the other two land use types. The pollution load index decreased in the order of industrial land > farmland > residential land. Multivariate statistical analysis divided the six PTEs into three groups by source: Zn and As both originated from industrial activities; vehicle emissions were the main source of Pb; and Ni and Cu were derived from natural parent materials. Meanwhile, Cr was found to come from a mixture of artificial and natural sources. The soil environment in the study area faced ecological risk from moderate pollution levels mainly contributed by As. PTEs did not pose a non-carcinogenic risk to humans; however, residents of the three land use types all faced estimated carcinogenic risks caused by Cr, and As in industrial land also posed high estimated carcinogenic risk to human health. The conclusion of this article provides corresponding data support to the government's policy formulation of remediating different types of land and preventing exposure and related environmental risks. [ABSTRACT FROM AUTHOR]

Drought vulnerability analysis of crops can build a bridge between hazard factors and disasters and become the main tool to mitigate the impact of drought. However, the resulting disagreement about the appropriate definition of vulnerability is a frequent cause for misunderstanding and a challenge for attempts to develop formal models of vulnerability. This paper presents a generally applicable conceptual framework of vulnerability that combines a nomenclature of vulnerable situations and a terminology of vulnerability based on the definition in the intergovernmental panel on climate change (IPCC) report. By selecting 10 indicators, the drought disaster vulnerability assessment model is established from four aspects. In order to verify our model, we present a case study of maize drought vulnerability in the Midwest of the Jilin Province. Our analysis reveals the relationship between each single factor evaluation indicator and drought vulnerability, as well as each indicator to every other indicator. The results show that the drought disturbing degree in different growth periods increases from the central part of the Jilin Province to the western part of the Jilin Province. The sensitivity degree showed an increasing trend from the southeast to the northwest. The places with the strongest self-recovery ability are mainly concentrated in Changchun, Siping, Baicheng, and the other area. The ability to adjust to drought in each growth period is weak and crop yield reduction caused by drought is easy to create. Environmental adaptability is closely related to the social and economic situation every year, so it changes greatly and is flexible. Areas with strong drought vulnerability are mainly concentrated in Baicheng, Tongyu, and Qianguo. The research results can provide a certain basis for risk assessment, early warning, and disaster prevention and mitigation of agricultural drought disaster in the research area. [ABSTRACT FROM AUTHOR]

As an important component of urban disaster prevention and mitigation systems, the balance and equity of emergency shelter distribution can be measured based on spatial accessibility utilizing the two-step floating catchment area (2SFCA) method. However, there are some issues in previous studies on emergency shelter accessibility evaluated by the 2SFCA method: (1) the high discretization of population distribution data and the travel cost being measured base on Euclidean distance; (2) ignoring the difference between shelter and population catchment sizes. To address these issues, we propose an improved 2SFCA method that computes the shelter and population catchments respectively to evaluate the emergency shelter accessibility in Changchun, China. We compare the proposed improved 2SFCA method to the original 2SFCA method. The results indicate that the catchment size and shelter accessibility calculated by the proposed method are more realistic and objective. The improved 2SFCA method is applicable method for evaluating the shelter accessibility and can provide advice for the planning and management of emergency shelters in the future. [ABSTRACT FROM AUTHOR]

In order to develop film electrodes for the surface acoustic wave (SAW) devices operating in harsh high-temperature environments, novel Al₂O₃/Pt/ZnO/Al₂O₃ multilayered film electrodes were prepared by laser molecular beam epitaxy (LMBE) at 150 °C. The first Al₂O₃ layer was used as a barrier layer to prevent the diffusion of Ga, La, and Si atoms from the La₃Ga₅SiO 14 (LGS) substrate to the film electrode and thus improved the crystalline quality of ZnO and Pt films. It was found that the resistance of the Al₂O₃/Pt/ZnO/Al₂O₃ electrode did not vary up to a temperature of 1150 °C, suggesting a high reliability of electrode under harsh high-temperature environments. The mechanism of the stable resistance of the Al₂O₃/Pt/ZnO/Al₂O₃ film electrodes at high temperature was investigated by analyzing its microstructure. The proposed Al₂O₃/Pt/ZnO/Al₂O₃ film electrode has great potential for application in high-temperature SAW devices., Competing Interests: The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

In order to develop a film electrode for the surface acoustic wave (SAW) devices working in high temperature, harsh environments, novel AlN/Pt/ZnO multilayers were prepared using pulsed laser deposition (PLD) systems on langasite (LGS) substrates. The AlN film was used as a protective layer and the ZnO buffer layer was introduced to improve the crystal quality of Pt films. The results show that the resistances of Pt and AlN/Pt film electrodes violently increase above 600 °C and 800 °C, respectively, while the resistances of AlN/Pt/ZnO electrodes have more stable electrical resistance from room temperature to 1000 °C. The AlN/Pt/ZnO electrode, where the ZnO film was deposited at 600 °C, has the best temperature stability and can steadily work for 4 h at 1000 °C. The mechanism underlying the stable resistance of the AlN/Pt/ZnO electrode at a high temperature was investigated by analyzing the microstructure of the prepared samples. The proposed AlN/Pt/ZnO film electrode has great potential for applications in high temperature SAW sensors.

Floods are a devastating kind of natural disaster. About half of the population in China lives in rural areas. Therefore, it is necessary to assess the flood disaster risk of rural housings. The results are valuable for guiding the rescue and relief goods layout. In this study, we take the severe flood disaster that happened at Kouqian Town in Jilin, China in 2010 as an example to build an risk assessment system for flood disaster on rural housings. Based on the theory of natural disaster risk formation and "3S" technology (remote sensing, geography information systems and global positioning systems), taking the rural housing as the bearing body, we assess the flood disaster risk from three aspects: hazard, exposure and vulnerability. The hazard presented as the flood submerging range and depth. The exposure presented as the values of the housing and the property in it. The vulnerability presented as the relationship between the losses caused by flood and flood depth. We validate the model by the field survey after the flood disaster. The risk assessment results highly coincide with the field survey losses. This model can be used to assess the risk of other flood events in this area.

In this study, an experiment was performed to assess the trip difficulty for urban residents of different age groups walking in various depths of water, and the data were corroborated with the real urban rainstorm waterlogging scenarios in downtown (Daoli district) Ha-Erbin (China). Mathematical models of urban rainstorm waterlogging were constructed using scenario simulation methods, aided by the GIS spatial analysis technology and hydrodynamic analysis of the waterway systems in the study area. Then these models were used to evaluate the impact of waterlogging on the safety of residents walking in the affected area. Results are summarized as: (1) for an urban rainstorm waterlogging scenario reoccurring once every 10 years, three grid regions would have waterlogging above 0.5 m moving at a velocity of 1.5 m/s. Under this scenario, waterlogging would accumulate on traffic roads only in small areas, affecting the safety and mobility of residents walking in the neighborhood; (2) for an urban rainstorm waterlogging scenario reoccurring once every 20 years, 13 grids experienced the same waterlogging situation affecting a larger area of the city; (3) for an urban rainstorm waterlogging scenario reoccurring once every 50 years, 86 grid regions were affected (waterlogging above 0.5 m moving at 1.5 m/s), and those areas would become impassable for residents.