A novel flood risk management approach based on future climate and land use change scenarios.

Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools - particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS.

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries.

Electrically gated molecular thermal switch.

Controlling heat flow is a key challenge for applications ranging from thermal management in electronics to energy systems, industrial processing, and thermal therapy. However, progress has generally been limited by slow response times and low tunability in thermal conductance. In this work, we demonstrate an electronically gated solid-state thermal switch using self-assembled molecular junctions to achieve excellent performance at room temperature. In this three-terminal device, heat flow is continuously and reversibly modulated by an electric field through carefully controlled chemical bonding and charge distributions within the molecular interface. The devices have ultrahigh switching speeds above 1 megahertz, have on/off ratios in thermal conductance greater than 1300%, and can be switched more than 1 million times. We anticipate that these advances will generate opportunities in molecular engineering for thermal management systems and thermal circuit design.

Exploring the Multitarget Activity of Wedelolactone against Alzheimer's Disease: Insights from In Silico Study.

In this study, Wedelolactone's multitarget activity against Alzheimer's disease was examined using density functional theory and molecular docking techniques. At physiological pH, the pK a and molar fractions have been estimated. The most likely relative rate constants of two radical scavenger mechanisms are formal hydrogen transfer in a lipid environment and single-electron transfer in a water solvent. Compared to Trolox (k overall = 8.96 × 104 M-1 s-1), Wedelolactone (k overall = 4.26 × 109 M-1 s-1) is more efficient in scavenging the HOO• radical in an aqueous environment. The chelation capacity of metals was investigated by examining the complexation of the Cu(II) ion at various coordination positions and calculating the complexation kinetic constants. Furthermore, molecular docking simulations showed that the known forms of Wedelolactone at physiological pH effectively inhibited the AChE and BChE enzymes by comparing their activity to that of tacrine (control). Wedelolactone is a promising drug candidate for Alzheimer's disease therapy in light of these findings.

Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam's Mekong River Delta.

Soil salinization is considered one of the disasters that have significant effects on agricultural activities in many parts of the world, particularly in the context of climate change and sea level rise. This problem has become increasingly essential and severe in the Mekong River Delta of Vietnam. Therefore, soil salinity monitoring and assessment are critical to building appropriate strategies to develop agricultural activities. This study aims to develop a low-cost method based on machine learning and remote sensing to map soil salinity in Ben Tre province, which is located in Vietnam's Mekong River Delta. This objective was achieved by using six machine learning algorithms, including Xgboost (XGR), sparrow search algorithm (SSA), bird swarm algorithm (BSA), moth search algorithm (MSA), Harris hawk optimization (HHO), grasshopper optimization algorithm (GOA), particle swarm optimization algorithm (PSO), and 43 factors extracted from remote sensing images. Various indices were used, namely, root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2) to estimate the efficiency of the prediction models. The results show that six optimization algorithms successfully improved XGR model performance with an R2 value of more than 0.98. Among the proposed models, the XGR-HHO model was better than the other models with a value of R2 of 0.99 and a value of RMSE of 0.051, by XGR-GOA (R2 = 0.931, RMSE = 0.055), XGR-MSA (R2 = 0.928, RMSE = 0.06), XGR-BSA (R2 = 0.926, RMSE = 0.062), XGR-SSA (R2 = 0.917, 0.07), XGR-PSO (R2 = 0.916, RMSE = 0.08), XGR (R2 = 0.867, RMSE = 0.1), CatBoost (R2 = 0.78, RMSE = 0.12), and RF (R2 = 0.75, RMSE = 0.19), respectively. These proposed models have surpassed the reference models (CatBoost and random forest). The results indicated that the soils in the eastern areas of Ben Tre province are more saline than in the western areas. The results of this study highlighted the effectiveness of using hybrid machine learning and remote sensing in soil salinity monitoring. The finding of this study provides essential tools to support farmers and policymakers in selecting appropriate crop types in the context of climate change to ensure food security.

Higher tacrolimus trough levels and time in the therapeutic range are associated with the risk of acute rejection in the first month after renal transplantation.

BACKGROUND: Tacrolimus trough levels (C0) are used in most transplant centres for therapeutic drug monitoring (TDM) of tacrolimus (Tac). The target range of Tac C0 has been remarkably changed, with a target as low as 3-7 ng/ml in the 2009 European consensus conference and a target of 4-12 ng/ml (preferably to 7-12 ng/ml) following the second consensus report in 2019. Our aim was to investigate whether reaching early Tac therapeutic targets and maintaining time in the therapeutic range (TTR) according to the new recommendations may be necessary for preventing acute rejection (AR) during the first month after transplantation. METHODS: A retrospective study including 160 adult renal transplant patients (113 men and 47 women) with a median age of 36.3 (20-44) years was conducted between January 2018 and December 2019 at 103 Military Hospital (Vietnam). Tac trough levels were recorded in the first month, and episodes of AR were confirmed by kidney biopsy. Tac TTR was calculated as the percentage of time within the target range of 7-12 ng/ml, according to the 2019 second consensus report. Multivariate Cox analysis was performed to identify the correlation between the Tac target range and TTR with AR. RESULTS: In the first month after RT, 14 (8.8%) patients experienced AR. There was a significant difference in the incidence of AR between the Tac level groups of < 4, 4-7 and > 7 ng/ml (p = 0.0096). In the multivariate Cox analysis, after adjusting for related factors, a mean Tac level > 7 ng/ml was associated with an 86% decreased risk of AR compared with that of 4-7 ng/ml in the first month (HR, 0.14; 95% CI, 0.03-0.66; p = 0.0131). Every 10% increase in TTR was associated with a 28% lower risk of AR (HR, 0.72; 95% CI, 0.55-0.94; p = 0.014). CONCLUSION: Gaining and maintaining Tac C0 according to the 2019 second consensus report might reduce the risk of AR in the first month following transplantation.

Application of deep learning models to detect coastlines and shorelines.

Identifying and monitoring coastlines and shorelines play an important role in coastal erosion assessment around the world. The application of deep learning models was used in this study to detect coastlines and shorelines in Vietnam using high-resolution satellite images and different object segmentation methods. The aims are to (1) propose indicators to identify coastlines and shorelines; (2) build deep learning (DL) models to automatically interpret coastlines and shorelines from high-resolution remote sensing images; and (3) apply DL-trained models to monitor coastal erosion in Vietnam. Eight DL models were trained based on four artificial-intelligent-network structures, including U-Net, U2-Net, U-Net3+, and DexiNed. The high-resolution images collected from Google Earth Pro software were used as input data for training all models. As a result, the U-Net using an input-image size of 512 × 512 provides the highest performance of 98% with a loss function of 0.16. The interpretation results of this model were used effectively for the coastline and shoreline identification in assessing coastal erosion in Vietnam due to sea-level rise in storm events over 20 years. The outcomes proved that while the shoreline is ideal for observing seasonal tidal changes or the immediate motions of current waves, the coastline is suitable to assess coastal erosion caused by the influence of sea-level rise during storms. This paper has provided a broad scope of how the U-Net model can be used to predict the coastal changes over vietnam and the world.

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms.

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam.

: The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

Anisotropic Thermal Boundary Resistance across 2D Black Phosphorus: Experiment and Atomistic Modeling of Interfacial Energy Transport.

Interfacial thermal boundary resistance (TBR) plays a critical role in near-junction thermal management of modern electronics. In particular, TBR can dominate heat dissipation and has become increasingly important due to the continuous emergence of novel nanomaterials with promising electronic and thermal applications. A highly anisotropic TBR across a prototype 2D material, i.e., black phosphorus, is reported through a crystal-orientation-dependent interfacial transport study. The measurements show that the metal-semiconductor TBR of the cross-plane interfaces is 241% and 327% as high as that of the armchair and zigzag direction-oriented interfaces, respectively. Atomistic ab initio calculations are conducted to analyze the anisotropic and temperature-dependent TBR using density functional theory (DFT)-derived full phonon dispersion relation and molecular dynamics simulation. The measurement and modeling work reveals that such a highly anisotropic TBR can be attributed to the intrinsic band structure and phonon spectral transmission. Furthermore, it is shown that phonon hopping between different branches is important to modulate the interfacial transport process but with directional preferences. A critical fundamental understanding of interfacial thermal transport and TBR-structure relationships is provided, which may open up new opportunities in developing advanced thermal management technology through the rational control over nanostructures and interfaces.

High-performance field emission based on nanostructured tin selenide for nanoscale vacuum transistors.

Vacuum field effect transistors have been envisioned to hold the promise of replacing solid-state electronics when the ballistic transport of electrons in a nanoscale vacuum can enable significantly high switching speed and stability. However, it remains challenging to obtain high-performance and reliable field-emitter materials. In this work, we report a systematic study on the field emission of novel two-dimensional tin selenide (SnSe) with rational design of its structures and surface morphologies. SnSe in the form of atomically smooth single crystals and nanostructures (nanoflowers) is chemically synthesized and studied as field emitters with varying channel lengths from 6 µm to 100 nm. Our study shows that devices based on SnSe nanoflowers significantly improve the performance and enable field emission at a reduced voltage due to a surface-enhanced local electrostatic field, and further lead to nonlinear dependent channel scaling when the channel length is shorter than 600 nm. We measured a record-high short-channel field-enhancement factor of 50 600 for a 100 nm device. Moreover, we investigated the emission stability and measured the fluctuations of the emission current which are smaller than 5% for more than 20 hours. Our results demonstrated a high-performance and highly reliable field emitter based on 2D SnSe nanostructures and we developed an important building block for nanoscale vacuum field effect transistors.