Despite prevailing interests,no rigorous research has been conducted to examine the role of nature in natural-hazard preparedness.This systematic review aimed to describe how nature can reduce the impacts of natural h...Despite prevailing interests,no rigorous research has been conducted to examine the role of nature in natural-hazard preparedness.This systematic review aimed to describe how nature can reduce the impacts of natural hazards during the preparedness stage.The study focuses on the land,water,and air systems and on three types of stakeholders:international organizations,developed countries,and developing countries.Further,it provides supplementary strategies,such as immediate actions,local engagement,and research and development,that the stakeholders should apply to enhance their nature-based natural-hazard preparedness.We suggest integrating costs and benefits analysis,local culture,societal challenges,and environmental justice into the implementation of nature-based solutions.Finally,this review outlines the framework of nature-based natural-hazard preparedness by discussing the relationship between nature and society.展开更多
Safeguarding against malware requires precise machine-learning algorithms to classify harmful apps.The Drebin dataset of 15,036 samples and 215 features yielded significant and reliable results for two hybrid models,C...Safeguarding against malware requires precise machine-learning algorithms to classify harmful apps.The Drebin dataset of 15,036 samples and 215 features yielded significant and reliable results for two hybrid models,CNN+XGBoost and KNN+XGBoost.To address the class imbalance issue,SMOTE(Synthetic Minority Oversampling Technique)was used to preprocess the dataset,creating synthetic samples of the minority class(malware)to balance the training set.XGBoost was then used to choose the most essential features for separating malware from benign programs.The models were trained and tested using 6-fold cross-validation,measuring accuracy,precision,recall,F1 score,and ROC AUC.The results are highly dependable,showing that CNN+XGBoost consistently outperforms KNN+XGBoost with an average accuracy of 98.76%compared to 97.89%.The CNN-based malware classification model,with its higher precision,recall,and F1 scores,is a secure choice.CNN+XGBoost,with its fewer all-fold misclassifications in confusion matrices,further solidifies this security.The calibration curve research,confirming the accuracy and cybersecurity applicability of the models’probability projections,adds to the sense of reliability.This study unequivocally demonstrates that CNN+XGBoost is a reliable and effective malware detection system,underlining the importance of feature selection and hybrid models.展开更多
Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small obje...Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small object detection a complex and demanding task.One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities.This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations,such as Hyperspectral-Multispectral(HSMS),Hyperspectral-Synthetic Aperture Radar(HS-SAR),and HS-SAR-Digital Surface Model(HS-SAR-DSM).The detection process is done by the proposed Jaccard Deep Q-Net(JDQN),which integrates the Jaccard similarity measure with a Deep Q-Network(DQN)using regression modeling.To produce the final output,a Deep Maxout Network(DMN)is employed to fuse the detection results obtained from each modality.The effectiveness of the proposed JDQN is validated using performance metrics,such as accuracy,Mean Squared Error(MSE),precision,and Root Mean Squared Error(RMSE).Experimental results demonstrate that the proposed JDQN method outperforms existing approaches,achieving the highest accuracy of 0.907,a precision of 0.904,the lowest normalized MSE of 0.279,and a normalized RMSE of 0.528.展开更多
In this study,a set of coupled multi-media compartments(i.e.,sediment,soil,water and vegetable)was used to assess ecological and health risks from the ingestion of 11 PTEs(Pb,Cd,Cr,As,Hg,Cu,Zn,Ni,Co,Fe,and Mn)and thei...In this study,a set of coupled multi-media compartments(i.e.,sediment,soil,water and vegetable)was used to assess ecological and health risks from the ingestion of 11 PTEs(Pb,Cd,Cr,As,Hg,Cu,Zn,Ni,Co,Fe,and Mn)and their transportation routes in the water-soil-plant system from the coastal Bhola Island,Bangladesh.The mean concentrations of Cd,Pb,and Co for soil and Cd,Co,and As for sediment were higher than their reference values.In contrast,Cd,Pb,and Ni concentrations in water surpassed the acceptable limits set by national and international laws and were considered unsuitable for drinking purposes.Vegetables demonstrated high Pb and Cd contents,demonstrating a potential food safety risk to the inhabitants.Results of principal component analysis(PCA)revealed that Cd,Pb,Hg,Cu,Ni and Zn sources were likely to be anthropogenic,especially agro-farming inputs,whereas the Fe,As,Cr,Mn,and Co sources were similar to natural origin.So,Cd,Pb and Co are the key contaminants in the study area and pose the elevated health and ecological risks in the coastal area.Cd and Pb exhibited higher ecological risks in soils and sediments,as Pb had the highest bio-accessibility(BA;0.02±0.003)and Cd possessed a high bioaccumulation factor(BCF;0.004±0.006).The self-organizing map analysis recognized three spatial patterns which are good agreement with PCA.The average hazard index(HI)values for soil were above the permissible level(HI>1)set by the respective agency;two times higher HI values were noticed for children than adults,suggesting children are highly susceptible to health risk.Continuous monitoring and source controls for Cd and Pb,along with agro-farming management practices,need to be implemented to reduce the risk of PTE contamination to the aquatic ecosystem and its inhabitants.展开更多
文摘Despite prevailing interests,no rigorous research has been conducted to examine the role of nature in natural-hazard preparedness.This systematic review aimed to describe how nature can reduce the impacts of natural hazards during the preparedness stage.The study focuses on the land,water,and air systems and on three types of stakeholders:international organizations,developed countries,and developing countries.Further,it provides supplementary strategies,such as immediate actions,local engagement,and research and development,that the stakeholders should apply to enhance their nature-based natural-hazard preparedness.We suggest integrating costs and benefits analysis,local culture,societal challenges,and environmental justice into the implementation of nature-based solutions.Finally,this review outlines the framework of nature-based natural-hazard preparedness by discussing the relationship between nature and society.
文摘Safeguarding against malware requires precise machine-learning algorithms to classify harmful apps.The Drebin dataset of 15,036 samples and 215 features yielded significant and reliable results for two hybrid models,CNN+XGBoost and KNN+XGBoost.To address the class imbalance issue,SMOTE(Synthetic Minority Oversampling Technique)was used to preprocess the dataset,creating synthetic samples of the minority class(malware)to balance the training set.XGBoost was then used to choose the most essential features for separating malware from benign programs.The models were trained and tested using 6-fold cross-validation,measuring accuracy,precision,recall,F1 score,and ROC AUC.The results are highly dependable,showing that CNN+XGBoost consistently outperforms KNN+XGBoost with an average accuracy of 98.76%compared to 97.89%.The CNN-based malware classification model,with its higher precision,recall,and F1 scores,is a secure choice.CNN+XGBoost,with its fewer all-fold misclassifications in confusion matrices,further solidifies this security.The calibration curve research,confirming the accuracy and cybersecurity applicability of the models’probability projections,adds to the sense of reliability.This study unequivocally demonstrates that CNN+XGBoost is a reliable and effective malware detection system,underlining the importance of feature selection and hybrid models.
文摘Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue.However,low resolution,occlusion,and background interference make small object detection a complex and demanding task.One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities.This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations,such as Hyperspectral-Multispectral(HSMS),Hyperspectral-Synthetic Aperture Radar(HS-SAR),and HS-SAR-Digital Surface Model(HS-SAR-DSM).The detection process is done by the proposed Jaccard Deep Q-Net(JDQN),which integrates the Jaccard similarity measure with a Deep Q-Network(DQN)using regression modeling.To produce the final output,a Deep Maxout Network(DMN)is employed to fuse the detection results obtained from each modality.The effectiveness of the proposed JDQN is validated using performance metrics,such as accuracy,Mean Squared Error(MSE),precision,and Root Mean Squared Error(RMSE).Experimental results demonstrate that the proposed JDQN method outperforms existing approaches,achieving the highest accuracy of 0.907,a precision of 0.904,the lowest normalized MSE of 0.279,and a normalized RMSE of 0.528.
文摘In this study,a set of coupled multi-media compartments(i.e.,sediment,soil,water and vegetable)was used to assess ecological and health risks from the ingestion of 11 PTEs(Pb,Cd,Cr,As,Hg,Cu,Zn,Ni,Co,Fe,and Mn)and their transportation routes in the water-soil-plant system from the coastal Bhola Island,Bangladesh.The mean concentrations of Cd,Pb,and Co for soil and Cd,Co,and As for sediment were higher than their reference values.In contrast,Cd,Pb,and Ni concentrations in water surpassed the acceptable limits set by national and international laws and were considered unsuitable for drinking purposes.Vegetables demonstrated high Pb and Cd contents,demonstrating a potential food safety risk to the inhabitants.Results of principal component analysis(PCA)revealed that Cd,Pb,Hg,Cu,Ni and Zn sources were likely to be anthropogenic,especially agro-farming inputs,whereas the Fe,As,Cr,Mn,and Co sources were similar to natural origin.So,Cd,Pb and Co are the key contaminants in the study area and pose the elevated health and ecological risks in the coastal area.Cd and Pb exhibited higher ecological risks in soils and sediments,as Pb had the highest bio-accessibility(BA;0.02±0.003)and Cd possessed a high bioaccumulation factor(BCF;0.004±0.006).The self-organizing map analysis recognized three spatial patterns which are good agreement with PCA.The average hazard index(HI)values for soil were above the permissible level(HI>1)set by the respective agency;two times higher HI values were noticed for children than adults,suggesting children are highly susceptible to health risk.Continuous monitoring and source controls for Cd and Pb,along with agro-farming management practices,need to be implemented to reduce the risk of PTE contamination to the aquatic ecosystem and its inhabitants.
