In recent years,many adversarial malware examples with different feature strategies,especially GAN and its variants,have been introduced to handle the security threats,e.g.,evading the detection of machine learning de...In recent years,many adversarial malware examples with different feature strategies,especially GAN and its variants,have been introduced to handle the security threats,e.g.,evading the detection of machine learning detectors.However,these solutions still suffer from problems of complicated deployment or long running time.In this paper,we propose an n-gram MalGAN method to solve these problems.We borrow the idea of n-gram from the Natural Language Processing(NLP)area to expand feature sources for adversarial malware examples in MalGAN.Generally,the n-gram MalGAN obtains the feature vector directly from the hexadecimal bytecodes of the executable file.It can be implemented easily and conveniently with a simple program language(e.g.,C++),with no need for any prior knowledge of the executable file or any professional feature extraction tools.These features are functionally independent and thus can be added to the non-functional area of the malicious program to maintain its original executability.In this way,the n-gram could make the adversarial attack easier and more convenient.Experimental results show that the evasion rate of the n-gram MalGAN is at least 88.58%to attack different machine learning algorithms under an appropriate group rate,growing to even 100%for the Random Forest algorithm.展开更多
Although China has achieved great advancements toward national food security,the country is still confronted with a range of challenges,including natural resource stress,imbalanced diets and environmental pollution.Op...Although China has achieved great advancements toward national food security,the country is still confronted with a range of challenges,including natural resource stress,imbalanced diets and environmental pollution.Optimized management of crop–livestock systems is the key measure to realize agricultural green transformation.However,optimized management of crop–livestock systems that use multi-objective zoning is lacking.This study employed a multi-objective zoning management approach to comprehensively analyze four indicators:ammonia volatilization,nitrogen surplus,soil carrying capacity and ecological red line area.With its significant ecological integrity and a strong emphasis on sustainability,the Baiyangdian Basin serves as a unique and suitable test case for conducting analyses on multi-objective nutrient optimization management,with the aim to facilitate the agricultural green transformation.This study finds that less than 8%of the area in the Baiyangdian Basin meet the acceptable environmental indicator standard,whereas around 50%of the area that had both nitrogen surplus and ammonia volatilization exceeded the threshold.Implementation of unified management,that is,the same management technique across the study areas,could result in an increase of areas meeting environmental indicator thresholds to 21.1%.This project developed a novel multi-indicator partition optimization method,in which distinct measures are tailored for different areas to satisfy multiple environmental indicators.Implementation of this method,could potentially bring more than 50%area below the threshold,and areas with ammonia emissions and nitrogen surplus could be reduced to 15.8%.The multi-indicators partition optimization method represents a more advanced and efficiency-oriented management approach when compared to unified management.This approach could be regarded as the best available option to help China achieve agricultural transformation to improve efficient production and reduce environmental pollution.It is recommended that current policies aimed at nutrient management toward sustainable agricultural development should shift toward the application of multi-indicators partition optimization.展开更多
While agricultural green development(AGD)is highly recognized and has become a national strategy in China,it is imperative to bridge the knowledge gaps between AGD and the UN Sustainable Development Goals(SDGs),and to...While agricultural green development(AGD)is highly recognized and has become a national strategy in China,it is imperative to bridge the knowledge gaps between AGD and the UN Sustainable Development Goals(SDGs),and to evaluate the contribution of AGD to meeting the SDGs.The first aim of this study was to compare the AGD goals and indicators with those of the SDGs so as to identify their relationship.The next aim was to examine the historical evolution of AGD indicators and analyze the gaps between the current status of various indicators and their benchmarks.Limiting factors were identified in China's transition toward AGD.These findings reveal that the indicators of AGD align with those of the SDGs,but have greater specificity to the context in China and are more quantifiable.There has been a significant increase per capita calorie and protein intakes in China,as well as a notable rise in agricultural output per unit of arable land and rural incomes from 1980 to the 2010s.However,these achievements have been accompanied by a high resource use and environmental pollution,highlighting the need for a more sustainable,environmentally responsible agriculture in China.展开更多
Africa has experienced increasing aridity and higher frequency of droughts due to climate change during the half past century with possible adverse effects on agricultural production,especially in dry areas with low r...Africa has experienced increasing aridity and higher frequency of droughts due to climate change during the half past century with possible adverse effects on agricultural production,especially in dry areas with low rainfall.Under the auspices of the Africa Water Action Program between the Chinese Ministry of Science and Technology(MOST)and the United Nations Environment Program(UNEP),the Institute of Agricultural Environment and Resources,Shanxi Academy of Agricultural Sciences(SAAS-IAER)worked closely with domestic and overseas partners on technology transfer in Morocco,Zambia,Egypt,Niger and Ethiopia from 2008 to 2013.A drought early warning system has been established and validated,and drought adaptation technologies have been trialed,modified,demonstrated and extended in African countries,and this shows great potential to increase crop production,water and fertilizer use efficiency and desert control in rainfed areas of Africa.The project has continued for six years and is a successful case of technology transfer and capacity building in Africa.The knowledge and experience gained will be useful to researchers,technicians,aid agencies and policy makers who work on agricultural technology transfer for in dry areas of Africa.展开更多
基金supported in part by National Natural Science Foundation of China(No.61802383)Research Project of Pazhou Lab for Excellent Young Scholars(No.PZL2021KF0024)+3 种基金Guangzhou Science and Technology Project Basic Research Plan(No.202201010330,202201020162)Guangdong Philosophy and Social Science Planning Project(No.GD19YYJ02)Research on the Supporting Technologies of the Metaverse in Cultural Media(No.PT252022039)National Undergraduate Training Platform for Innovation and Entrepreneurship(No.202111078029).
文摘In recent years,many adversarial malware examples with different feature strategies,especially GAN and its variants,have been introduced to handle the security threats,e.g.,evading the detection of machine learning detectors.However,these solutions still suffer from problems of complicated deployment or long running time.In this paper,we propose an n-gram MalGAN method to solve these problems.We borrow the idea of n-gram from the Natural Language Processing(NLP)area to expand feature sources for adversarial malware examples in MalGAN.Generally,the n-gram MalGAN obtains the feature vector directly from the hexadecimal bytecodes of the executable file.It can be implemented easily and conveniently with a simple program language(e.g.,C++),with no need for any prior knowledge of the executable file or any professional feature extraction tools.These features are functionally independent and thus can be added to the non-functional area of the malicious program to maintain its original executability.In this way,the n-gram could make the adversarial attack easier and more convenient.Experimental results show that the evasion rate of the n-gram MalGAN is at least 88.58%to attack different machine learning algorithms under an appropriate group rate,growing to even 100%for the Random Forest algorithm.
基金funded by the National Key R&D Program of China(2021YFE0101900)the Key R&D Program of Hebei,China(21327507D)the National Natural Science Foundation of China(32002138,T2222016,31972517)。
文摘Although China has achieved great advancements toward national food security,the country is still confronted with a range of challenges,including natural resource stress,imbalanced diets and environmental pollution.Optimized management of crop–livestock systems is the key measure to realize agricultural green transformation.However,optimized management of crop–livestock systems that use multi-objective zoning is lacking.This study employed a multi-objective zoning management approach to comprehensively analyze four indicators:ammonia volatilization,nitrogen surplus,soil carrying capacity and ecological red line area.With its significant ecological integrity and a strong emphasis on sustainability,the Baiyangdian Basin serves as a unique and suitable test case for conducting analyses on multi-objective nutrient optimization management,with the aim to facilitate the agricultural green transformation.This study finds that less than 8%of the area in the Baiyangdian Basin meet the acceptable environmental indicator standard,whereas around 50%of the area that had both nitrogen surplus and ammonia volatilization exceeded the threshold.Implementation of unified management,that is,the same management technique across the study areas,could result in an increase of areas meeting environmental indicator thresholds to 21.1%.This project developed a novel multi-indicator partition optimization method,in which distinct measures are tailored for different areas to satisfy multiple environmental indicators.Implementation of this method,could potentially bring more than 50%area below the threshold,and areas with ammonia emissions and nitrogen surplus could be reduced to 15.8%.The multi-indicators partition optimization method represents a more advanced and efficiency-oriented management approach when compared to unified management.This approach could be regarded as the best available option to help China achieve agricultural transformation to improve efficient production and reduce environmental pollution.It is recommended that current policies aimed at nutrient management toward sustainable agricultural development should shift toward the application of multi-indicators partition optimization.
基金financially supported by the National Natural Science Foundation of China(31972517)Key R&D Program of Hebei,China(21327507D)。
文摘While agricultural green development(AGD)is highly recognized and has become a national strategy in China,it is imperative to bridge the knowledge gaps between AGD and the UN Sustainable Development Goals(SDGs),and to evaluate the contribution of AGD to meeting the SDGs.The first aim of this study was to compare the AGD goals and indicators with those of the SDGs so as to identify their relationship.The next aim was to examine the historical evolution of AGD indicators and analyze the gaps between the current status of various indicators and their benchmarks.Limiting factors were identified in China's transition toward AGD.These findings reveal that the indicators of AGD align with those of the SDGs,but have greater specificity to the context in China and are more quantifiable.There has been a significant increase per capita calorie and protein intakes in China,as well as a notable rise in agricultural output per unit of arable land and rural incomes from 1980 to the 2010s.However,these achievements have been accompanied by a high resource use and environmental pollution,highlighting the need for a more sustainable,environmentally responsible agriculture in China.
基金We gratefully acknowledge funding from the Technological Assistance Program of MOST to Developing Countries(KY201904003)the International Cooperation Program of Shanxi Key R&D Program(201903D421001)+2 种基金International Cooperation Program“Africa Water Action”between MOST and UNEP(2010DFA92860)Shanxi Key R&D Program(201803D221011-1)the S&T Innovation Program of Shanxi Academy of Agricultural Sciences(YCX2018DZYX16).
文摘Africa has experienced increasing aridity and higher frequency of droughts due to climate change during the half past century with possible adverse effects on agricultural production,especially in dry areas with low rainfall.Under the auspices of the Africa Water Action Program between the Chinese Ministry of Science and Technology(MOST)and the United Nations Environment Program(UNEP),the Institute of Agricultural Environment and Resources,Shanxi Academy of Agricultural Sciences(SAAS-IAER)worked closely with domestic and overseas partners on technology transfer in Morocco,Zambia,Egypt,Niger and Ethiopia from 2008 to 2013.A drought early warning system has been established and validated,and drought adaptation technologies have been trialed,modified,demonstrated and extended in African countries,and this shows great potential to increase crop production,water and fertilizer use efficiency and desert control in rainfed areas of Africa.The project has continued for six years and is a successful case of technology transfer and capacity building in Africa.The knowledge and experience gained will be useful to researchers,technicians,aid agencies and policy makers who work on agricultural technology transfer for in dry areas of Africa.
