The American critic argues that the 2025 U.S.National Security Strategy,with its isolationist and confrontational approach towards allies and China,is a desperate fiction that undermines genuine American prosperity an...The American critic argues that the 2025 U.S.National Security Strategy,with its isolationist and confrontational approach towards allies and China,is a desperate fiction that undermines genuine American prosperity and security.展开更多
Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,B...Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare I...The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.展开更多
This study examined the role of green energy development in mitigating climate change and fostering sustainable development in Central Asia including Kazakhstan,Uzbekistan,Kyrgyzstan,Tajikistan,and Turkmenistan.The re...This study examined the role of green energy development in mitigating climate change and fostering sustainable development in Central Asia including Kazakhstan,Uzbekistan,Kyrgyzstan,Tajikistan,and Turkmenistan.The region has substantial untapped potential in solar energy,wind energy,hydropower energy,as well as biomass and bioenergy,positioning it strategically for renewable energy deployment.The result demonstrated that integrating renewable energy can reduce greenhouse gas emissions,improve air quality,enhance energy security,and support rural development.Case studies from Kazakhstan,Uzbekistan,Kyrgyzstan,and Tajikistan showed measurable environmental and economic benefits.However,the large-scale use of renewable energy still faces numerous barriers,including outdated infrastructure,fragmented regulatory frameworks,limited investment,and shortages of technical expertise.Overcoming these obstacles requires institutional reform,stronger regional cooperation,and increasing engagement from international financial institutions and private investors.Modernizing grids,deploying storage systems,and investing in education,research,and innovation are critical for building human capacity in renewable energy sector.Accelerating the renewable energy transition is essential for Central Asia to meet climate goals,enhance environmental resilience,and ensure long-term socioeconomic development through innovation,investment,and regional collaboration.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
Black soils represent only one-sixth of the global arable land area but play an important role in maintaining world food security due to their high fertility and gigantic potential for food production.With the ongoing...Black soils represent only one-sixth of the global arable land area but play an important role in maintaining world food security due to their high fertility and gigantic potential for food production.With the ongoing intensification of agricultural practices and negative natural factors,black soils are confronting enhanced degradation.The holistic overview of black soil degradation and the underlying mechanisms for soil health improvement will be key for agricultural sustainability and food security.In this review,the current status and driving factors of soil degradation in the four major black soil regions of the world are summarized,and effective measures for black soil conservation are proposed.The Northeast Plain of China is the research hotspot with 41.5%of the published studies related to black soil degradation,despite its relatively short history of agricultural reclamation,followed by the East European Plain(28.3%),the Great Plains of North America(20.7%),and the Pampas of South American(7.9%).Among the main types of soil degradation,soil erosion and soil fertility decline(especially organic matter loss)have been reported as the most common problems,with 27.6%and 39.4%of the published studies,respectively.In addition to the natural influences of climate and topography,human activities have been reported to have great influences on the degradation of black soils globally.Unsustainable farming practices and excess in agrochemical applications are common factors reported to accelerate the degradation process and threaten the sustainable use of black soils.Global efforts for black soil conservation and utilization should focus on standardizing evaluation criteria including real-time monitoring and the measures of prevention and restoration for sustainable management.International cooperation in technology and policy is crucial for overcoming the challenges and thus achieving the protection,sustainable use,and management of global black soil resources.展开更多
The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.De...The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.展开更多
Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, ...Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.展开更多
ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential sec...ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future directions.Through this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.展开更多
The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by phy...The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.展开更多
This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA f...This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA framework integrates security by design principles, micro-segmentation, and Island Mode Operation (IMO) to enhance cyber resilience and ensure continuous, secure operations. The methodology deploys a Forward-Thinking Architecture Strategy (FTAS) algorithm, which utilises an industrial Intrusion Detection System (IDS) implemented with Python’s Network Intrusion Detection System (NIDS) library. The FTAS algorithm successfully identified and responded to cyber-attacks, ensuring minimal system disruption. ISERA has been validated through comprehensive testing scenarios simulating Denial of Service (DoS) attacks and malware intrusions, at both the IT and OT layers where it successfully mitigates the impact of malicious activity. Results demonstrate ISERA’s efficacy in real-time threat detection, containment, and incident response, thus ensuring the integrity and reliability of critical infrastructure systems. ISERA’s decentralised approach contributes to global net zero goals by optimising resource use and minimising environmental impact. By adopting a decentralised control architecture and leveraging virtualisation, ISERA significantly enhances the cyber resilience and sustainability of critical infrastructure systems. This approach not only strengthens defences against evolving cyber threats but also optimises resource allocation, reducing the system’s carbon footprint. As a result, ISERA ensures the uninterrupted operation of essential services while contributing to broader net zero goals.展开更多
There is a growing recognition of the critical role of security governance in advancing democratic transition in the post-conflict environment.Despite such a recognition,the security sector reform concept has overshad...There is a growing recognition of the critical role of security governance in advancing democratic transition in the post-conflict environment.Despite such a recognition,the security sector reform concept has overshadowed the importance of the overarching strategic role of security governance in transition to democracy,particularly in Africa.This paper assesses the status and challenges facing security governance and how they thwarted the efforts to furthering the democratic transition in South Sudan.The paper shows a deterioration in security,safety and security governance outcomes since the independence of South Sudan in 2011 with such a trend unlikely to be abated in the near future without strategic interventions.Some of the challenges facing security governance in South Sudan include the legacies of some historical events including the“Big Tent Policy”,absence of strategic leadership,lack of overarching policy framework,impractical and tenuous security arrangements in the 2018 peace agreement,persistent postponement of the first elections,and dysfunctional justice sector.The paper provides some strategic and operational recommendations to improve security governance and advance democratic transition in South Sudan.These recommendations include formulation of an inclusive and people-centered national security policy,rigorous judicial reform,and early political agreement on new political infrastructure if conditions for holding the first national elections are not met in 2026.展开更多
The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facili...The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.展开更多
Land use transformations in Sonipat District,Haryana,driven by urbanization,industrialization,and land acquisitions,have posed significant ecological and socio-economic challenges,particularly concerning food security...Land use transformations in Sonipat District,Haryana,driven by urbanization,industrialization,and land acquisitions,have posed significant ecological and socio-economic challenges,particularly concerning food security.This study investigates the interplay between these land use changes and their environmental implications at macro(district)and micro(village)levels,focusing on agricultural productivity and resource sustainability.The study employs a mixed-method approach,integrating secondary data from official datasets and primary data gathered through structured household surveys,focus group discussions,and visual analysis techniques.Data from 20 villages,selected based on predominant land use characteristics,were analysed using statistical and geospatial tools,including ArcGIS and STATA,to quantify food grain losses and evaluate environmental degradation.Findings of this study reveal a 19%reduction in agricultural land over two decades(2000-2024),correlating with increased residential and industrial areas.Groundwater resources face severe overexploitation,with pollution from industrial clusters further degrading water and soil quality.The study estimates a total food grain loss of 1.5 million kilograms across surveyed villages due to land acquisitions.A strong positive correlation(R^(2)=0.98)between land acquisition and food loss underscores the direct impact of urbanization on agricultural output.The research underscores the urgency of sustainable land management practices,including preserving agricultural lands,optimizing groundwater usage,and enhancing community involvement in planning.By addressing these challenges,the study advocates for balanced urban expansion and food security to ensure ecological and economic resilience in the region.展开更多
This study investigates the critical intersection of cyberpsychology and cybersecurity policy development in small and medium-sized enterprises (SMEs). Through a mixed-methods approach incorporating surveys of 523 emp...This study investigates the critical intersection of cyberpsychology and cybersecurity policy development in small and medium-sized enterprises (SMEs). Through a mixed-methods approach incorporating surveys of 523 employees across 78 SMEs, qualitative interviews, and case studies, the research examines how psychological factors influence cybersecurity behaviors and policy effectiveness. Key findings reveal significant correlations between psychological factors and security outcomes, including the relationship between self-efficacy and policy compliance (r = 0.42, p β = 0.37, p < 0.001). The study identifies critical challenges in risk perception, policy complexity, and organizational culture affecting SME cybersecurity implementation. Results demonstrate that successful cybersecurity initiatives require the integration of psychological principles with technical solutions. The research provides a framework for developing human-centric security policies that address both behavioral and technical aspects of cybersecurity in resource-constrained environments.展开更多
Global crop productivity faces a significant threat from climate change-induced drought stress(DS),which is vital for sustainable agriculture and global food security.Uncovering DS adaptation and tolerance mechanisms ...Global crop productivity faces a significant threat from climate change-induced drought stress(DS),which is vital for sustainable agriculture and global food security.Uncovering DS adaptation and tolerance mechanisms in crops is necessary to alleviate climate challenges.Innovative plant breeding demands revolutionary approaches to develop stress-smart plants.Metabolomics,a promising field in plant breeding,offers a predictive tool to identify metabolic markers associated with plant performance under DS,enabling accelerated crop improvement.Central to DS adaptation is metabolomics-driven metabolic regulation,which is critical for maintaining cell osmotic potential in crops.Recent innovations allow rapid mapping of specific metabolites to their genetic pathways,providing a valuable resource for plant scientists.Metabolomics-driven molecular breeding,integrating techniques such as mQTL and mGWAS,enhances our ability to discover key genetic elements linked to stress-responsive metabolites.This integration offers a beneficial platform for plant scientists,yielding significant insights into the complex metabolic networks underlying DS tolerance.Therefore,this review discusses(1)insights into metabolic regulation for DS adaptation,(2)the multifaceted role of metabolites in DS tolerance and nutritional/yield trait improvement,(3)the potential of single-cell metabolomics and imaging,(4)metabolomics-driven molecular breeding,and(5)the application of metabolic and genetic engineering for DS-tolerant crops.We finally propose that the metabolomics-driven approach positions drought-smart crops as key contributors to future food production,supporting the vital goal of achieving“zero hunger”.展开更多
The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,dist...The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.展开更多
文摘The American critic argues that the 2025 U.S.National Security Strategy,with its isolationist and confrontational approach towards allies and China,is a desperate fiction that undermines genuine American prosperity and security.
文摘Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
文摘The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.
文摘This study examined the role of green energy development in mitigating climate change and fostering sustainable development in Central Asia including Kazakhstan,Uzbekistan,Kyrgyzstan,Tajikistan,and Turkmenistan.The region has substantial untapped potential in solar energy,wind energy,hydropower energy,as well as biomass and bioenergy,positioning it strategically for renewable energy deployment.The result demonstrated that integrating renewable energy can reduce greenhouse gas emissions,improve air quality,enhance energy security,and support rural development.Case studies from Kazakhstan,Uzbekistan,Kyrgyzstan,and Tajikistan showed measurable environmental and economic benefits.However,the large-scale use of renewable energy still faces numerous barriers,including outdated infrastructure,fragmented regulatory frameworks,limited investment,and shortages of technical expertise.Overcoming these obstacles requires institutional reform,stronger regional cooperation,and increasing engagement from international financial institutions and private investors.Modernizing grids,deploying storage systems,and investing in education,research,and innovation are critical for building human capacity in renewable energy sector.Accelerating the renewable energy transition is essential for Central Asia to meet climate goals,enhance environmental resilience,and ensure long-term socioeconomic development through innovation,investment,and regional collaboration.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
基金funded by the Science and Technology Plan for the Belt and Road Innovation Cooperation Project of Jiangsu Province,China(No.BZ2023003)the National Key Research and Development Program of China(No.2021YFD1500202)+2 种基金the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA28010100)the“14th Five-Year Plan”Self-Deployment Project of the Institute of Soil Science,Chinese Academy of Sciences(No.ISSAS2418)the National Natural Science Foundation of China(No.42107334)。
文摘Black soils represent only one-sixth of the global arable land area but play an important role in maintaining world food security due to their high fertility and gigantic potential for food production.With the ongoing intensification of agricultural practices and negative natural factors,black soils are confronting enhanced degradation.The holistic overview of black soil degradation and the underlying mechanisms for soil health improvement will be key for agricultural sustainability and food security.In this review,the current status and driving factors of soil degradation in the four major black soil regions of the world are summarized,and effective measures for black soil conservation are proposed.The Northeast Plain of China is the research hotspot with 41.5%of the published studies related to black soil degradation,despite its relatively short history of agricultural reclamation,followed by the East European Plain(28.3%),the Great Plains of North America(20.7%),and the Pampas of South American(7.9%).Among the main types of soil degradation,soil erosion and soil fertility decline(especially organic matter loss)have been reported as the most common problems,with 27.6%and 39.4%of the published studies,respectively.In addition to the natural influences of climate and topography,human activities have been reported to have great influences on the degradation of black soils globally.Unsustainable farming practices and excess in agrochemical applications are common factors reported to accelerate the degradation process and threaten the sustainable use of black soils.Global efforts for black soil conservation and utilization should focus on standardizing evaluation criteria including real-time monitoring and the measures of prevention and restoration for sustainable management.International cooperation in technology and policy is crucial for overcoming the challenges and thus achieving the protection,sustainable use,and management of global black soil resources.
基金supported by the National Key R&D Program of China under Grant No.2022YFB3103500the National Natural Science Foundation of China under Grants No.62402087 and No.62020106013+3 种基金the Sichuan Science and Technology Program under Grant No.2023ZYD0142the Chengdu Science and Technology Program under Grant No.2023-XT00-00002-GXthe Fundamental Research Funds for Chinese Central Universities under Grants No.ZYGX2020ZB027 and No.Y030232063003002the Postdoctoral Innovation Talents Support Program under Grant No.BX20230060.
文摘The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.
文摘Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.
文摘ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future directions.Through this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
文摘The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.
基金funded by the Office of Gas and Electricity Markets(Ofgem)and supported by De Montfort University(DMU)and Nottingham Trent University(NTU),UK.
文摘This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA framework integrates security by design principles, micro-segmentation, and Island Mode Operation (IMO) to enhance cyber resilience and ensure continuous, secure operations. The methodology deploys a Forward-Thinking Architecture Strategy (FTAS) algorithm, which utilises an industrial Intrusion Detection System (IDS) implemented with Python’s Network Intrusion Detection System (NIDS) library. The FTAS algorithm successfully identified and responded to cyber-attacks, ensuring minimal system disruption. ISERA has been validated through comprehensive testing scenarios simulating Denial of Service (DoS) attacks and malware intrusions, at both the IT and OT layers where it successfully mitigates the impact of malicious activity. Results demonstrate ISERA’s efficacy in real-time threat detection, containment, and incident response, thus ensuring the integrity and reliability of critical infrastructure systems. ISERA’s decentralised approach contributes to global net zero goals by optimising resource use and minimising environmental impact. By adopting a decentralised control architecture and leveraging virtualisation, ISERA significantly enhances the cyber resilience and sustainability of critical infrastructure systems. This approach not only strengthens defences against evolving cyber threats but also optimises resource allocation, reducing the system’s carbon footprint. As a result, ISERA ensures the uninterrupted operation of essential services while contributing to broader net zero goals.
文摘There is a growing recognition of the critical role of security governance in advancing democratic transition in the post-conflict environment.Despite such a recognition,the security sector reform concept has overshadowed the importance of the overarching strategic role of security governance in transition to democracy,particularly in Africa.This paper assesses the status and challenges facing security governance and how they thwarted the efforts to furthering the democratic transition in South Sudan.The paper shows a deterioration in security,safety and security governance outcomes since the independence of South Sudan in 2011 with such a trend unlikely to be abated in the near future without strategic interventions.Some of the challenges facing security governance in South Sudan include the legacies of some historical events including the“Big Tent Policy”,absence of strategic leadership,lack of overarching policy framework,impractical and tenuous security arrangements in the 2018 peace agreement,persistent postponement of the first elections,and dysfunctional justice sector.The paper provides some strategic and operational recommendations to improve security governance and advance democratic transition in South Sudan.These recommendations include formulation of an inclusive and people-centered national security policy,rigorous judicial reform,and early political agreement on new political infrastructure if conditions for holding the first national elections are not met in 2026.
文摘The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.
文摘Land use transformations in Sonipat District,Haryana,driven by urbanization,industrialization,and land acquisitions,have posed significant ecological and socio-economic challenges,particularly concerning food security.This study investigates the interplay between these land use changes and their environmental implications at macro(district)and micro(village)levels,focusing on agricultural productivity and resource sustainability.The study employs a mixed-method approach,integrating secondary data from official datasets and primary data gathered through structured household surveys,focus group discussions,and visual analysis techniques.Data from 20 villages,selected based on predominant land use characteristics,were analysed using statistical and geospatial tools,including ArcGIS and STATA,to quantify food grain losses and evaluate environmental degradation.Findings of this study reveal a 19%reduction in agricultural land over two decades(2000-2024),correlating with increased residential and industrial areas.Groundwater resources face severe overexploitation,with pollution from industrial clusters further degrading water and soil quality.The study estimates a total food grain loss of 1.5 million kilograms across surveyed villages due to land acquisitions.A strong positive correlation(R^(2)=0.98)between land acquisition and food loss underscores the direct impact of urbanization on agricultural output.The research underscores the urgency of sustainable land management practices,including preserving agricultural lands,optimizing groundwater usage,and enhancing community involvement in planning.By addressing these challenges,the study advocates for balanced urban expansion and food security to ensure ecological and economic resilience in the region.
文摘This study investigates the critical intersection of cyberpsychology and cybersecurity policy development in small and medium-sized enterprises (SMEs). Through a mixed-methods approach incorporating surveys of 523 employees across 78 SMEs, qualitative interviews, and case studies, the research examines how psychological factors influence cybersecurity behaviors and policy effectiveness. Key findings reveal significant correlations between psychological factors and security outcomes, including the relationship between self-efficacy and policy compliance (r = 0.42, p β = 0.37, p < 0.001). The study identifies critical challenges in risk perception, policy complexity, and organizational culture affecting SME cybersecurity implementation. Results demonstrate that successful cybersecurity initiatives require the integration of psychological principles with technical solutions. The research provides a framework for developing human-centric security policies that address both behavioral and technical aspects of cybersecurity in resource-constrained environments.
基金supported by Chinese National Key R&DProject for Synthetic Biology(2018YFA0902500)National Natural Science Foundation of China(32273118)+3 种基金The Guangdong Key R&D Project(2022B1111070005)Shenzhen Special Fund for Sustainable Development(KCXFZ20211020164013021)Shenzhen University 2035 Program for Excellent Research(2022B010)supported by a startup grant from the Food Futures Institute of Murdoch University,Australia.
文摘Global crop productivity faces a significant threat from climate change-induced drought stress(DS),which is vital for sustainable agriculture and global food security.Uncovering DS adaptation and tolerance mechanisms in crops is necessary to alleviate climate challenges.Innovative plant breeding demands revolutionary approaches to develop stress-smart plants.Metabolomics,a promising field in plant breeding,offers a predictive tool to identify metabolic markers associated with plant performance under DS,enabling accelerated crop improvement.Central to DS adaptation is metabolomics-driven metabolic regulation,which is critical for maintaining cell osmotic potential in crops.Recent innovations allow rapid mapping of specific metabolites to their genetic pathways,providing a valuable resource for plant scientists.Metabolomics-driven molecular breeding,integrating techniques such as mQTL and mGWAS,enhances our ability to discover key genetic elements linked to stress-responsive metabolites.This integration offers a beneficial platform for plant scientists,yielding significant insights into the complex metabolic networks underlying DS tolerance.Therefore,this review discusses(1)insights into metabolic regulation for DS adaptation,(2)the multifaceted role of metabolites in DS tolerance and nutritional/yield trait improvement,(3)the potential of single-cell metabolomics and imaging,(4)metabolomics-driven molecular breeding,and(5)the application of metabolic and genetic engineering for DS-tolerant crops.We finally propose that the metabolomics-driven approach positions drought-smart crops as key contributors to future food production,supporting the vital goal of achieving“zero hunger”.
文摘The accelerating global energy transition,driven by climate imperatives and technological advancements,demands fundamen-tal transformations in power systems.Smart grids,characterized by cyber-physical integration,distributed renewable resources,and data-driven intelligence,have emerged as the backbone of this evolution.This convergence,however,introduces unprecedented complexities in resilience,security,stability,and market operation.This special issue presents five pivotal studies addressing these interconnected challenges,offering novel methodologies and insights to advance the efficiency,resilience,and sustainability of modern power systems.
