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and economic benefits.However,the large-scale use of renewable energy still faces numerous barriers,including outdated infrastructure,fragmented regulatory frameworks,limited investment,and shortages of technical expertise.Overcoming these obstacles requires institutional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
Due to space constraints in mountainous areas,twin tunnels are sometimes constructed very close to each other or even overlap.This proximity challenges the structural stability of tunnels built with the drill-and-blas...Due to space constraints in mountainous areas,twin tunnels are sometimes constructed very close to each other or even overlap.This proximity challenges the structural stability of tunnels built with the drill-and-blast method,as the short propagation distance amplifies blasting vibrations.A case of blasting damage is reported in this paper,where concrete cracks crossed construction joints in the twin-arch lining.To identify the causes of these cracks and develop effective vibration mitigation measures,field monitoring and numerical analysis were conducted.Specifically,a restart method was used to simulate the second peak particle velocity(PPV)of MS3 delays occurring 50 ms after the MS1 delays.The study found that the dynamic tensile stress in the tunnel induced by the blast wave has a linear relationship with the of the product of the concrete wave impedance and the PPV.A blast vibration velocity exceeding 23.3 cm/s resulted in tensile stress in the lining surpassing the ultimate tensile strength of C30 concrete,leading to tensile cracking on the blast-facing arch of the constructed tunnel.To control excessive vi-bration velocity,a mitigation trench was implemented to reduce blast wave impact.The trench,approximately 15 m in length,50 cm in width,and 450 cm in height,effectively lowered vibration ve-locities,achieving an average reduction rate of 52%according to numerical analysis.A key innovation of this study is the on-site implementation and validation of the trench's effectiveness in mitigating vi-brations.A feasible trench construction configuration was proposed to overcome the limitations of a single trench in fully controlling vibrations.To further enhance protection,zoned blasting and an auxiliary rock pillar,80 cm in width,were incorporated to reinforce the mid-wall.This study introduces novel strategies for vibration protection in tunnel blasting,offering innovative solutions to address blasting-induced vibrations and effectively minimize their impact,thereby enhancing safety and struc-tural stability.展开更多
The explosive expansion of the Internet of Things(IoT)systems has increased the imperative to have strong and robust solutions to cyber Security,especially to curtail Distributed Denial of Service(DDoS)attacks,which c...The explosive expansion of the Internet of Things(IoT)systems has increased the imperative to have strong and robust solutions to cyber Security,especially to curtail Distributed Denial of Service(DDoS)attacks,which can cripple critical infrastructure.The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabledmitigation tomake intelligent,decentralized,and real-time DDoS countermeasures in an IoT network.The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms,which makes them more accurate in detection than statistical features alone,based on the Convolutional Neural Network(CNN)architecture,which can extract deep features.A permissioned blockchain will be included to record the threat cases immutably and automatically execute mitigation measures through smart contracts to provide transparency and resilience.When tested on two test sets,BoT-IoT and IoT-23,the framework obtains a maximum F1-score at 97.5 percent and only a 1.8 percent false positive rate,which compares favorably to other solutions regarding effectiveness and the amount of time required to respond.Our findings support the feasibility of our method as an extensible and secure paradigm of nextgeneration IoT security,which has constrictive utility in mission-critical or resource-constrained settings.The work is a substantial milestone in autonomous and trustful mitigation against DDoS attacks through intelligent learning and decentralized enforcement.展开更多
As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. Ther...As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.展开更多
As centers of human activity,cities concentrate populations,resources,and wealth in limited areas.According to the United Nations,55%of the global population now lives in urban areas[1].Moreover,the World Economic Fo...As centers of human activity,cities concentrate populations,resources,and wealth in limited areas.According to the United Nations,55%of the global population now lives in urban areas[1].Moreover,the World Economic Forum’s“Global Risks Report 2023”[2]highlights natural disasters as a major threat to sustainable development,especially for densely populated cities.展开更多
Over the past few years,Malware attacks have become more and more widespread,posing threats to digital assets throughout the world.Although numerous methods have been developed to detect malicious attacks,these malwar...Over the past few years,Malware attacks have become more and more widespread,posing threats to digital assets throughout the world.Although numerous methods have been developed to detect malicious attacks,these malware detection techniques need to be more efficient in detecting new and progressively sophisticated variants of malware.Therefore,the development of more advanced and accurate techniques is necessary for malware detection.This paper introduces a comprehensive Dual-Channel Attention Deep Bidirectional Long Short-Term Memory(DCADBiLSTM)model for malware detection and riskmitigation.The Dual Channel Attention(DCA)mechanism improves themodel’s capability to concentrate on the features that aremost appropriate in the input data,which reduces the false favourable rates.The Bidirectional Long,Short-Term Memory framework helps capture crucial interdependence from past and future circumstances,which is essential for enhancing the model’s understanding of malware behaviour.As soon as malware is detected,the risk mitigation phase is implemented,which evaluates the severity of each threat and helps mitigate threats earlier.The outcomes of the method demonstrate better accuracy of 98.96%,which outperforms traditional models.It indicates the method detects and mitigates several kinds of malware threats,thereby providing a proactive defence mechanism against the emerging challenges in cybersecurity.展开更多
The global supply chain turbulence has increased the difficulty of protecting foreign well-known trademarks.Although there are many studies on cross-border trademark rights protection in academia,there is relatively l...The global supply chain turbulence has increased the difficulty of protecting foreign well-known trademarks.Although there are many studies on cross-border trademark rights protection in academia,there is relatively little research on its risk mitigation effectiveness in the context of supply chain fluctuations.Based on case studies of commercial law and data statistics,the study explores the relationship between protection efficiency and market response through legal applicability.Due to the long litigation cycle and uneven law enforcement,there are differences in market regulation,weakening the protection of well-known trademarks and exacerbating supply chain uncertainty.Strengthening international legal framework cooperation and promoting law enforcement linkage can enhance protection effectiveness.In theory,enriching the theory of cross-border trademark protection and expanding research on brand rights protection in the context of global supply chains.In practice,it helps enterprises adjust their trademark layout,avoid legal risks,and improve market competitiveness.Due to the complexity of the legal environment and limitations in data acquisition,future research will strengthen data analysis,promote international cooperation in intelligent supervision,and build a more efficient cross-border well-known trademark protection mechanism.展开更多
This research investigates the effectiveness of climate-related development aid in Indonesia’s climate mitigation.Specific objectives include assessing the contribution of official development assistance(ODA)to reduc...This research investigates the effectiveness of climate-related development aid in Indonesia’s climate mitigation.Specific objectives include assessing the contribution of official development assistance(ODA)to reducing CO_(2) emissions and evaluating the implementation of the Busan Principles of aid effectiveness to achieve Indonesia’s mitigation priorities and targets.We utilize a new primary dataset based on interviews with the most knowledgeable stakeholders of ODA on climate change mitigation.Additionally,we use secondary data from the annual Rio Marker and the Common Reporting Standard data of the Organization for Economic Co-operation and Development.The results show a significant correlation between climate-related development aid and CO_(2) emission reduction in Indonesia.Additionally,the implementation of the Busan Principles enhances aid management by fostering project ownership and increasing the involvement of civil society and private sector.The study has implications for devising an effective climate change mitigation strategy for Indonesia.It is suggested that the government of Indonesia exercise greater flexibility and dynamism in engaging with development partners.展开更多
Sewer corrosion is a critical issue that significantly threatens sewer systems,contributing to approximately 40%of sewer infrastructure deterioration.Although numerous review studies have been conducted in this field,...Sewer corrosion is a critical issue that significantly threatens sewer systems,contributing to approximately 40%of sewer infrastructure deterioration.Although numerous review studies have been conducted in this field,gaps persist in identifying the complex factors driving corrosion and understanding their interrelationships.These deficiencies impede the development of accurate corrosion prediction models and the identification of more effective mitigation strategies.This research aims to deepen the understanding of the underlying causes of sewer corrosion,evaluate the latest advancements in prediction models,and explore current mitigation techniques.A novel hybrid approach is employed,combining bibliometric,scientometric,and systematic analysis.While widely used in other fields,this methodology is new in sewer corrosion.The key findings of this study include a comprehensive identification of the various factors influencing corrosion,an overview of existing corrosion prediction models,and an evaluation of currently employed mitigation strategies.Additionally,this research highlights critical research gaps and suggests future avenues for investigation,with the potential to support municipalities in more efficient and flexible management of sewer infrastructure.展开更多
EHL-2 is a compact,high-field spherical tokamak designed to explore the potential of an advanced p-11B nuclear fusion reactor.Due to its high plasma current and thermal energy,it is crucial to mitigate the impact asso...EHL-2 is a compact,high-field spherical tokamak designed to explore the potential of an advanced p-11B nuclear fusion reactor.Due to its high plasma current and thermal energy,it is crucial to mitigate the impact associated with disruptions to ensure the safe operation of EHL-2.This paper evaluates the performance requirements of the disruption prediction system on EHL-2,with a particular focus on applying generalizable knowledge transfer from existing devices to future ones.Furthermore,the key characteristics of disruption mitigation strategies are analyzed,and their overall mitigation performance on EHL-2 is assessed.This insight provides valuable guidance for optimizing the engineering design of EHL-2 and identifying its optimal operational regime.展开更多
In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(M...In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.展开更多
The intertwined challenges of air pollution and climate change represent a critical environmental dilemma of our time.These issues are inextricably linked through shared emission sources,coupled physical and chemical ...The intertwined challenges of air pollution and climate change represent a critical environmental dilemma of our time.These issues are inextricably linked through shared emission sources,coupled physical and chemical processes,and a common solution space in the transition to a sustainable future.Advanced atmospheric and Earth system modeling is therefore an indispensable tool for developing coordinated strategies that maximize co-benefits.This special issue,“Atmospheric and Earth System Modeling towards Coordinated Pollution Control and Climate Change Mitigation,”showcases cutting-edge research that enhances our modeling capabilities to address this complex nexus.The contributions collectively advance model fidelity and integration across scales,from fundamental particle properties to regional pollution transport and climate impacts.展开更多
Public participation is crucial in mitigating disasters.Stemming from the ongoing debate on benefit-and risk-driven approaches to landslide mitigation,this study seeks to uncover the factors and underlying mechanisms ...Public participation is crucial in mitigating disasters.Stemming from the ongoing debate on benefit-and risk-driven approaches to landslide mitigation,this study seeks to uncover the factors and underlying mechanisms that affect farmers'willingness to participate in landslide prevention and mitigation(WPLPM).Conducted in Heifangtai,Gansu Province,China,renowned as the"landslide natural laboratory",this research employs multiple linear regression analysis on data from 399 questionnaires to pinpoint the key determinants of farmers'WPLPM.The findings reveal:(1)the"risk perception paradox"exists—farmers have high-risk perception but low WPLPM;(2)the impact of risk perception on WPLPM is tempered by self-efficacy related to fund,learning ability,and operation ability,offering an insight into the"risk perception paradox";and(3)There are significant positive influences of farmers'benefit perception,social network,and perceived responsibility on their WPLPM.Based on these insights,the study offers targeted policy recommendations.展开更多
Global Navigation Satellite Systems(GNSSs)face significant security threats from spoofing attacks.Typical anti-spoofing methods rely on estimating the delays between spoofing and authentic signals using multicorrelato...Global Navigation Satellite Systems(GNSSs)face significant security threats from spoofing attacks.Typical anti-spoofing methods rely on estimating the delays between spoofing and authentic signals using multicorrelator outputs.However,the accuracy of the delay estimation is limited by the spacing of the correlators.To address this,an innovative anti-spoofing method is introduced,which incorporates distinct coarse and refined stages for more accurate spoofing estimation.By leveraging the coarse delay estimates obtained through maximum likelihood estimation,the proposed method establishes the Windowed Sum of the Relative Delay(WSRD)statistics to detect the presence of spoofing signals.The iterative strategy is then employed to enhance the precision of the delay estimation.To further adapt to variations in the observation noise caused by spoofing intrusions and restore precise position,velocity,and timing solutions,an adaptive extended Kalman filter is proposed.This comprehensive framework offers detection,mitigation,and recovery against spoofing attacks.Experimental validation using datasets from the Texas Spoofing Test Battery(TEXBAT)demonstrates the effectiveness of the proposed anti-spoofing method.With 41 correlators,the method achieves a detection rate exceeding 90%at a false alarm rate of 10-5,with position or time errors below 15 m.Notably,this refined anti-spoofing approach shows robust detection and mitigation capabilities,requiring only a single antenna without the need for additional external sensors.These advancements can significantly contribute to the development of GNSS anti-spoofing measures.展开更多
This study aims to analyze waste mitigation policies implemented in South Tangerang City,Indonesia,which faces significant challenges in waste management.Despite various mitigation efforts,issues such as limited landf...This study aims to analyze waste mitigation policies implemented in South Tangerang City,Indonesia,which faces significant challenges in waste management.Despite various mitigation efforts,issues such as limited landfill capacity,low community participation in waste sorting,and inadequate treatment facilities continue to hinder effective waste management.Using a case study approach,the research assesses the effectiveness of existing policies and identifies key barriers.The findings show that poor waste management,characterized by a high volume of waste sent to landfills,leads to severe environmental pollution—including air,soil,and water contamination—and increases the risk of disasters such as landfill collapses.This negative impact is not only felt by the environment,but also has an impact on public health and regional budget efficiency.While initiatives such as the 3R(Reduce,Reuse,Recycle)program and organic waste treatment have been introduced,low community engagement and inadequate treatment facilities remain major obstacles.The study also compares successful waste management policies from developed countries such as Germany,Sweden,and South Korea,offering valuable insights for local policy adaptation.Based on these findings,the study recommends increasing government capacity,improving access to and the quality of Reduce,Reuse,Recycle(WPP3R)Waste Treatment sites,providing incentives,encouraging community involvement,and promoting collaboration between the public and private sectors to achieve more efficient and sustainable waste management.展开更多
The land,water,energy use,and greenhouse gas(GHG)emissions involved in agricultural production are intrinsically linked.However,quantitative characterization and scenario simulations of these elements'inherent int...The land,water,energy use,and greenhouse gas(GHG)emissions involved in agricultural production are intrinsically linked.However,quantitative characterization and scenario simulations of these elements'inherent interrelationships remain scarce.We developed a land–water–energy–GHG(LWEG)nexus framework for the North China Plain(NCP).The framework identifies the mutual feedback in the life cycle of agricultural production among the four factors.We applied the framework to assess the agricultural GHG mitigation potential for winter wheat,summer maize,and rice in NCP municipalities.The results showed that cropping structure optimization reduced GHG emissions by 1.96 Mt CO_(2)e.Controlling indirect energy consumption in upstream processes of crop production and reducing on-site energy use reduced the volume and intensity per unit area of agricultural GHG emissions.Because of the synergies between land,water,and energy,nexus management,which combines multiple measures of groundwater management,fertilizer,and energy control,has substantial GHG mitigation potential.The nexus management scenario produced a total GHG of 159.51 Mt CO_(2)e,a decrease of 15.38%from the baseline scenario.This study quantifies the LWEG nexus within agricultural production processes and identifies agricultural management practices that integrate water,energy conservation,and emissions mitigation contributing to the Sustainable Development Goals.展开更多
Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,...Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.展开更多
Reducing greenhouse gas(GHG)emissions to address climate change is a global consensus,and municipal wastewater treatment plants(MWWTPs)should lead the way in low-carbon sustainable development.However,achieving efflue...Reducing greenhouse gas(GHG)emissions to address climate change is a global consensus,and municipal wastewater treatment plants(MWWTPs)should lead the way in low-carbon sustainable development.However,achieving effluent discharge standards often requires considerable energy and chemical consumption during operation,resulting in significant carbon footprints.In this study,GHG emissions are systematically accounted for,and the driving factors of carbon footprint growth in China’s MWWTPs are explored.In 2020,a total of 41.9 million tonnes(Mt)of carbon dioxide equivalent(CO_(2)-eq)were released by the sector,with nearly two-thirds being indirect emissions resulting from energy and material usage.The intensity of electricity,carbon source,and phosphorus removing agent consumption increasingly influence carbon footprint growth over time.Through statistical inference,benchmarks for electricity and chemical consumption intensity are established across all MWWTPs under various operational conditions,and the potential for mitigation through more efficient energy and material utilization is calculated.The results suggest that many MWWTPs offer significant opportunities for emission reduction.Consequently,empirical decarbonization measures,including intelligent device control,optimization of aeration equipment,energy recovery initiatives,and other enhancements to improve operational and carbon efficiency,are recommended.展开更多
Nature-based solutions(NBS)involve the sustainable maintenance,management,and restoration of natural or modified ecosystems.Flooding is a major problem in Phnom Penh,Cambodia,and has significant social and economic ra...Nature-based solutions(NBS)involve the sustainable maintenance,management,and restoration of natural or modified ecosystems.Flooding is a major problem in Phnom Penh,Cambodia,and has significant social and economic ramifications.This study tries to suggest creative solutions that support human welfare and biodiversity while simultaneously resolving social problems by adopting NBS.An online survey using convenience and snowball sampling was conducted to assess the openness of Phnom Penh residents to adopting NBS for flood mitigation in their homes or buildings.The survey investigated perceptions of NBS effectiveness based on previous knowledge and flood risk perception.Results revealed a strong correlation between perceived efficacy and willingness to adopt NBS.Specifically,flood risk perception and prior knowledge significantly influenced the perceived effectiveness of NBS.Key findings indicate that high installation and maintenance costs,lack of awareness,limited space,cultural factors,and perceived ineffectiveness are primary barriers to NBS adoption.Additionally,specific regional factors contribute to reluctance in certain areas of Phnom Penh.To overcome these barriers,the study recommends that the Cambodian government and other stakeholders invest in public education campaigns to raise awareness about the benefits of NBS.Financial incentives and subsidies should be provided to reduce the economic burden on residents.Furthermore,integrating NBS into urban planning and infrastructure development is crucial to enhance community resilience against floods.展开更多
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 Shenzhen Stability Support Plan(Grant No.20231122095154003)National Natural Science Foundation of China(Grant Nos.51978671 and 52378425)Guizhou Provincial Department of Transportation Science and Technology Program(Grant No.2023-122-003)。
文摘Due to space constraints in mountainous areas,twin tunnels are sometimes constructed very close to each other or even overlap.This proximity challenges the structural stability of tunnels built with the drill-and-blast method,as the short propagation distance amplifies blasting vibrations.A case of blasting damage is reported in this paper,where concrete cracks crossed construction joints in the twin-arch lining.To identify the causes of these cracks and develop effective vibration mitigation measures,field monitoring and numerical analysis were conducted.Specifically,a restart method was used to simulate the second peak particle velocity(PPV)of MS3 delays occurring 50 ms after the MS1 delays.The study found that the dynamic tensile stress in the tunnel induced by the blast wave has a linear relationship with the of the product of the concrete wave impedance and the PPV.A blast vibration velocity exceeding 23.3 cm/s resulted in tensile stress in the lining surpassing the ultimate tensile strength of C30 concrete,leading to tensile cracking on the blast-facing arch of the constructed tunnel.To control excessive vi-bration velocity,a mitigation trench was implemented to reduce blast wave impact.The trench,approximately 15 m in length,50 cm in width,and 450 cm in height,effectively lowered vibration ve-locities,achieving an average reduction rate of 52%according to numerical analysis.A key innovation of this study is the on-site implementation and validation of the trench's effectiveness in mitigating vi-brations.A feasible trench construction configuration was proposed to overcome the limitations of a single trench in fully controlling vibrations.To further enhance protection,zoned blasting and an auxiliary rock pillar,80 cm in width,were incorporated to reinforce the mid-wall.This study introduces novel strategies for vibration protection in tunnel blasting,offering innovative solutions to address blasting-induced vibrations and effectively minimize their impact,thereby enhancing safety and struc-tural stability.
文摘The explosive expansion of the Internet of Things(IoT)systems has increased the imperative to have strong and robust solutions to cyber Security,especially to curtail Distributed Denial of Service(DDoS)attacks,which can cripple critical infrastructure.The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabledmitigation tomake intelligent,decentralized,and real-time DDoS countermeasures in an IoT network.The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms,which makes them more accurate in detection than statistical features alone,based on the Convolutional Neural Network(CNN)architecture,which can extract deep features.A permissioned blockchain will be included to record the threat cases immutably and automatically execute mitigation measures through smart contracts to provide transparency and resilience.When tested on two test sets,BoT-IoT and IoT-23,the framework obtains a maximum F1-score at 97.5 percent and only a 1.8 percent false positive rate,which compares favorably to other solutions regarding effectiveness and the amount of time required to respond.Our findings support the feasibility of our method as an extensible and secure paradigm of nextgeneration IoT security,which has constrictive utility in mission-critical or resource-constrained settings.The work is a substantial milestone in autonomous and trustful mitigation against DDoS attacks through intelligent learning and decentralized enforcement.
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry(RS-2024-00415520)supervised by the Korea Institute for Advancement of Technology(KIAT)the Ministry of Science and ICT(MSIT)under the ICT Challenge and Advanced Network of HRD(ICAN)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.
基金the Ministry of Science and Technology National Key Research and Development Program(2023YFC3805000)the National Natural Science Foundation of China(52025083 and 52208501)the Shanghai Science and Technology Innovation Action Plan(22dz1201400).
文摘As centers of human activity,cities concentrate populations,resources,and wealth in limited areas.According to the United Nations,55%of the global population now lives in urban areas[1].Moreover,the World Economic Forum’s“Global Risks Report 2023”[2]highlights natural disasters as a major threat to sustainable development,especially for densely populated cities.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant No.(IPP:421-611-2025).
文摘Over the past few years,Malware attacks have become more and more widespread,posing threats to digital assets throughout the world.Although numerous methods have been developed to detect malicious attacks,these malware detection techniques need to be more efficient in detecting new and progressively sophisticated variants of malware.Therefore,the development of more advanced and accurate techniques is necessary for malware detection.This paper introduces a comprehensive Dual-Channel Attention Deep Bidirectional Long Short-Term Memory(DCADBiLSTM)model for malware detection and riskmitigation.The Dual Channel Attention(DCA)mechanism improves themodel’s capability to concentrate on the features that aremost appropriate in the input data,which reduces the false favourable rates.The Bidirectional Long,Short-Term Memory framework helps capture crucial interdependence from past and future circumstances,which is essential for enhancing the model’s understanding of malware behaviour.As soon as malware is detected,the risk mitigation phase is implemented,which evaluates the severity of each threat and helps mitigate threats earlier.The outcomes of the method demonstrate better accuracy of 98.96%,which outperforms traditional models.It indicates the method detects and mitigates several kinds of malware threats,thereby providing a proactive defence mechanism against the emerging challenges in cybersecurity.
文摘The global supply chain turbulence has increased the difficulty of protecting foreign well-known trademarks.Although there are many studies on cross-border trademark rights protection in academia,there is relatively little research on its risk mitigation effectiveness in the context of supply chain fluctuations.Based on case studies of commercial law and data statistics,the study explores the relationship between protection efficiency and market response through legal applicability.Due to the long litigation cycle and uneven law enforcement,there are differences in market regulation,weakening the protection of well-known trademarks and exacerbating supply chain uncertainty.Strengthening international legal framework cooperation and promoting law enforcement linkage can enhance protection effectiveness.In theory,enriching the theory of cross-border trademark protection and expanding research on brand rights protection in the context of global supply chains.In practice,it helps enterprises adjust their trademark layout,avoid legal risks,and improve market competitiveness.Due to the complexity of the legal environment and limitations in data acquisition,future research will strengthen data analysis,promote international cooperation in intelligent supervision,and build a more efficient cross-border well-known trademark protection mechanism.
文摘This research investigates the effectiveness of climate-related development aid in Indonesia’s climate mitigation.Specific objectives include assessing the contribution of official development assistance(ODA)to reducing CO_(2) emissions and evaluating the implementation of the Busan Principles of aid effectiveness to achieve Indonesia’s mitigation priorities and targets.We utilize a new primary dataset based on interviews with the most knowledgeable stakeholders of ODA on climate change mitigation.Additionally,we use secondary data from the annual Rio Marker and the Common Reporting Standard data of the Organization for Economic Co-operation and Development.The results show a significant correlation between climate-related development aid and CO_(2) emission reduction in Indonesia.Additionally,the implementation of the Busan Principles enhances aid management by fostering project ownership and increasing the involvement of civil society and private sector.The study has implications for devising an effective climate change mitigation strategy for Indonesia.It is suggested that the government of Indonesia exercise greater flexibility and dynamism in engaging with development partners.
基金supported by the Research Grants Council of the University Grants Committee in Hong Kong,China(No.RGC-15209022).
文摘Sewer corrosion is a critical issue that significantly threatens sewer systems,contributing to approximately 40%of sewer infrastructure deterioration.Although numerous review studies have been conducted in this field,gaps persist in identifying the complex factors driving corrosion and understanding their interrelationships.These deficiencies impede the development of accurate corrosion prediction models and the identification of more effective mitigation strategies.This research aims to deepen the understanding of the underlying causes of sewer corrosion,evaluate the latest advancements in prediction models,and explore current mitigation techniques.A novel hybrid approach is employed,combining bibliometric,scientometric,and systematic analysis.While widely used in other fields,this methodology is new in sewer corrosion.The key findings of this study include a comprehensive identification of the various factors influencing corrosion,an overview of existing corrosion prediction models,and an evaluation of currently employed mitigation strategies.Additionally,this research highlights critical research gaps and suggests future avenues for investigation,with the potential to support municipalities in more efficient and flexible management of sewer infrastructure.
基金supported by the ENN Group,the ENN Energy Research Institute and National Natural Science Foundation of China(No.12205122).
文摘EHL-2 is a compact,high-field spherical tokamak designed to explore the potential of an advanced p-11B nuclear fusion reactor.Due to its high plasma current and thermal energy,it is crucial to mitigate the impact associated with disruptions to ensure the safe operation of EHL-2.This paper evaluates the performance requirements of the disruption prediction system on EHL-2,with a particular focus on applying generalizable knowledge transfer from existing devices to future ones.Furthermore,the key characteristics of disruption mitigation strategies are analyzed,and their overall mitigation performance on EHL-2 is assessed.This insight provides valuable guidance for optimizing the engineering design of EHL-2 and identifying its optimal operational regime.
文摘In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.
文摘The intertwined challenges of air pollution and climate change represent a critical environmental dilemma of our time.These issues are inextricably linked through shared emission sources,coupled physical and chemical processes,and a common solution space in the transition to a sustainable future.Advanced atmospheric and Earth system modeling is therefore an indispensable tool for developing coordinated strategies that maximize co-benefits.This special issue,“Atmospheric and Earth System Modeling towards Coordinated Pollution Control and Climate Change Mitigation,”showcases cutting-edge research that enhances our modeling capabilities to address this complex nexus.The contributions collectively advance model fidelity and integration across scales,from fundamental particle properties to regional pollution transport and climate impacts.
基金funded by National Social Science Foundation of China(Grant Number 24&ZD164)。
文摘Public participation is crucial in mitigating disasters.Stemming from the ongoing debate on benefit-and risk-driven approaches to landslide mitigation,this study seeks to uncover the factors and underlying mechanisms that affect farmers'willingness to participate in landslide prevention and mitigation(WPLPM).Conducted in Heifangtai,Gansu Province,China,renowned as the"landslide natural laboratory",this research employs multiple linear regression analysis on data from 399 questionnaires to pinpoint the key determinants of farmers'WPLPM.The findings reveal:(1)the"risk perception paradox"exists—farmers have high-risk perception but low WPLPM;(2)the impact of risk perception on WPLPM is tempered by self-efficacy related to fund,learning ability,and operation ability,offering an insight into the"risk perception paradox";and(3)There are significant positive influences of farmers'benefit perception,social network,and perceived responsibility on their WPLPM.Based on these insights,the study offers targeted policy recommendations.
基金co-supported by the Tianjin Research innovation Project for Postgraduate Students,China(No.2022BKYZ039)the China Postdoctoral Science Foundation(No.2023M731788)the National Natural Science Foundation of China(No.62303246)。
文摘Global Navigation Satellite Systems(GNSSs)face significant security threats from spoofing attacks.Typical anti-spoofing methods rely on estimating the delays between spoofing and authentic signals using multicorrelator outputs.However,the accuracy of the delay estimation is limited by the spacing of the correlators.To address this,an innovative anti-spoofing method is introduced,which incorporates distinct coarse and refined stages for more accurate spoofing estimation.By leveraging the coarse delay estimates obtained through maximum likelihood estimation,the proposed method establishes the Windowed Sum of the Relative Delay(WSRD)statistics to detect the presence of spoofing signals.The iterative strategy is then employed to enhance the precision of the delay estimation.To further adapt to variations in the observation noise caused by spoofing intrusions and restore precise position,velocity,and timing solutions,an adaptive extended Kalman filter is proposed.This comprehensive framework offers detection,mitigation,and recovery against spoofing attacks.Experimental validation using datasets from the Texas Spoofing Test Battery(TEXBAT)demonstrates the effectiveness of the proposed anti-spoofing method.With 41 correlators,the method achieves a detection rate exceeding 90%at a false alarm rate of 10-5,with position or time errors below 15 m.Notably,this refined anti-spoofing approach shows robust detection and mitigation capabilities,requiring only a single antenna without the need for additional external sensors.These advancements can significantly contribute to the development of GNSS anti-spoofing measures.
文摘This study aims to analyze waste mitigation policies implemented in South Tangerang City,Indonesia,which faces significant challenges in waste management.Despite various mitigation efforts,issues such as limited landfill capacity,low community participation in waste sorting,and inadequate treatment facilities continue to hinder effective waste management.Using a case study approach,the research assesses the effectiveness of existing policies and identifies key barriers.The findings show that poor waste management,characterized by a high volume of waste sent to landfills,leads to severe environmental pollution—including air,soil,and water contamination—and increases the risk of disasters such as landfill collapses.This negative impact is not only felt by the environment,but also has an impact on public health and regional budget efficiency.While initiatives such as the 3R(Reduce,Reuse,Recycle)program and organic waste treatment have been introduced,low community engagement and inadequate treatment facilities remain major obstacles.The study also compares successful waste management policies from developed countries such as Germany,Sweden,and South Korea,offering valuable insights for local policy adaptation.Based on these findings,the study recommends increasing government capacity,improving access to and the quality of Reduce,Reuse,Recycle(WPP3R)Waste Treatment sites,providing incentives,encouraging community involvement,and promoting collaboration between the public and private sectors to achieve more efficient and sustainable waste management.
基金supported by the National Natural Science Foundation of China(Grants No.72474200,72104223)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.72221002)the Innovation Centre for Digital Business and Capital Development of Beijing Technology and Business University(Grant No.SZSK202211)。
文摘The land,water,energy use,and greenhouse gas(GHG)emissions involved in agricultural production are intrinsically linked.However,quantitative characterization and scenario simulations of these elements'inherent interrelationships remain scarce.We developed a land–water–energy–GHG(LWEG)nexus framework for the North China Plain(NCP).The framework identifies the mutual feedback in the life cycle of agricultural production among the four factors.We applied the framework to assess the agricultural GHG mitigation potential for winter wheat,summer maize,and rice in NCP municipalities.The results showed that cropping structure optimization reduced GHG emissions by 1.96 Mt CO_(2)e.Controlling indirect energy consumption in upstream processes of crop production and reducing on-site energy use reduced the volume and intensity per unit area of agricultural GHG emissions.Because of the synergies between land,water,and energy,nexus management,which combines multiple measures of groundwater management,fertilizer,and energy control,has substantial GHG mitigation potential.The nexus management scenario produced a total GHG of 159.51 Mt CO_(2)e,a decrease of 15.38%from the baseline scenario.This study quantifies the LWEG nexus within agricultural production processes and identifies agricultural management practices that integrate water,energy conservation,and emissions mitigation contributing to the Sustainable Development Goals.
基金funded by the So Lo Mon project“Monitoraggio a Lungo Termine di Grandi Frane basato su Sistemi Integrati di Sensori e Reti”(Longterm monitoring of large-scale landslides based on integrated systems of sensors and networks),Program EFRE-FESR 2014–2020,Project EFRE-FESR4008 South Tyrol–Person in charge:V.Mair。
文摘Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.
基金supported by the National Natural Science Foundation of China(52200228 and 72022004)the National Key Research and Development Program of China(2021YFC3200205 and 2022YFC3203704).
文摘Reducing greenhouse gas(GHG)emissions to address climate change is a global consensus,and municipal wastewater treatment plants(MWWTPs)should lead the way in low-carbon sustainable development.However,achieving effluent discharge standards often requires considerable energy and chemical consumption during operation,resulting in significant carbon footprints.In this study,GHG emissions are systematically accounted for,and the driving factors of carbon footprint growth in China’s MWWTPs are explored.In 2020,a total of 41.9 million tonnes(Mt)of carbon dioxide equivalent(CO_(2)-eq)were released by the sector,with nearly two-thirds being indirect emissions resulting from energy and material usage.The intensity of electricity,carbon source,and phosphorus removing agent consumption increasingly influence carbon footprint growth over time.Through statistical inference,benchmarks for electricity and chemical consumption intensity are established across all MWWTPs under various operational conditions,and the potential for mitigation through more efficient energy and material utilization is calculated.The results suggest that many MWWTPs offer significant opportunities for emission reduction.Consequently,empirical decarbonization measures,including intelligent device control,optimization of aeration equipment,energy recovery initiatives,and other enhancements to improve operational and carbon efficiency,are recommended.
文摘Nature-based solutions(NBS)involve the sustainable maintenance,management,and restoration of natural or modified ecosystems.Flooding is a major problem in Phnom Penh,Cambodia,and has significant social and economic ramifications.This study tries to suggest creative solutions that support human welfare and biodiversity while simultaneously resolving social problems by adopting NBS.An online survey using convenience and snowball sampling was conducted to assess the openness of Phnom Penh residents to adopting NBS for flood mitigation in their homes or buildings.The survey investigated perceptions of NBS effectiveness based on previous knowledge and flood risk perception.Results revealed a strong correlation between perceived efficacy and willingness to adopt NBS.Specifically,flood risk perception and prior knowledge significantly influenced the perceived effectiveness of NBS.Key findings indicate that high installation and maintenance costs,lack of awareness,limited space,cultural factors,and perceived ineffectiveness are primary barriers to NBS adoption.Additionally,specific regional factors contribute to reluctance in certain areas of Phnom Penh.To overcome these barriers,the study recommends that the Cambodian government and other stakeholders invest in public education campaigns to raise awareness about the benefits of NBS.Financial incentives and subsidies should be provided to reduce the economic burden on residents.Furthermore,integrating NBS into urban planning and infrastructure development is crucial to enhance community resilience against floods.
