In the field of quantum error mitigation,most current research separately addresses quantum gate noise mitigation and measurement noise mitigation.However,due to the typically high complexity of measurement noise miti...In the field of quantum error mitigation,most current research separately addresses quantum gate noise mitigation and measurement noise mitigation.However,due to the typically high complexity of measurement noise mitigation methods,such as those based on estimating response matrices,the overall complexity of noise mitigation schemes increases when combining measurement noise mitigation with other quantum gate noise mitigation approaches.This paper proposes a low-complexity quantum error mitigation scheme that jointly mitigates quantum gate and measurement noise,specifically when measurement noise manifests as an amplitude damping channel.The proposed scheme requires estimating only three parameters to jointly mitigate both types of noise,whereas the zero-noise extrapolation method enhanced by response matrix estimation requires estimating at least six parameters under the same conditions.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
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
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
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
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
There are many theoretical explanations for the mitigation of tornados, storms, and hurricanes and one or two known simulation models that address the reduction of the intensities of these forces. We introduce an inno...There are many theoretical explanations for the mitigation of tornados, storms, and hurricanes and one or two known simulation models that address the reduction of the intensities of these forces. We introduce an innovative methodology that releases environmentally friendly aerosol particles responsible for cloud condensation and weakens the intensities of these forces. For the past nine years, we did several experiments and analyzed the results. Experimental results give evidence to this methodology is practical, environment-friendly, cost-effective, and consistent. In this paper, we described our experiments along with results in three different scenarios such as tornado (March 2021, Georgia USA), storm Claudette (June 2021, Georgia USA), and hurricane Elsa (July 2021, Florida USA). Our experimental outcome and subsequent relevant meteorology data support the reason for mitigating the intensity of these destructive forces in and around the experiment locations.展开更多
An altemative algorithm for mitigating GPS multipath was presented by integrating unscented Kalman filter （UKF） and wavelet transform with particle filter. Within consideration of particle degeneracy, UKF was taken ...An altemative algorithm for mitigating GPS multipath was presented by integrating unscented Kalman filter （UKF） and wavelet transform with particle filter. Within consideration of particle degeneracy, UKF was taken for drawing particle. To remove the noise from raw data and data processing error, adaptive wavelet filtering with threshold was adopted while data preprocessing and drawing particle. Three algorithms, named EKF-PF, UKF-PF and WM-UKF-PF, were performed for comparison. The proposed WM-UKF-PF algorithm gives better error minimization, and significantly improves performance of multipath mitigation in terms of SNR and coefficient even though it has computation complexity. It is of significance for high-accuracy positioning and non-stationary deformation analysis.展开更多
Recent researches focused on developing robust blast load mitigation systems due to the threats of terrorist attacks.One of the main embraced strategies is the structural systems that use mitigation techniques.They ar...Recent researches focused on developing robust blast load mitigation systems due to the threats of terrorist attacks.One of the main embraced strategies is the structural systems that use mitigation techniques.They are developed from a combination of structural elements and described herein as conventional systems.Among the promising techniques is that redirect the waves propagation through hollow tubes.The blast wave propagation through tubes provides an efficient system since it combines many blast wave phenomena,such as reflection,diffraction,and interaction.In this research,a novel blast load mitigation system,employed as a protection fence,is developed using a technique similar to the technique of the bent tube in manipulating the shock-wave.The relative performance of the novel system to the conventional system is evaluated based on mitigation percent criteria.Performances of both systems are calculated through numerical simulation.The proposed novel system proved to satisfy high performance in mitigating the generated blast waves from charges weight up to 500 kg TNT at relatively small standoff distances(5 m and 8 m).It mitigates at least 94%of the blast waves,which means that only 6%of that blast impulse is considered as the applied load on the targeted structure.展开更多
[Objective] The aim was to study the characteristics of the changes of extreme weather climate incidents such as severe drought in northwest and rainstorm in Xiji County of Ningxia. [Method] Precipitation anomaly perc...[Objective] The aim was to study the characteristics of the changes of extreme weather climate incidents such as severe drought in northwest and rainstorm in Xiji County of Ningxia. [Method] Precipitation anomaly percentage was applied to divide drought level and for statistics analysis. Seasonal index, linear tendency, and 5-years gliding average were used to reflect the trend of drought changes. The circulation wave of temporal sequence used polynomial expression to simulate the interannual variation scale. The positive part of the polynomial expression used bar chart to simulate interannual variation scale. [Result] The index of drought season from November to June was large. The general trend of annual drought was increasing. The drought of interannual scale was most serious around 1977, about 15 and 20 years. The drought in recent years went up. The general situation of drought, interannual scale and changes of interannual scales from March to May and from September to October were discussed. Based on the weather at 500 hPa, the first rain in Xiji and the drought-turning-into-rain situation were classified. [Conclusion]These may provide reference value to the prevention and mitigation of drought.展开更多
This project is aimed at bridging the three planes,from basic research,through enabling processes,to engineered systems.At the basic research plane,we have been working to improve our collective understanding about ob...This project is aimed at bridging the three planes,from basic research,through enabling processes,to engineered systems.At the basic research plane,we have been working to improve our collective understanding about obstacles to implementing mitigation practices,owner decision processes (in connection with other MCEER projects),and public policy processes.At the level of enabling processes,we have been seeking to develop an understanding of how obstacles to greater mitigation can be overcome by improved policy design and processes.At the engineered systems plane, our work is intended to result in practical guidelines for devising policies and programs with appropriate motivation and incentives for implementing policies and programs once adopted.This phase of the research has been aimed,first,at a thorough,multidisciplinary review of the literature concerning obstacles to implementation.Second,the research has focused on advancing the state of the art by developing means for integrating the insights offered by diverse perspectives on the implementation process from the several social,behavioral,and decision sciences.The research establishes a basis for testing our understanding of these processes in the case of hospital retrofit decisions.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62271265)。
文摘In the field of quantum error mitigation,most current research separately addresses quantum gate noise mitigation and measurement noise mitigation.However,due to the typically high complexity of measurement noise mitigation methods,such as those based on estimating response matrices,the overall complexity of noise mitigation schemes increases when combining measurement noise mitigation with other quantum gate noise mitigation approaches.This paper proposes a low-complexity quantum error mitigation scheme that jointly mitigates quantum gate and measurement noise,specifically when measurement noise manifests as an amplitude damping channel.The proposed scheme requires estimating only three parameters to jointly mitigate both types of noise,whereas the zero-noise extrapolation method enhanced by response matrix estimation requires estimating at least six parameters under the same conditions.
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 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.
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.
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.
文摘There are many theoretical explanations for the mitigation of tornados, storms, and hurricanes and one or two known simulation models that address the reduction of the intensities of these forces. We introduce an innovative methodology that releases environmentally friendly aerosol particles responsible for cloud condensation and weakens the intensities of these forces. For the past nine years, we did several experiments and analyzed the results. Experimental results give evidence to this methodology is practical, environment-friendly, cost-effective, and consistent. In this paper, we described our experiments along with results in three different scenarios such as tornado (March 2021, Georgia USA), storm Claudette (June 2021, Georgia USA), and hurricane Elsa (July 2021, Florida USA). Our experimental outcome and subsequent relevant meteorology data support the reason for mitigating the intensity of these destructive forces in and around the experiment locations.
基金Project(51174206)supported by the National Natural Science Foundation of ChinaProject(2013AA12A201)supported by the National Hi-tech Research and Development Program of China+1 种基金Project(2012ZDP08)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(SZBF2011-6-B35)supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),China
文摘An altemative algorithm for mitigating GPS multipath was presented by integrating unscented Kalman filter （UKF） and wavelet transform with particle filter. Within consideration of particle degeneracy, UKF was taken for drawing particle. To remove the noise from raw data and data processing error, adaptive wavelet filtering with threshold was adopted while data preprocessing and drawing particle. Three algorithms, named EKF-PF, UKF-PF and WM-UKF-PF, were performed for comparison. The proposed WM-UKF-PF algorithm gives better error minimization, and significantly improves performance of multipath mitigation in terms of SNR and coefficient even though it has computation complexity. It is of significance for high-accuracy positioning and non-stationary deformation analysis.
文摘Recent researches focused on developing robust blast load mitigation systems due to the threats of terrorist attacks.One of the main embraced strategies is the structural systems that use mitigation techniques.They are developed from a combination of structural elements and described herein as conventional systems.Among the promising techniques is that redirect the waves propagation through hollow tubes.The blast wave propagation through tubes provides an efficient system since it combines many blast wave phenomena,such as reflection,diffraction,and interaction.In this research,a novel blast load mitigation system,employed as a protection fence,is developed using a technique similar to the technique of the bent tube in manipulating the shock-wave.The relative performance of the novel system to the conventional system is evaluated based on mitigation percent criteria.Performances of both systems are calculated through numerical simulation.The proposed novel system proved to satisfy high performance in mitigating the generated blast waves from charges weight up to 500 kg TNT at relatively small standoff distances(5 m and 8 m).It mitigates at least 94%of the blast waves,which means that only 6%of that blast impulse is considered as the applied load on the targeted structure.
基金Supported by National Natural Science Foundation of China(40765003)Guyuan Meteorological Bureau Science and Technology Program in Ningxia~~
文摘[Objective] The aim was to study the characteristics of the changes of extreme weather climate incidents such as severe drought in northwest and rainstorm in Xiji County of Ningxia. [Method] Precipitation anomaly percentage was applied to divide drought level and for statistics analysis. Seasonal index, linear tendency, and 5-years gliding average were used to reflect the trend of drought changes. The circulation wave of temporal sequence used polynomial expression to simulate the interannual variation scale. The positive part of the polynomial expression used bar chart to simulate interannual variation scale. [Result] The index of drought season from November to June was large. The general trend of annual drought was increasing. The drought of interannual scale was most serious around 1977, about 15 and 20 years. The drought in recent years went up. The general situation of drought, interannual scale and changes of interannual scales from March to May and from September to October were discussed. Based on the weather at 500 hPa, the first rain in Xiji and the drought-turning-into-rain situation were classified. [Conclusion]These may provide reference value to the prevention and mitigation of drought.
基金the National Science Foundation,Earthquake Engineering Research Centers Program through MCEER.
文摘This project is aimed at bridging the three planes,from basic research,through enabling processes,to engineered systems.At the basic research plane,we have been working to improve our collective understanding about obstacles to implementing mitigation practices,owner decision processes (in connection with other MCEER projects),and public policy processes.At the level of enabling processes,we have been seeking to develop an understanding of how obstacles to greater mitigation can be overcome by improved policy design and processes.At the engineered systems plane, our work is intended to result in practical guidelines for devising policies and programs with appropriate motivation and incentives for implementing policies and programs once adopted.This phase of the research has been aimed,first,at a thorough,multidisciplinary review of the literature concerning obstacles to implementation.Second,the research has focused on advancing the state of the art by developing means for integrating the insights offered by diverse perspectives on the implementation process from the several social,behavioral,and decision sciences.The research establishes a basis for testing our understanding of these processes in the case of hospital retrofit decisions.
