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
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
Cloud computing plays a significant role in modern information technology, providing organizations with numerous benefits, including flexibility, scalability, and cost-efficiency. However, it has become essential for ...Cloud computing plays a significant role in modern information technology, providing organizations with numerous benefits, including flexibility, scalability, and cost-efficiency. However, it has become essential for organizations to ensure the security of their applications, data, and cloud-based networks to use cloud services effectively. This systematic literature review aims to determine the latest information regarding cloud computing security, with a specific emphasis on threats and mitigation strategies. Additionally, it highlights some common threats related to cloud computing security, such as distributed denial-of-service (DDoS) attacks, account hijacking, malware attacks, and data breaches. This research also explores some mitigation strategies, including security awareness training, vulnerability management, security information and event management (SIEM), identity and access management (IAM), and encryption techniques. It discusses emerging trends in cloud security, such as integrating artificial intelligence (AI) and machine learning (ML), serverless computing, and containerization, as well as the effectiveness of the shared responsibility model and its related challenges. The importance of user awareness and the impact of emerging technologies on cloud security have also been discussed in detail to mitigate security risks. A literature review of previous research and scholarly articles has also been conducted to provide insights regarding cloud computing security. It shows the need for continuous research and innovation to address emerging threats and maintain a security-conscious culture in the company.展开更多
The Smart Grid is an enhancement of the traditional grid system and employs new technologies and sophisticated communication techniques for electrical power transmission and distribution. The Smart Grid’s communicati...The Smart Grid is an enhancement of the traditional grid system and employs new technologies and sophisticated communication techniques for electrical power transmission and distribution. The Smart Grid’s communication network shares information about status of its several integrated IEDs (Intelligent Electronic Devices). However, the IEDs connected throughout the Smart Grid, open opportunities for attackers to interfere with the communications and utilities resources or take clients’ private data. This development has introduced new cyber-security challenges for the Smart Grid and is a very concerning issue because of emerging cyber-threats and security incidents that have occurred recently all over the world. The purpose of this research is to detect and mitigate Distributed Denial of Service [DDoS] with application to the Electrical Smart Grid System by deploying an optimized Stealthwatch Secure Network analytics tool. In this paper, the DDoS attack in the Smart Grid communication networks was modeled using Stealthwatch tool. The simulated network consisted of Secure Network Analytic tools virtual machines (VMs), electrical Grid network communication topology, attackers and Target VMs. Finally, the experiments and simulations were performed, and the research results showed that Stealthwatch analytic tool is very effective in detecting and mitigating DDoS attacks in the Smart Grid System without causing any blackout or shutdown of any internal systems as compared to other tools such as GNS3, NeSSi2, NISST Framework, OMNeT++, INET Framework, ReaSE, NS2, NS3, M5 Simulator, OPNET, PLC & TIA Portal management Software which do not have the capability to do so. Also, using Stealthwatch tool to create a security baseline for Smart Grid environment, contributes to risk mitigation and sound security hygiene.展开更多
Humans have always engaged with their surroundings and the ecology in which they live.However,during the industrial age,this contact has been more intense and has had a substantial impact on environment and ecosystems...Humans have always engaged with their surroundings and the ecology in which they live.However,during the industrial age,this contact has been more intense and has had a substantial impact on environment and ecosystems.For example,overexploitation of natural resources,mining,pollution,and deforestation are all elements that negatively affect biodiversity and natural resources.Few studies have been conducted to evaluate the damage caused,despite the significant uncontrolled pressure from human activity.However,maintaining its environment is essential to the survival of coastal fishing.Goal:This study’s goal was to evaluate how human activity affected Tabounsou’s coastal ecology in order to suggest remedial actions for sustainable management.The following was the methodological approach used:executive consultation and archival analysis;stakeholder survey(locals,farmers,salt producers,fishers,and loggers);inventory of species;anthropogenic activity inventory;evaluation of how human activity affects aquatic life in the research region;suggestion and action for sustainable management;Outcome:Executive consultation indicated that the main issues are:construction projects that reduce the estuary’s surface area;agricultural practices such as woodcutting and salt farming;the rise in resource exploitation;noncompliance with fisheries laws;and the catching of young fish.Eighty-three percent of fisherman ditch their nets on the coast after using them,but only seventeen percent burn them.With a 75%frequency rate,the same survey indicates that most fisherman fish around the coast.In the Tabounsou area,according to loggers’survey,68%of the wood cut is Rhizophora,24%is Avicennia,and 8%is Laguncularia.Three fish stocks,representing nine families and nine species,were identified by the species inventory.At 18%and 15%,respectively,the actors most frequently capture the species Pseudotolithus elongatus and Arius parkii.According to a poll of 30 farmers,90%of them apply fertilizer to their soil,while only 10%do not.During the dry season,salt is grown.According to two actors,Bougna Toro Toro produces 100 kg of salt per day,followed by Khoumawadé,which produces 80 kg,and Toumbibougni,which produces 70 kg.展开更多
With the proliferation of online services and applications,adopting Single Sign-On(SSO)mechanisms has become increasingly prevalent.SSO enables users to authenticate once and gain access to multiple services,eliminati...With the proliferation of online services and applications,adopting Single Sign-On(SSO)mechanisms has become increasingly prevalent.SSO enables users to authenticate once and gain access to multiple services,eliminating the need to provide their credentials repeatedly.However,this convenience raises concerns about user security and privacy.The increasing reliance on SSO and its potential risks make it imperative to comprehensively review the various SSO security and privacy threats,identify gaps in existing systems,and explore effective mitigation solutions.This need motivated the first systematic literature review(SLR)of SSO security and privacy,conducted in this paper.The SLR is performed based on rigorous structured research methodology with specific inclusion/exclusion criteria and focuses specifically on the Web environment.Furthermore,it encompasses a meticulous examination and thematic synthesis of 88 relevant publications selected out of 2315 journal articles and conference/proceeding papers published between 2017 and 2024 from reputable academic databases.The SLR highlights critical security and privacy threats relating to SSO systems,reveals significant gaps in existing countermeasures,and emphasizes the need for more comprehensive protection mechanisms.The findings of this SLR will serve as an invaluable resource for scientists and developers interested in enhancing the security and privacy preservation of SSO and designing more efficient and robust SSO systems,thus contributing to the development of the authentication technologies field.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
Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of w...Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of wildfire pre-vention,monitoring,disaster response,and post-fire recovery.This review examines the potential utility of Digital Twin in wildfire management and aims to inspire further exploration and experimentation by researchers and practitioners in the fields of environment,forestry,fire ecology,and firefighting services.By creating virtual replicas of wildfire in the physical world,a Digital Twin platform facilitates data integration from multiple sources,such as remote sensing,weather forecast-ing,and ground-based sensors,providing a holistic view of emergency response and decision-making.Furthermore,Digital Twin can support simulation-based training and scenario testing for prescribed fire planning and firefighting to improve preparedness and response to evacuation and rescue.Successful applications of Digital Twin in wildfire management require horizontal collaboration among researchers,practitioners,and stakeholders,as well as enhanced resource sharing and data exchange.This review seeks a deeper understanding of future wildland fire management from a technological perspective and inspiration of future research and implementation.Further research should focus on refining and validating Digital Twin models and the integration into existing fire management operations,and then demonstrating them in real wildland fires.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
Flooding is a natural phenomenon influenced by various factors and occurs frequently across many regions in Indonesia,including Gedebage in Bandung City,West Java.Gedebage is one of the city’s lowest-lying areas,with...Flooding is a natural phenomenon influenced by various factors and occurs frequently across many regions in Indonesia,including Gedebage in Bandung City,West Java.Gedebage is one of the city’s lowest-lying areas,with an elevation of 666-669 meters above sea level,making it particularly prone to recurrent flooding.The main issue is the absence of an integrated disaster management system.This research aims to identify the drainage system’s asset life cycle(planning,implementation,and operation&maintenance)and assess flood risk in Gedebage.The risk assessment was conducted using questionnaires to evaluate the likelihood and potential impact of risks.In response to major risks,appropriate mitigation strategies were developed.Mitigation efforts included both structural and non-structural measures.The structural mitigation design involved selecting technological alternatives using the Analytical Hierarchy Process(AHP),a decision-making tool that helps compare multiple criteria and alternatives in a structured way.The results indicate that 27% of the assessed risks were unacceptable,42% undesirable,and 31% acceptable.Flood risk in Gedebage can be managed through structural actions,such as drainage revitalization using a closed system,and non-structural strategies,including human-centric,administrative,and cultural approaches.Based on AHP analysis,the most effective technology was a closed drainage system and porous paving blocks.展开更多
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 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Cloud computing plays a significant role in modern information technology, providing organizations with numerous benefits, including flexibility, scalability, and cost-efficiency. However, it has become essential for organizations to ensure the security of their applications, data, and cloud-based networks to use cloud services effectively. This systematic literature review aims to determine the latest information regarding cloud computing security, with a specific emphasis on threats and mitigation strategies. Additionally, it highlights some common threats related to cloud computing security, such as distributed denial-of-service (DDoS) attacks, account hijacking, malware attacks, and data breaches. This research also explores some mitigation strategies, including security awareness training, vulnerability management, security information and event management (SIEM), identity and access management (IAM), and encryption techniques. It discusses emerging trends in cloud security, such as integrating artificial intelligence (AI) and machine learning (ML), serverless computing, and containerization, as well as the effectiveness of the shared responsibility model and its related challenges. The importance of user awareness and the impact of emerging technologies on cloud security have also been discussed in detail to mitigate security risks. A literature review of previous research and scholarly articles has also been conducted to provide insights regarding cloud computing security. It shows the need for continuous research and innovation to address emerging threats and maintain a security-conscious culture in the company.
文摘The Smart Grid is an enhancement of the traditional grid system and employs new technologies and sophisticated communication techniques for electrical power transmission and distribution. The Smart Grid’s communication network shares information about status of its several integrated IEDs (Intelligent Electronic Devices). However, the IEDs connected throughout the Smart Grid, open opportunities for attackers to interfere with the communications and utilities resources or take clients’ private data. This development has introduced new cyber-security challenges for the Smart Grid and is a very concerning issue because of emerging cyber-threats and security incidents that have occurred recently all over the world. The purpose of this research is to detect and mitigate Distributed Denial of Service [DDoS] with application to the Electrical Smart Grid System by deploying an optimized Stealthwatch Secure Network analytics tool. In this paper, the DDoS attack in the Smart Grid communication networks was modeled using Stealthwatch tool. The simulated network consisted of Secure Network Analytic tools virtual machines (VMs), electrical Grid network communication topology, attackers and Target VMs. Finally, the experiments and simulations were performed, and the research results showed that Stealthwatch analytic tool is very effective in detecting and mitigating DDoS attacks in the Smart Grid System without causing any blackout or shutdown of any internal systems as compared to other tools such as GNS3, NeSSi2, NISST Framework, OMNeT++, INET Framework, ReaSE, NS2, NS3, M5 Simulator, OPNET, PLC & TIA Portal management Software which do not have the capability to do so. Also, using Stealthwatch tool to create a security baseline for Smart Grid environment, contributes to risk mitigation and sound security hygiene.
文摘Humans have always engaged with their surroundings and the ecology in which they live.However,during the industrial age,this contact has been more intense and has had a substantial impact on environment and ecosystems.For example,overexploitation of natural resources,mining,pollution,and deforestation are all elements that negatively affect biodiversity and natural resources.Few studies have been conducted to evaluate the damage caused,despite the significant uncontrolled pressure from human activity.However,maintaining its environment is essential to the survival of coastal fishing.Goal:This study’s goal was to evaluate how human activity affected Tabounsou’s coastal ecology in order to suggest remedial actions for sustainable management.The following was the methodological approach used:executive consultation and archival analysis;stakeholder survey(locals,farmers,salt producers,fishers,and loggers);inventory of species;anthropogenic activity inventory;evaluation of how human activity affects aquatic life in the research region;suggestion and action for sustainable management;Outcome:Executive consultation indicated that the main issues are:construction projects that reduce the estuary’s surface area;agricultural practices such as woodcutting and salt farming;the rise in resource exploitation;noncompliance with fisheries laws;and the catching of young fish.Eighty-three percent of fisherman ditch their nets on the coast after using them,but only seventeen percent burn them.With a 75%frequency rate,the same survey indicates that most fisherman fish around the coast.In the Tabounsou area,according to loggers’survey,68%of the wood cut is Rhizophora,24%is Avicennia,and 8%is Laguncularia.Three fish stocks,representing nine families and nine species,were identified by the species inventory.At 18%and 15%,respectively,the actors most frequently capture the species Pseudotolithus elongatus and Arius parkii.According to a poll of 30 farmers,90%of them apply fertilizer to their soil,while only 10%do not.During the dry season,salt is grown.According to two actors,Bougna Toro Toro produces 100 kg of salt per day,followed by Khoumawadé,which produces 80 kg,and Toumbibougni,which produces 70 kg.
文摘With the proliferation of online services and applications,adopting Single Sign-On(SSO)mechanisms has become increasingly prevalent.SSO enables users to authenticate once and gain access to multiple services,eliminating the need to provide their credentials repeatedly.However,this convenience raises concerns about user security and privacy.The increasing reliance on SSO and its potential risks make it imperative to comprehensively review the various SSO security and privacy threats,identify gaps in existing systems,and explore effective mitigation solutions.This need motivated the first systematic literature review(SLR)of SSO security and privacy,conducted in this paper.The SLR is performed based on rigorous structured research methodology with specific inclusion/exclusion criteria and focuses specifically on the Web environment.Furthermore,it encompasses a meticulous examination and thematic synthesis of 88 relevant publications selected out of 2315 journal articles and conference/proceeding papers published between 2017 and 2024 from reputable academic databases.The SLR highlights critical security and privacy threats relating to SSO systems,reveals significant gaps in existing countermeasures,and emphasizes the need for more comprehensive protection mechanisms.The findings of this SLR will serve as an invaluable resource for scientists and developers interested in enhancing the security and privacy preservation of SSO and designing more efficient and robust SSO systems,thus contributing to the development of the authentication technologies field.
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 National Natural Science Foundation of China(NSFC No.52322610)Hong Kong Research Grants Council Theme-based Research Scheme(T22-505/19-N).
文摘Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of wildfire pre-vention,monitoring,disaster response,and post-fire recovery.This review examines the potential utility of Digital Twin in wildfire management and aims to inspire further exploration and experimentation by researchers and practitioners in the fields of environment,forestry,fire ecology,and firefighting services.By creating virtual replicas of wildfire in the physical world,a Digital Twin platform facilitates data integration from multiple sources,such as remote sensing,weather forecast-ing,and ground-based sensors,providing a holistic view of emergency response and decision-making.Furthermore,Digital Twin can support simulation-based training and scenario testing for prescribed fire planning and firefighting to improve preparedness and response to evacuation and rescue.Successful applications of Digital Twin in wildfire management require horizontal collaboration among researchers,practitioners,and stakeholders,as well as enhanced resource sharing and data exchange.This review seeks a deeper understanding of future wildland fire management from a technological perspective and inspiration of future research and implementation.Further research should focus on refining and validating Digital Twin models and the integration into existing fire management operations,and then demonstrating them in real wildland fires.
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.
基金supported by the Research,Community Service,and Innovation/Penelitian,Pengabdian kepada Masyarakat dan Inovasi(P2MI)scheme of the Faculty of Civil and Environmental Engineering,Bandung Institute of Technology/Institut Teknologi Bandung(ITB),grant number[Dean's Decree No.71B/IT1.C06/SK-TA/2023].
文摘Flooding is a natural phenomenon influenced by various factors and occurs frequently across many regions in Indonesia,including Gedebage in Bandung City,West Java.Gedebage is one of the city’s lowest-lying areas,with an elevation of 666-669 meters above sea level,making it particularly prone to recurrent flooding.The main issue is the absence of an integrated disaster management system.This research aims to identify the drainage system’s asset life cycle(planning,implementation,and operation&maintenance)and assess flood risk in Gedebage.The risk assessment was conducted using questionnaires to evaluate the likelihood and potential impact of risks.In response to major risks,appropriate mitigation strategies were developed.Mitigation efforts included both structural and non-structural measures.The structural mitigation design involved selecting technological alternatives using the Analytical Hierarchy Process(AHP),a decision-making tool that helps compare multiple criteria and alternatives in a structured way.The results indicate that 27% of the assessed risks were unacceptable,42% undesirable,and 31% acceptable.Flood risk in Gedebage can be managed through structural actions,such as drainage revitalization using a closed system,and non-structural strategies,including human-centric,administrative,and cultural approaches.Based on AHP analysis,the most effective technology was a closed drainage system and porous paving blocks.
