Energy-storage systems and their production have attracted significant interest for practical applications.Batteries are the foundation of sustainable energy sources for electric vehicles(EVs),portable electronic devi...Energy-storage systems and their production have attracted significant interest for practical applications.Batteries are the foundation of sustainable energy sources for electric vehicles(EVs),portable electronic devices(PEDs),etc.In recent decades,Lithium-ion batteries(LIBs) have been extensively utilized in largescale energy storage devices owing to their long cycle life and high energy density.However,the high cost and limited availability of Li are the two main obstacles for LIBs.In this regard,sodium-ion batteries(SIBs) are attractive alternatives to LIBs for large-scale energy storage systems because of the abundance and low cost of sodium materials.Cathode is one of the most important components in the battery,which limits cost and performance of a battery.Among the classified cathode structures,layered structure materials have attracted attention because of their high ionic conductivity,fast diffusion rate,and high specific capacity.Here,we present a comprehensive review of the classification of layered structures and the preparation of layered materials.Furthermore,the review article discusses extensively about the issues of the layered materials,namely(1) electrochemical degradation,(2) irreversible structural changes,and(3) structural instability,and also it provides strategies to overcome the issues such as elemental phase composition,a small amount of elemental doping,structural design,and surface alteration for emerging SIBs.In addition,the article discusses about the recent research development on layered unary,binary,ternary,quaternary,quinary,and senary-based O3-and P2-type cathode materials for high-energy SIBs.This review article provides useful information for the development of high-energy layered sodium transition metal oxide P2 and O3-cathode materials for practical SIBs.展开更多
Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effe...Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.展开更多
Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based di...Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based diagnosis,teaching,and research.Although the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved images.Despite the prevalence of 3D volumetric data in medical imaging such as computed tomography(CT),current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images.Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information,including the size,shape,and spatial relations of multiple structures.This process is time-consuming and reliant on users'experience.Methods In this study,we proposed an importance-aware 3D volume visualization method.The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process.We then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.Results Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography(PETCT)images of a non-small cell lung cancer dataset.展开更多
Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC a...Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.展开更多
Lithium-sulfur(Li-S)batteries are one of the most promising modern-day energy supply systems because of their high theoretical energy density and low cost.However,the development of high-energy density Li-S batteries ...Lithium-sulfur(Li-S)batteries are one of the most promising modern-day energy supply systems because of their high theoretical energy density and low cost.However,the development of high-energy density Li-S batteries with high loading of flammable sulfur faces the challenges of electrochemical performance degradation owing to the shuttle effect and safety issues related to fire or explosion accidents.In this work,we report a three-dimensional(3D)conductive nitrogen-doped carbon foam supported electrostatic self-assembled MXene-ammonium polyphosphate(NCF-MXene-APP)layer as a heat-resistant,thermally-insulated,flame-retardant,and freestanding host for Li-S batteries with a facile and costeffective synthesis method.Consequently,through the use of NCF-MXene-APP hosts that strongly anchor polysulfides,the Li-S batteries demonstrate outstanding electrochemical properties,including a high initial discharge capacity of 1191.6 mA h g^(-1),excellent rate capacity of 755.0 mA h g^(-1)at 1 C,and long-term cycling stability with an extremely low-capacity decay rate of 0.12%per cycle at 2 C.More importantly,these batteries can continue to operate reliably under high temperature or flame attack conditions.Thus,this study provides valuable insights into the design of safe high-performance Li-S batteries.展开更多
Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to dete...Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.展开更多
Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learn...Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.展开更多
Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more qual...Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.展开更多
Introduction:Spanning the three stages of the Alzheimer’s disease (AD) continuum,amyloid-beta(Aβ40and Aβ42) oligomers (AβO’s) and tau protein constitute a set of biomarkers ideally suited for the non-invasive mon...Introduction:Spanning the three stages of the Alzheimer’s disease (AD) continuum,amyloid-beta(Aβ40and Aβ42) oligomers (AβO’s) and tau protein constitute a set of biomarkers ideally suited for the non-invasive monitoring of AD (Wolgin et al.,2022).AD progression is correlated with the presence of low molecular weight oligomers and not amyloid plaques.Moreover,low molecular weight AβO is present in the beginning and later stages of disease even when the plaque burden becomes prevalent.Furthermore.展开更多
BACKGROUND Patients with combined hepatocellular carcinoma and cholangiocarcinoma(cHCC-CC)are not traditionally considered eligible for liver transplantation(LT)AIM To compare outcomes between living donor LT(LDLT)pat...BACKGROUND Patients with combined hepatocellular carcinoma and cholangiocarcinoma(cHCC-CC)are not traditionally considered eligible for liver transplantation(LT)AIM To compare outcomes between living donor LT(LDLT)patients with hepatocellular carcinoma(HCC)and LT patients with cHCC-CC and to identify risk factors for tumor recurrence and death after LT in cHCC-CC patients.METHODS Data for pathologically diagnosed cHCC-CC patients(n=111)who underwent LT from 2000 to 2018 were collected for a nine-center retrospective review.Patients(n=141)who received LDLT for HCC at Samsung Medical Center from January 2013 to March 2017 were selected as the control group.Seventy patients in two groups,respectively,were selected by 1:1 matching.RESULTS Cumulative disease-free survival(DFS)and overall survival(OS)in the cHCC-CC group were significantly worse than in the HCC group both before and after matching.Extrahepatic recurrence incidence in the cHCC-CC group was higher than that in the HCC group(75.5%vs 33.3%,P<0.001).Multivariate analysis demonstrated that the cHCC-CC group had significantly higher rates of tumor recurrence and death compared to the HCC group.In cHCC-CC subgroup analysis,frequency of locoregional therapies>3,tumor size>3 cm,and lymph node metastasis were predisposing factors for tumor recurrence in multivariate analysis.Only a maximum tumor size>3 cm was a predisposing factor for death.CONCLUSION The poor prognosis of patients diagnosed with cHCC-CC after LT can be predicted based on the explanted liver.Frequent regular surveillance for cHCC-CC patients should be required for early detection of tumor recurrence.展开更多
The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information...The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.展开更多
As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration.Renewable energy is becoming increasingly important,wit...As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration.Renewable energy is becoming increasingly important,with solar power accounting for the most significant proportion of renewables.As the scale and importance of solar energy have increased,cyber threats against solar power plants have also increased.So,we need an anomaly detection system that effectively detects cyber threats to solar power plants.However,as mentioned earlier,the existing solar power plant anomaly detection system monitors only operating information such as power generation,making it difficult to detect cyberattacks.To address this issue,in this paper,we propose a network packet-based anomaly detection system for the Programmable Logic Controller(PLC)of the inverter,an essential system of photovoltaic plants,to detect cyber threats.Cyberattacks and vulnerabilities in solar power plants were analyzed to identify cyber threats in solar power plants.The analysis shows that Denial of Service(DoS)and Manin-the-Middle(MitM)attacks are primarily carried out on inverters,aiming to disrupt solar plant operations.To develop an anomaly detection system,we performed preprocessing,such as correlation analysis and normalization for PLC network packets data and trained various machine learning-based classification models on such data.The Random Forest model showed the best performance with an accuracy of 97.36%.The proposed system can detect anomalies based on network packets,identify potential cyber threats that cannot be identified by the anomaly detection system currently in use in solar power plants,and enhance the security of solar plants.展开更多
Dear editor,The incidence of renal infarction in patients admitted to the emergency department(ED) is approximately 0.004%.[1]Among patients with renal infarction, bilateral renal involvement has been reported in 28.6...Dear editor,The incidence of renal infarction in patients admitted to the emergency department(ED) is approximately 0.004%.[1]Among patients with renal infarction, bilateral renal involvement has been reported in 28.6% of patients.[2]However, there are very few cases of bilateral renal infarction after a traumatic injury.展开更多
The development of urbanization and industrialization leads to rapid depletion of fossil fuels.Therefore,the production of fuel from renewable resources is highly desired.Electrotechnical energy conversion and storage...The development of urbanization and industrialization leads to rapid depletion of fossil fuels.Therefore,the production of fuel from renewable resources is highly desired.Electrotechnical energy conversion and storage is a benign technique with reliable output and is eco-friendly.Developing an exceptional electrochemical catalyst with tunable properties like a huge specific surface area,porous channels,and abundant active sites is critical points.Recently,Metal-organic frameworks(MOFs)and two-dimensional(2D)transition-metal carbides/nitrides(MXenes)have been extensively investigated in the field of electrochemical energy conversion and storage.However,advances in the research on MOFs are hampered by their limited structural stability and conventionally low electrical conductivity,whereas the practical electrochemical performance of MXenes is impeded by their low porosity,inadequate redox sites,and agglomeration.Consequently,researchers have been designing MOF/MXene nanoarchitectures to overcome the limitations in electrochemical energy conversion and storage.This review explores the recent advances in MOF/MXene nanoarchitectures design strategies,tailoring their properties based on the morphologies(0D,1D,2D,and 3D),and broadening their future opportunities in electrochemical energy storage(batteries,supercapacitors)and catalytic energy conversion(HER,OER,and ORR).The intercalation of MOF in between the MXene layers in the nanoarchitectures functions synergistically to address the issues associated with bare MXene and MOF in the electrochemical energy storage and conversion.This review gives a clear emphasis on the general aspects of MOF/MXene nanoarchitectures,and the future research perspectives,challenges of MOF/MXene design strategies and electrochemical applications are highlighted.展开更多
Lithium dual-ion batteries(LiDIBs)have attracted significant attention owing to the growing demand for modern anode materials with high energy density.Herein,rust encapsulated in graphite was achieved by utilizing amm...Lithium dual-ion batteries(LiDIBs)have attracted significant attention owing to the growing demand for modern anode materials with high energy density.Herein,rust encapsulated in graphite was achieved by utilizing ammonium bicarbonate(ABC)as a template,which resulted in mesoporous Fe3O4embedded in expanded carbon(Fe3O4@G(ABC))via simple ball milling followed by annealing.This self-assembly approach for graphite-encapsulated Fe3O4composites helps enhance the electrochemical performance,such as the cycling stability and superior rate stability(at 3 A/g),with improved conductivity in Li DIBs.Specifically,Fe3O4@G-1:4(ABC)and Fe3O4@G-1:6(ABC)anodes in a half-cell at 0.1 A/g delivered initial capacities of 1390.6 and 824.4 mA h g^(-1),respectively.The optimized anode(Fe3O4@G-1:4(ABC))coupled with the expanded graphite(EG)cathode in Li DIBs provided a substantial initial specific capacity of 260.9 mA h g^(-1)at 1 A/g and a specific capacity regain of 106.3 mA h g^(-1)(at 0.1 A/g)after 250 cycles,with a very high energy density of 387.9 Wh kg^(-1).The strategically designed Fe3O4@G accelerated Li-ion kinetics,alleviated the volume change,and provided an efficient conductive network with excellent mechanical flexibility,resulting in exceptional performance in Li DIBs.Various postmortem analyses of the anode and cathode(XRD,Raman,EDS,and XPS)are presented to explain the intercalation-type electrochemical mechanisms of Li DIBs.This study offers several advantages,including safety,low cost,sustainability,environmental friendliness,and high energy density.展开更多
The performance of a speech emotion recognition(SER)system is heavily influenced by the efficacy of its feature extraction techniques.The study was designed to advance the field of SER by optimizing feature extraction...The performance of a speech emotion recognition(SER)system is heavily influenced by the efficacy of its feature extraction techniques.The study was designed to advance the field of SER by optimizing feature extraction tech-niques,specifically through the incorporation of high-resolution Mel-spectrograms and the expedited calculation of Mel Frequency Cepstral Coefficients(MFCC).This initiative aimed to refine the system’s accuracy by identifying and mitigating the shortcomings commonly found in current approaches.Ultimately,the primary objective was to elevate both the intricacy and effectiveness of our SER model,with a focus on augmenting its proficiency in the accurate identification of emotions in spoken language.The research employed a dual-strategy approach for feature extraction.Firstly,a rapid computation technique for MFCC was implemented and integrated with a Bi-LSTM layer to optimize the encoding of MFCC features.Secondly,a pretrained ResNet model was utilized in conjunction with feature Stats pooling and dense layers for the effective encoding of Mel-spectrogram attributes.These two sets of features underwent separate processing before being combined in a Convolutional Neural Network(CNN)outfitted with a dense layer,with the aim of enhancing their representational richness.The model was rigorously evaluated using two prominent databases:CMU-MOSEI and RAVDESS.Notable findings include an accuracy rate of 93.2%on the CMU-MOSEI database and 95.3%on the RAVDESS database.Such exceptional performance underscores the efficacy of this innovative approach,which not only meets but also exceeds the accuracy benchmarks established by traditional models in the field of speech emotion recognition.展开更多
Unlike external attacks,insider threats arise from legitimate users who belong to the organization.These individuals may be a potential threat for hostile behavior depending on their motives.For insider detection,many...Unlike external attacks,insider threats arise from legitimate users who belong to the organization.These individuals may be a potential threat for hostile behavior depending on their motives.For insider detection,many intrusion detection systems learn and prevent known scenarios,but because malicious behavior has similar patterns to normal behavior,in reality,these systems can be evaded.Furthermore,because insider threats share a feature space similar to normal behavior,identifying them by detecting anomalies has limitations.This study proposes an improved anomaly detection methodology for insider threats that occur in cybersecurity in which a discrete wavelet transformation technique is applied to classify normal vs.malicious users.The discrete wavelet transformation technique easily discovers new patterns or decomposes synthesized data,making it possible to distinguish between shared characteristics.To verify the efficacy of the proposed methodology,experiments were conducted in which normal users and malicious users were classified based on insider threat scenarios provided in Carnegie Mellon University’s Computer Emergency Response Team(CERT)dataset.The experimental results indicate that the proposed methodology with discrete wavelet transformation reduced the false-positive rate by 82%to 98%compared to the case with no wavelet applied.Thus,the proposed methodology has high potential for application to similar feature spaces.展开更多
Cyberattacks targeting industrial control systems(ICS)are becoming more sophisticated and advanced than in the past.A programmable logic controller(PLC),a core component of ICS,controls and monitors sensors and actuat...Cyberattacks targeting industrial control systems(ICS)are becoming more sophisticated and advanced than in the past.A programmable logic controller(PLC),a core component of ICS,controls and monitors sensors and actuators in the field.However,PLC has memory attack threats such as program injection and manipulation,which has long been a major target for attackers,and it is important to detect these attacks for ICS security.To detect PLC memory attacks,a security system is required to acquire and monitor PLC memory directly.In addition,the performance impact of the security system on the PLC makes it difficult to apply to the ICS.To address these challenges,this paper proposes a system to detect PLC memory attacks by continuously acquiring and monitoring PLC memory.The proposed system detects PLC memory attacks by acquiring the program blocks and block information directly from the same layer as the PLC and then comparing them in bytes with previous data.Experiments with Siemens S7-300 and S7-400 PLC were conducted to evaluate the PLC memory detection performance and performance impact on PLC.The experimental results demonstrate that the proposed system detects all malicious organization block(OB)injection and data block(DB)manipulation,and the increment of PLC cycle time,the impact on PLC performance,was less than 1 ms.The proposed system detects PLC memory attacks with a simpler detection method than earlier studies.Furthermore,the proposed system can be applied to ICS with a small performance impact on PLC.展开更多
The smart city comprises various infrastructures,including health-care,transportation,manufacturing,and energy.A smart city’s Internet of Things(IoT)environment constitutes a massive IoT environment encom-passing num...The smart city comprises various infrastructures,including health-care,transportation,manufacturing,and energy.A smart city’s Internet of Things(IoT)environment constitutes a massive IoT environment encom-passing numerous devices.As many devices are installed,managing security for the entire IoT device ecosystem becomes challenging,and attack vectors accessible to attackers increase.However,these devices often have low power and specifications,lacking the same security features as general Information Technology(IT)systems,making them susceptible to cyberattacks.This vulnerability is particularly concerning in smart cities,where IoT devices are connected to essential support systems such as healthcare and transportation.Disruptions can lead to significant human and property damage.One rep-resentative attack that exploits IoT device vulnerabilities is the Distributed Denial of Service(DDoS)attack by forming an IoT botnet.In a smart city environment,the formation of IoT botnets can lead to extensive denial-of-service attacks,compromising the availability of services rendered by the city.Moreover,the same IoT devices are typically employed across various infrastructures within a smart city,making them potentially vulnerable to similar attacks.This paper addresses this problem by designing a defense process to effectively respond to IoT botnet attacks in smart city environ-ments.The proposed defense process leverages the defense techniques of the MITRE D3FEND framework to mitigate the propagation of IoT botnets and support rapid and integrated decision-making by security personnel,enabling an immediate response.展开更多
BACKGROUND Protein C deficiency is typically associated with venous thromboembolism;however,arterial thrombosis has been reported in several cases.We report the case of a patient with pulmonary thromboembolism and dee...BACKGROUND Protein C deficiency is typically associated with venous thromboembolism;however,arterial thrombosis has been reported in several cases.We report the case of a patient with pulmonary thromboembolism and deep vein thrombosis following acute myocardial infarction with high thrombus burden.CASE SUMMARY A 40-year-old man was diagnosed with pulmonary thromboembolism and deep vein thrombosis without any provoking factors.The patient was treated with anticoagulants for six months,which were then discontinued.Three months after the discontinuation of anticoagulant therapy,the patient was hospitalized with chest pain and diagnosed with acute myocardial infarction with high thrombus burden.Additional tests revealed protein C deficiency associated with thrombophilia.The patient was treated with anticoagulants combined with dual antiplatelet agents for 1 year after percutaneous coronary intervention,and no recurrent events were reported during a follow-up period of 5 years.CONCLUSION Recurrent thromboembolic events including acute myocardial infarction with thrombus should be considered an alarming sign of thrombophilia.展开更多
基金supported by a grant from the Subway Fine Dust Reduction Technology Development Project of the Ministry of Land Infrastructure and Transport,Republic of Korea(21QPPWB152306-03)the Basic Science Research Capacity Enhancement Project through a Korea Basic Science Institute(National Research Facilities and Equipment Center)grant funded by the Ministry of Education of the Republic of Korea(2019R1A6C1010016)。
文摘Energy-storage systems and their production have attracted significant interest for practical applications.Batteries are the foundation of sustainable energy sources for electric vehicles(EVs),portable electronic devices(PEDs),etc.In recent decades,Lithium-ion batteries(LIBs) have been extensively utilized in largescale energy storage devices owing to their long cycle life and high energy density.However,the high cost and limited availability of Li are the two main obstacles for LIBs.In this regard,sodium-ion batteries(SIBs) are attractive alternatives to LIBs for large-scale energy storage systems because of the abundance and low cost of sodium materials.Cathode is one of the most important components in the battery,which limits cost and performance of a battery.Among the classified cathode structures,layered structure materials have attracted attention because of their high ionic conductivity,fast diffusion rate,and high specific capacity.Here,we present a comprehensive review of the classification of layered structures and the preparation of layered materials.Furthermore,the review article discusses extensively about the issues of the layered materials,namely(1) electrochemical degradation,(2) irreversible structural changes,and(3) structural instability,and also it provides strategies to overcome the issues such as elemental phase composition,a small amount of elemental doping,structural design,and surface alteration for emerging SIBs.In addition,the article discusses about the recent research development on layered unary,binary,ternary,quaternary,quinary,and senary-based O3-and P2-type cathode materials for high-energy SIBs.This review article provides useful information for the development of high-energy layered sodium transition metal oxide P2 and O3-cathode materials for practical SIBs.
基金the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2020R1A2C1A01011131)the Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073164).
文摘Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.
文摘Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based diagnosis,teaching,and research.Although the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved images.Despite the prevalence of 3D volumetric data in medical imaging such as computed tomography(CT),current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images.Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information,including the size,shape,and spatial relations of multiple structures.This process is time-consuming and reliant on users'experience.Methods In this study,we proposed an importance-aware 3D volume visualization method.The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process.We then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.Results Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography(PETCT)images of a non-small cell lung cancer dataset.
基金supported by the Key Research and Development Project in Anhui Province of China(Grant No.202304a05020059)the Fundamental Research Funds for the Central Universities of China(Grant No.PA2023GDSK0055)the Project of Anhui Province Economic and Information Bureau(Grant No.JB20099).
文摘Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.
基金supported by the National Research Foundation of Korea(NRF-2021R1A2C1008272)supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2021-0-00259,Development of a Fast Wireless Charging System for Portable Terminals with improved heat dissipation and shielding performance)supported by the Applied Basic Research Program of Changzhou City(CJ20220030).
文摘Lithium-sulfur(Li-S)batteries are one of the most promising modern-day energy supply systems because of their high theoretical energy density and low cost.However,the development of high-energy density Li-S batteries with high loading of flammable sulfur faces the challenges of electrochemical performance degradation owing to the shuttle effect and safety issues related to fire or explosion accidents.In this work,we report a three-dimensional(3D)conductive nitrogen-doped carbon foam supported electrostatic self-assembled MXene-ammonium polyphosphate(NCF-MXene-APP)layer as a heat-resistant,thermally-insulated,flame-retardant,and freestanding host for Li-S batteries with a facile and costeffective synthesis method.Consequently,through the use of NCF-MXene-APP hosts that strongly anchor polysulfides,the Li-S batteries demonstrate outstanding electrochemical properties,including a high initial discharge capacity of 1191.6 mA h g^(-1),excellent rate capacity of 755.0 mA h g^(-1)at 1 C,and long-term cycling stability with an extremely low-capacity decay rate of 0.12%per cycle at 2 C.More importantly,these batteries can continue to operate reliably under high temperature or flame attack conditions.Thus,this study provides valuable insights into the design of safe high-performance Li-S batteries.
文摘Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.
基金This Research is funded by Researchers Supporting Project Number(RSPD2024R947),King Saud University,Riyadh,Saudi Arabia.
文摘Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.
文摘Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.
基金National Research Foundation of Korea (NRF) by the Korean Government (2020R1A2B5B01002463&2021R1A6A1A03038996)(to JPH)。
文摘Introduction:Spanning the three stages of the Alzheimer’s disease (AD) continuum,amyloid-beta(Aβ40and Aβ42) oligomers (AβO’s) and tau protein constitute a set of biomarkers ideally suited for the non-invasive monitoring of AD (Wolgin et al.,2022).AD progression is correlated with the presence of low molecular weight oligomers and not amyloid plaques.Moreover,low molecular weight AβO is present in the beginning and later stages of disease even when the plaque burden becomes prevalent.Furthermore.
文摘BACKGROUND Patients with combined hepatocellular carcinoma and cholangiocarcinoma(cHCC-CC)are not traditionally considered eligible for liver transplantation(LT)AIM To compare outcomes between living donor LT(LDLT)patients with hepatocellular carcinoma(HCC)and LT patients with cHCC-CC and to identify risk factors for tumor recurrence and death after LT in cHCC-CC patients.METHODS Data for pathologically diagnosed cHCC-CC patients(n=111)who underwent LT from 2000 to 2018 were collected for a nine-center retrospective review.Patients(n=141)who received LDLT for HCC at Samsung Medical Center from January 2013 to March 2017 were selected as the control group.Seventy patients in two groups,respectively,were selected by 1:1 matching.RESULTS Cumulative disease-free survival(DFS)and overall survival(OS)in the cHCC-CC group were significantly worse than in the HCC group both before and after matching.Extrahepatic recurrence incidence in the cHCC-CC group was higher than that in the HCC group(75.5%vs 33.3%,P<0.001).Multivariate analysis demonstrated that the cHCC-CC group had significantly higher rates of tumor recurrence and death compared to the HCC group.In cHCC-CC subgroup analysis,frequency of locoregional therapies>3,tumor size>3 cm,and lymph node metastasis were predisposing factors for tumor recurrence in multivariate analysis.Only a maximum tumor size>3 cm was a predisposing factor for death.CONCLUSION The poor prognosis of patients diagnosed with cHCC-CC after LT can be predicted based on the explanted liver.Frequent regular surveillance for cHCC-CC patients should be required for early detection of tumor recurrence.
基金funded by the University of Haripur,KP Pakistan Researchers Supporting Project number (PKURFL2324L33)。
文摘The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government(MOTIE)(20224B10100140,50%)the Nuclear Safety Research Program through the Korea Foundation of Nuclear Safety(KoFONS)using the financial resource granted by the Nuclear Safety and Security Commission(NSSC)of the Republic of Korea(No.2106058,40%)the Gachon University Research Fund of 2023(GCU-202110280001,10%)。
文摘As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration.Renewable energy is becoming increasingly important,with solar power accounting for the most significant proportion of renewables.As the scale and importance of solar energy have increased,cyber threats against solar power plants have also increased.So,we need an anomaly detection system that effectively detects cyber threats to solar power plants.However,as mentioned earlier,the existing solar power plant anomaly detection system monitors only operating information such as power generation,making it difficult to detect cyberattacks.To address this issue,in this paper,we propose a network packet-based anomaly detection system for the Programmable Logic Controller(PLC)of the inverter,an essential system of photovoltaic plants,to detect cyber threats.Cyberattacks and vulnerabilities in solar power plants were analyzed to identify cyber threats in solar power plants.The analysis shows that Denial of Service(DoS)and Manin-the-Middle(MitM)attacks are primarily carried out on inverters,aiming to disrupt solar plant operations.To develop an anomaly detection system,we performed preprocessing,such as correlation analysis and normalization for PLC network packets data and trained various machine learning-based classification models on such data.The Random Forest model showed the best performance with an accuracy of 97.36%.The proposed system can detect anomalies based on network packets,identify potential cyber threats that cannot be identified by the anomaly detection system currently in use in solar power plants,and enhance the security of solar plants.
文摘Dear editor,The incidence of renal infarction in patients admitted to the emergency department(ED) is approximately 0.004%.[1]Among patients with renal infarction, bilateral renal involvement has been reported in 28.6% of patients.[2]However, there are very few cases of bilateral renal infarction after a traumatic injury.
基金supported by the National Research Foundation(NRF-2020R1C1C1012655 NRF-2020K1A3A7A09078095,and NRF2021R1A4A5030513,M.Y.)also supported by grants from the National Research Foundation of Korea(2020R1A2C1101561 and 2021M3F6A1085886,M.J.K.)。
文摘The development of urbanization and industrialization leads to rapid depletion of fossil fuels.Therefore,the production of fuel from renewable resources is highly desired.Electrotechnical energy conversion and storage is a benign technique with reliable output and is eco-friendly.Developing an exceptional electrochemical catalyst with tunable properties like a huge specific surface area,porous channels,and abundant active sites is critical points.Recently,Metal-organic frameworks(MOFs)and two-dimensional(2D)transition-metal carbides/nitrides(MXenes)have been extensively investigated in the field of electrochemical energy conversion and storage.However,advances in the research on MOFs are hampered by their limited structural stability and conventionally low electrical conductivity,whereas the practical electrochemical performance of MXenes is impeded by their low porosity,inadequate redox sites,and agglomeration.Consequently,researchers have been designing MOF/MXene nanoarchitectures to overcome the limitations in electrochemical energy conversion and storage.This review explores the recent advances in MOF/MXene nanoarchitectures design strategies,tailoring their properties based on the morphologies(0D,1D,2D,and 3D),and broadening their future opportunities in electrochemical energy storage(batteries,supercapacitors)and catalytic energy conversion(HER,OER,and ORR).The intercalation of MOF in between the MXene layers in the nanoarchitectures functions synergistically to address the issues associated with bare MXene and MOF in the electrochemical energy storage and conversion.This review gives a clear emphasis on the general aspects of MOF/MXene nanoarchitectures,and the future research perspectives,challenges of MOF/MXene design strategies and electrochemical applications are highlighted.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(NRF2022R1F1A1062928)supported by the Basic Science Research Capacity Enhancement Project through a grant from the Korea Basic Science Institute(National Research Facilities and Equipment Center)funded by the Ministry of Education(2019R1A6C1010016)。
文摘Lithium dual-ion batteries(LiDIBs)have attracted significant attention owing to the growing demand for modern anode materials with high energy density.Herein,rust encapsulated in graphite was achieved by utilizing ammonium bicarbonate(ABC)as a template,which resulted in mesoporous Fe3O4embedded in expanded carbon(Fe3O4@G(ABC))via simple ball milling followed by annealing.This self-assembly approach for graphite-encapsulated Fe3O4composites helps enhance the electrochemical performance,such as the cycling stability and superior rate stability(at 3 A/g),with improved conductivity in Li DIBs.Specifically,Fe3O4@G-1:4(ABC)and Fe3O4@G-1:6(ABC)anodes in a half-cell at 0.1 A/g delivered initial capacities of 1390.6 and 824.4 mA h g^(-1),respectively.The optimized anode(Fe3O4@G-1:4(ABC))coupled with the expanded graphite(EG)cathode in Li DIBs provided a substantial initial specific capacity of 260.9 mA h g^(-1)at 1 A/g and a specific capacity regain of 106.3 mA h g^(-1)(at 0.1 A/g)after 250 cycles,with a very high energy density of 387.9 Wh kg^(-1).The strategically designed Fe3O4@G accelerated Li-ion kinetics,alleviated the volume change,and provided an efficient conductive network with excellent mechanical flexibility,resulting in exceptional performance in Li DIBs.Various postmortem analyses of the anode and cathode(XRD,Raman,EDS,and XPS)are presented to explain the intercalation-type electrochemical mechanisms of Li DIBs.This study offers several advantages,including safety,low cost,sustainability,environmental friendliness,and high energy density.
基金supported by the GRRC program of Gyeonggi Province(GRRC-Gachon2023(B02),Development of AI-based medical service technology).
文摘The performance of a speech emotion recognition(SER)system is heavily influenced by the efficacy of its feature extraction techniques.The study was designed to advance the field of SER by optimizing feature extraction tech-niques,specifically through the incorporation of high-resolution Mel-spectrograms and the expedited calculation of Mel Frequency Cepstral Coefficients(MFCC).This initiative aimed to refine the system’s accuracy by identifying and mitigating the shortcomings commonly found in current approaches.Ultimately,the primary objective was to elevate both the intricacy and effectiveness of our SER model,with a focus on augmenting its proficiency in the accurate identification of emotions in spoken language.The research employed a dual-strategy approach for feature extraction.Firstly,a rapid computation technique for MFCC was implemented and integrated with a Bi-LSTM layer to optimize the encoding of MFCC features.Secondly,a pretrained ResNet model was utilized in conjunction with feature Stats pooling and dense layers for the effective encoding of Mel-spectrogram attributes.These two sets of features underwent separate processing before being combined in a Convolutional Neural Network(CNN)outfitted with a dense layer,with the aim of enhancing their representational richness.The model was rigorously evaluated using two prominent databases:CMU-MOSEI and RAVDESS.Notable findings include an accuracy rate of 93.2%on the CMU-MOSEI database and 95.3%on the RAVDESS database.Such exceptional performance underscores the efficacy of this innovative approach,which not only meets but also exceeds the accuracy benchmarks established by traditional models in the field of speech emotion recognition.
基金This work was supported by the Research Program through the National Research Foundation of Korea,NRF-2022R1F1A1073375。
文摘Unlike external attacks,insider threats arise from legitimate users who belong to the organization.These individuals may be a potential threat for hostile behavior depending on their motives.For insider detection,many intrusion detection systems learn and prevent known scenarios,but because malicious behavior has similar patterns to normal behavior,in reality,these systems can be evaded.Furthermore,because insider threats share a feature space similar to normal behavior,identifying them by detecting anomalies has limitations.This study proposes an improved anomaly detection methodology for insider threats that occur in cybersecurity in which a discrete wavelet transformation technique is applied to classify normal vs.malicious users.The discrete wavelet transformation technique easily discovers new patterns or decomposes synthesized data,making it possible to distinguish between shared characteristics.To verify the efficacy of the proposed methodology,experiments were conducted in which normal users and malicious users were classified based on insider threat scenarios provided in Carnegie Mellon University’s Computer Emergency Response Team(CERT)dataset.The experimental results indicate that the proposed methodology with discrete wavelet transformation reduced the false-positive rate by 82%to 98%compared to the case with no wavelet applied.Thus,the proposed methodology has high potential for application to similar feature spaces.
基金supported by the Korea WESTERN POWER(KOWEPO)(2022-Commissioned Research-11,Development of Cyberattack Detection Technology for New and Renewable Energy Control System Using AI(Artificial Intelligence),50%)the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-01806,Development of Security by Design and Security Management Technology in Smart Factory,40%)the Gachon University Research Fund of 2023(GCU-202110280001,10%).
文摘Cyberattacks targeting industrial control systems(ICS)are becoming more sophisticated and advanced than in the past.A programmable logic controller(PLC),a core component of ICS,controls and monitors sensors and actuators in the field.However,PLC has memory attack threats such as program injection and manipulation,which has long been a major target for attackers,and it is important to detect these attacks for ICS security.To detect PLC memory attacks,a security system is required to acquire and monitor PLC memory directly.In addition,the performance impact of the security system on the PLC makes it difficult to apply to the ICS.To address these challenges,this paper proposes a system to detect PLC memory attacks by continuously acquiring and monitoring PLC memory.The proposed system detects PLC memory attacks by acquiring the program blocks and block information directly from the same layer as the PLC and then comparing them in bytes with previous data.Experiments with Siemens S7-300 and S7-400 PLC were conducted to evaluate the PLC memory detection performance and performance impact on PLC.The experimental results demonstrate that the proposed system detects all malicious organization block(OB)injection and data block(DB)manipulation,and the increment of PLC cycle time,the impact on PLC performance,was less than 1 ms.The proposed system detects PLC memory attacks with a simpler detection method than earlier studies.Furthermore,the proposed system can be applied to ICS with a small performance impact on PLC.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-00493,5G Massive Next Generation Cyber Attack Deception Technology Development,60%)supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-01806,Development of Security by Design and Security Management Technology in Smart Factory,30%)this work was supported by the Gachon University Research Fund of 2023(GCU-202106330001%,10%).
文摘The smart city comprises various infrastructures,including health-care,transportation,manufacturing,and energy.A smart city’s Internet of Things(IoT)environment constitutes a massive IoT environment encom-passing numerous devices.As many devices are installed,managing security for the entire IoT device ecosystem becomes challenging,and attack vectors accessible to attackers increase.However,these devices often have low power and specifications,lacking the same security features as general Information Technology(IT)systems,making them susceptible to cyberattacks.This vulnerability is particularly concerning in smart cities,where IoT devices are connected to essential support systems such as healthcare and transportation.Disruptions can lead to significant human and property damage.One rep-resentative attack that exploits IoT device vulnerabilities is the Distributed Denial of Service(DDoS)attack by forming an IoT botnet.In a smart city environment,the formation of IoT botnets can lead to extensive denial-of-service attacks,compromising the availability of services rendered by the city.Moreover,the same IoT devices are typically employed across various infrastructures within a smart city,making them potentially vulnerable to similar attacks.This paper addresses this problem by designing a defense process to effectively respond to IoT botnet attacks in smart city environ-ments.The proposed defense process leverages the defense techniques of the MITRE D3FEND framework to mitigate the propagation of IoT botnets and support rapid and integrated decision-making by security personnel,enabling an immediate response.
文摘BACKGROUND Protein C deficiency is typically associated with venous thromboembolism;however,arterial thrombosis has been reported in several cases.We report the case of a patient with pulmonary thromboembolism and deep vein thrombosis following acute myocardial infarction with high thrombus burden.CASE SUMMARY A 40-year-old man was diagnosed with pulmonary thromboembolism and deep vein thrombosis without any provoking factors.The patient was treated with anticoagulants for six months,which were then discontinued.Three months after the discontinuation of anticoagulant therapy,the patient was hospitalized with chest pain and diagnosed with acute myocardial infarction with high thrombus burden.Additional tests revealed protein C deficiency associated with thrombophilia.The patient was treated with anticoagulants combined with dual antiplatelet agents for 1 year after percutaneous coronary intervention,and no recurrent events were reported during a follow-up period of 5 years.CONCLUSION Recurrent thromboembolic events including acute myocardial infarction with thrombus should be considered an alarming sign of thrombophilia.
