Parkinson's disease is a neurodegenerative disorder marked by the degeneration of dopaminergic neurons and clinical symptoms such as tremors,rigidity,and slowed movements.A key feature of Parkinson's disease i...Parkinson's disease is a neurodegenerative disorder marked by the degeneration of dopaminergic neurons and clinical symptoms such as tremors,rigidity,and slowed movements.A key feature of Parkinson's disease is the accumulation of misfoldedα-synuclein,forming insoluble Lewy bodies in the substantia nigra pars compacta,which contributes to neurodegeneration.Theseα-synuclein aggregates may act as autoantigens,leading to T-cell-mediated neuroinflammation and contributing to dopaminergic cell death.Our perspective explores the hypothesis that Parkinson's disease may have an autoimmune component,highlighting research that connects peripheral immune responses with neurodegeneration.T cells derived from Parkinson's disease patients appear to have the potential to initiate an autoimmune response againstα-synuclein and its modified peptides,possibly leading to the formation of neo-epitopes.Recent evidence associates Parkinson's disease with abnormal immune responses,as indicated by increased levels of immune cells,such as CD4^(+)and CD8^(+)T cells,observed in both patients and mouse models.The convergence of T cells filtration increasing major histocompatibility complex molecules,and the susceptibility of dopaminergic neurons supports the hypothesis that Parkinson's disease may exhibit autoimmune characteristics.Understanding the immune mechanisms involved in Parkinson's disease will be crucial for developing therapeutic strategies that target the autoimmune aspects of the disease.Novel approaches,including precision medicine based on major histocompatibility complex/human leukocyte antigen typing and early biomarker identification,could pave the way for immune-based treatments aimed at slowing or halting disease progression.This perspective explores the relationship between autoimmunity and Parkinson's disease,suggesting that further research could deepen understanding and offer new therapeutic avenues.In this paper,it is organized to provide a comprehensive perspective on the autoimmune aspects of Parkinson's disease.It investigates critical areas such as the autoimmune response observed in Parkinson's disease patients and the role of autoimmune mechanisms targetingα-synuclein in Parkinson's disease.The paper also examines the impact of CD4~+T cells,specifically Th1 and Th17,on neurons through in vitro and ex vivo studies.Additionally,it explores howα-synuclein influences glia-induced neuroinflammation in Parkinson's disease.The discussion extends to the clinical implications and therapeutic landscape,offering insights into potential treatments.Consequently,we aim to provide a comprehensive perspective on the autoimmune aspects of Parkinson's disease,incorporating both supportive and opposing views on its classification as an autoimmune disorder and exploring implications for clinical applications.展开更多
Polystyrene nanoparticles pose significant toxicological risks to aquatic ecosystems,yet their impact on zebrafish(Danio rerio)embryonic development,particularly erythropoiesis,remains underexplored.This study used si...Polystyrene nanoparticles pose significant toxicological risks to aquatic ecosystems,yet their impact on zebrafish(Danio rerio)embryonic development,particularly erythropoiesis,remains underexplored.This study used single-cell RNA sequencing to comprehensively evaluate the effects of polystyrene nanoparticle exposure on erythropoiesis in zebrafish embryos.In vivo validation experiments corroborated the transcriptomic findings,revealing that polystyrene nanoparticle exposure disrupted erythrocyte differentiation,as evidenced by the decrease in mature erythrocytes and concomitant increase in immature erythrocytes.Additionally,impaired heme synthesis further contributed to the diminished erythrocyte population.These findings underscore the toxic effects of polystyrene nanoparticles on hematopoietic processes,highlighting their potential to compromise organismal health in aquatic environments.展开更多
BACKGROUND Studies on varicose veins have focused its effects on physical function;however,whether nonsurgical treatments alter muscle oxygenation or physical function remains unclear.Moreover,the differences in such ...BACKGROUND Studies on varicose veins have focused its effects on physical function;however,whether nonsurgical treatments alter muscle oxygenation or physical function remains unclear.Moreover,the differences in such functions between individuals with varicose veins and healthy individuals remain unclear.AIM To investigate changes in physical function and the quality of life(QOL)following nonsurgical treatment of patients with varicose veins and determine the changes in their muscle oxygenation during activity.METHODS We enrolled 37 participants(those with varicose veins,n=17;healthy individuals,n=20).We performed the following measurements pre-and post-nonsurgical treatment in the varicose vein patients and healthy individuals:Calf muscle oxygenation during the two-minute step test,open eyes one-leg stance,30 s sit-to-stand test,visual analog scale(VAS)for pain,Pittsburgh sleep quality index,physical activity assessment,and QOL assessment.RESULTS Varicose veins patients and healthy individuals differ in most variables(physical function,sleep quality,and QOL).Varicose veins patients showed significant differences between pre-and post-nonsurgical treatment—results in the 30 sit-to-stand test[14.41(2.45)to 16.35(4.11),P=0.018],two-minute step test[162.29(25.98)to 170.65(23.80),P=0.037],VAS for pain[5.35(1.90)to 3.88(1.73),P=0.004],and QOL[39.34(19.98)to 26.69(17.02),P=0.005];however,no significant difference was observed for muscle oxygenation.CONCLUSION Nonsurgical treatment improved lower extremity function and QOL in varicose veins patients,bringing their condition close to that of healthy individuals.Future studies should include patients with severe varicose veins requiring surgery to confirm our findings.展开更多
As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphas...As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems(ITS).This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering(GOAVNET)algorithm,an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks(VANETs),leveraging the Grasshopper Optimization Algorithm(GOA)to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems(ITS).The proposed GOA-VNET employs an iterative and interactive optimization mechanism to dynamically adjust node positions and cluster configurations,ensuring robust adaptability to varying vehicular densities and transmission ranges.Key features of GOA-VNET include the utilization of attraction zone,repulsion zone,and comfort zone parameters,which collectively enhance clustering efficiency and minimize congestion within Regions of Interest(ROI).By managing cluster configurations and node densities effectively,GOA-VNET ensures balanced load distribution and seamless data transmission,even in scenarios with high vehicular densities and varying transmission ranges.Comparative evaluations against the Whale Optimization Algorithm(WOA)and Grey Wolf Optimization(GWO)demonstrate that GOA-VNET consistently outperforms these methods by achieving superior clustering efficiency,reducing the number of clusters by up to 10%in high-density scenarios,and improving data transmission reliability.Simulation results reveal that under a 100-600 m transmission range,GOA-VNET achieves an average reduction of 8%-15%in the number of clusters and maintains a 5%-10%improvement in packet delivery ratio(PDR)compared to baseline algorithms.Additionally,the algorithm incorporates a heat transfer-inspired load-balancing mechanism,ensuring equitable distribution of nodes among cluster leaders(CLs)and maintaining a stable network environment.These results validate GOA-VNET as a reliable and scalable solution for VANETs,with significant potential to support next-generation ITS.Future research could further enhance the algorithm by integrating multi-objective optimization techniques and exploring broader applications in complex traffic scenarios.展开更多
In network-based intrusion detection practices,there are more regular instances than intrusion instances.Because there is always a statistical imbalance in the instances,it is difficult to train the intrusion detectio...In network-based intrusion detection practices,there are more regular instances than intrusion instances.Because there is always a statistical imbalance in the instances,it is difficult to train the intrusion detection system effectively.In this work,we compare intrusion detection performance by increasing the rarely appearing instances rather than by eliminating the frequently appearing duplicate instances.Our technique mitigates the statistical imbalance in these instances.We also carried out an experiment on the training model by increasing the instances,thereby increasing the attack instances step by step up to 13 levels.The experiments included not only known attacks,but also unknown new intrusions.The results are compared with the existing studies from the literature,and show an improvement in accuracy,sensitivity,and specificity over previous studies.The detection rates for the remote-to-user(R2L)and user-to-root(U2L)categories are improved significantly by adding fewer instances.The detection of many intrusions is increased from a very low to a very high detection rate.The detection of newer attacks that had not been used in training improved from 9%to 12%.This study has practical applications in network administration to protect from known and unknown attacks.If network administrators are running out of instances for some attacks,they can increase the number of instances with rarely appearing instances,thereby improving the detection of both known and unknown new attacks.展开更多
In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the...In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.展开更多
We have studied the effect of magnesia （MgO） addition （0, 5, 10, and 20 mol%） in zirconia at pH values （7, 9, 11）. The magnesia doped zirconia （MgO-ZrO2） has been synthesized by a co-precipitation method using...We have studied the effect of magnesia （MgO） addition （0, 5, 10, and 20 mol%） in zirconia at pH values （7, 9, 11）. The magnesia doped zirconia （MgO-ZrO2） has been synthesized by a co-precipitation method using ammonium hydroxide as a mineralizer. As-prepared samples were characterized by XRD, FE-SEM, and TG-DSC. The XRD results showed that the quantity of tetragonal phase was increased with increasing pH value during synthesis. On the other hand, a decrease in the crystallite size of tetragonal phase was observed with increasing pH value. Therefore, the FE-SEM micrograph showed a clear decline in the particle size with increasing pH value. As-precipitated at pH-11, the addition of 10 mol% of MgO showed nearly pure tetragonal phase with a crystallite size of-34.16 nm.展开更多
Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security.It involves constructing machine learning models using datasets spr...Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security.It involves constructing machine learning models using datasets spread across several data centers,including medical facilities,clinical research facilities,Internet of Things devices,and even mobile devices.The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information,reducing the risk of data loss,privacy breaches,or data exposure.The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources,such as patient records,medical imaging,wearable devices,and clinical research surveys.This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare.It evaluates the effectiveness of federated learning strategies in the field of healthcare.It offers a systematic analysis of federated learning in the healthcare domain,encompassing the evaluation metrics employed.In addition,this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.展开更多
BACKGROUND Neuralgic amyotrophy(NA)is a rare disease with sudden upper limb pain followed by affected muscle weakness.The most commonly affected area in NA is the upper part of the brachial plexus,and the paraspinal m...BACKGROUND Neuralgic amyotrophy(NA)is a rare disease with sudden upper limb pain followed by affected muscle weakness.The most commonly affected area in NA is the upper part of the brachial plexus,and the paraspinal muscles are rarely affected(1.5%),making these cases difficult to distinguish from cervical radiculopathy.CASE SUMMARY A 76-year-old male presented to the emergency department with left hip pain post-fall.After undergoing left femoral neck fracture surgery,he experienced sudden left shoulder pain for 10 days with subsequent left arm weakness.Cervical spine computed tomography revealed mild right asymmetric intervertebral disc bulging with a decreased C5-6disc space.Three weeks later,an electrodiagnostic study confirmed brachial plexopathy findings involving the cervical root.Magnetic resonance neurography was performed for a differential diagnosis.Contrast enhancement was identified at the upper trunk of the brachial plexus,including the C5 nerve root.A suprascapular nerve hourglass-like focal constriction(HLFC)was also identified,confirming NA.After being diagnosed with NA,the patient received 15 mg prednisolone,twice daily,for 3 weeks.Physical therapy was initiated,including left arm strengthening exercises and electrical stimulation therapy.Left shoulder muscle strength significantly improved one CONCLUSION NA's unique features like HLFC and paraspinal involvement are crucial for accurate diagnosis,avoiding confusion with cervical radiculopathy.展开更多
The importance of reducing energy-related environmental and economic issues by extending the lifetime and efficiency of mechanical systems has increased.The use of ultralow friction composite materials is one approach...The importance of reducing energy-related environmental and economic issues by extending the lifetime and efficiency of mechanical systems has increased.The use of ultralow friction composite materials is one approach to eliminate frictional wear.Polytetrafluoroethylene(PTFE)has excellent low friction properties and has been used to reduce frictional wear in various industrial fields.However,degradation of PTFE in natural environments poses challenges owing to its stable chemical structure,which is characterized by strong C-F bonds.Furthermore,PTFE can accumulate in the living body and environment over a long period of time.Consequently,it is resistant to physiological filtration or decomposition.Hence,it is sometimes called a"forever chemical".Therefore,PTFE,which is a type of poly-and perfluoroalkyl substance(PFAS),is increasingly being adopted as a regulated substance.This review focuses on several aspects of PTFE and PFAS,reasons for their adoption as regulated chemicals,and research onalternatives toPTFE,particularlytheuseof liquid lubricants.展开更多
Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision rapidly.Classification and severity grading of DR are difficult processes to accomplish.Traditionally,it depends on o...Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision rapidly.Classification and severity grading of DR are difficult processes to accomplish.Traditionally,it depends on ophthalmoscopically-visible symptoms of growing severity,which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity.This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization(OPSO)algorithm-based Convolutional Neural Network(CNN)Model EOPSO-CNN in order to perform DR detection and grading.The proposed EOPSO-CNN model involves three main processes such as preprocessing,feature extraction,and classification.The proposed model initially involves preprocessing stage which removes the presence of noise in the input image.Then,the watershed algorithm is applied to segment the preprocessed images.Followed by,feature extraction takes place by leveraging EOPSO-CNN model.Finally,the extracted feature vectors are provided to a Decision Tree(DT)classifier to classify the DR images.The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way.The simulation outcome offered the maximum classification with accuracy,sensitivity,and specificity values being 98.47%,96.43%,and 99.02%respectively.展开更多
Dysregulation of cellular processes,such as cell division and proliferation,is a hallmark of cancer and is driven by the aberrant expression of cell cyclerelated genes[1].Aurora kinase B(AURKB),due to its pivotal role...Dysregulation of cellular processes,such as cell division and proliferation,is a hallmark of cancer and is driven by the aberrant expression of cell cyclerelated genes[1].Aurora kinase B(AURKB),due to its pivotal role in mitotic progression,has been implicated in various cancers.Overexpression or hyperactivation of AURKB significantly contributes to tumorigenesis and cancer progression[2].Although mechanisms that enhance AURKB activity,including binding to INCENP,autophosphorylation[3],and ubiquitination by TRAF6[4],have been extensively investigated,regulation of AURKB synthesis,particularly mRNA translation,remains unclear.The translation of eukaryotic mRNAs typically occurs either through cap-dependent scanning or through direct ribosomal binding to specialized RNA elements known as internal ribosome entry sites(IRES).IRES-mediated translation is strongly influenced by specific RNA-binding proteins,known as IRES trans-acting factors(ITAFs).SYNCRIP(Synaptotagmin-binding cytoplasmic RNA-interacting protein),also known as hnRNP Q,has been identified as an ITAF[5],integrating various aspects of RNA metabolism with key cellular processes.Here,we aim to elucidate the mechanism of AURKB mRNA translation and investigate whether SYNCRIP regulates AURKB mRNA translation in lung cancer.展开更多
基金supported by the National Research Foundation of South Korea(2023R1A2C2004516,RS-2023-00219399 to SPY,and 2022R1I1A1A01063513 to MGJ)。
文摘Parkinson's disease is a neurodegenerative disorder marked by the degeneration of dopaminergic neurons and clinical symptoms such as tremors,rigidity,and slowed movements.A key feature of Parkinson's disease is the accumulation of misfoldedα-synuclein,forming insoluble Lewy bodies in the substantia nigra pars compacta,which contributes to neurodegeneration.Theseα-synuclein aggregates may act as autoantigens,leading to T-cell-mediated neuroinflammation and contributing to dopaminergic cell death.Our perspective explores the hypothesis that Parkinson's disease may have an autoimmune component,highlighting research that connects peripheral immune responses with neurodegeneration.T cells derived from Parkinson's disease patients appear to have the potential to initiate an autoimmune response againstα-synuclein and its modified peptides,possibly leading to the formation of neo-epitopes.Recent evidence associates Parkinson's disease with abnormal immune responses,as indicated by increased levels of immune cells,such as CD4^(+)and CD8^(+)T cells,observed in both patients and mouse models.The convergence of T cells filtration increasing major histocompatibility complex molecules,and the susceptibility of dopaminergic neurons supports the hypothesis that Parkinson's disease may exhibit autoimmune characteristics.Understanding the immune mechanisms involved in Parkinson's disease will be crucial for developing therapeutic strategies that target the autoimmune aspects of the disease.Novel approaches,including precision medicine based on major histocompatibility complex/human leukocyte antigen typing and early biomarker identification,could pave the way for immune-based treatments aimed at slowing or halting disease progression.This perspective explores the relationship between autoimmunity and Parkinson's disease,suggesting that further research could deepen understanding and offer new therapeutic avenues.In this paper,it is organized to provide a comprehensive perspective on the autoimmune aspects of Parkinson's disease.It investigates critical areas such as the autoimmune response observed in Parkinson's disease patients and the role of autoimmune mechanisms targetingα-synuclein in Parkinson's disease.The paper also examines the impact of CD4~+T cells,specifically Th1 and Th17,on neurons through in vitro and ex vivo studies.Additionally,it explores howα-synuclein influences glia-induced neuroinflammation in Parkinson's disease.The discussion extends to the clinical implications and therapeutic landscape,offering insights into potential treatments.Consequently,we aim to provide a comprehensive perspective on the autoimmune aspects of Parkinson's disease,incorporating both supportive and opposing views on its classification as an autoimmune disorder and exploring implications for clinical applications.
基金supported by the Institute for Basic Science (IBS-R022-D1)Global Learning&Academic Research Institution for Master’s/Ph D students and Post-Doc Program of the National Research Foundation of Korea Grant funded by the Ministry of Education (RS-2023-00301938)+1 种基金National Research Foundation of Korea Grant funded by the Korean government (RS-2024-00406152,MSIT)Additional financial support was provided by the 2024 Post-Doc Development Program of Pusan National University,Korea Medical Institute,and KREONET。
文摘Polystyrene nanoparticles pose significant toxicological risks to aquatic ecosystems,yet their impact on zebrafish(Danio rerio)embryonic development,particularly erythropoiesis,remains underexplored.This study used single-cell RNA sequencing to comprehensively evaluate the effects of polystyrene nanoparticle exposure on erythropoiesis in zebrafish embryos.In vivo validation experiments corroborated the transcriptomic findings,revealing that polystyrene nanoparticle exposure disrupted erythrocyte differentiation,as evidenced by the decrease in mature erythrocytes and concomitant increase in immature erythrocytes.Additionally,impaired heme synthesis further contributed to the diminished erythrocyte population.These findings underscore the toxic effects of polystyrene nanoparticles on hematopoietic processes,highlighting their potential to compromise organismal health in aquatic environments.
基金Supported by Biomedical Research Institute,Pusan National University Hospital,202200420001.
文摘BACKGROUND Studies on varicose veins have focused its effects on physical function;however,whether nonsurgical treatments alter muscle oxygenation or physical function remains unclear.Moreover,the differences in such functions between individuals with varicose veins and healthy individuals remain unclear.AIM To investigate changes in physical function and the quality of life(QOL)following nonsurgical treatment of patients with varicose veins and determine the changes in their muscle oxygenation during activity.METHODS We enrolled 37 participants(those with varicose veins,n=17;healthy individuals,n=20).We performed the following measurements pre-and post-nonsurgical treatment in the varicose vein patients and healthy individuals:Calf muscle oxygenation during the two-minute step test,open eyes one-leg stance,30 s sit-to-stand test,visual analog scale(VAS)for pain,Pittsburgh sleep quality index,physical activity assessment,and QOL assessment.RESULTS Varicose veins patients and healthy individuals differ in most variables(physical function,sleep quality,and QOL).Varicose veins patients showed significant differences between pre-and post-nonsurgical treatment—results in the 30 sit-to-stand test[14.41(2.45)to 16.35(4.11),P=0.018],two-minute step test[162.29(25.98)to 170.65(23.80),P=0.037],VAS for pain[5.35(1.90)to 3.88(1.73),P=0.004],and QOL[39.34(19.98)to 26.69(17.02),P=0.005];however,no significant difference was observed for muscle oxygenation.CONCLUSION Nonsurgical treatment improved lower extremity function and QOL in varicose veins patients,bringing their condition close to that of healthy individuals.Future studies should include patients with severe varicose veins requiring surgery to confirm our findings.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2024-00337489Development of Data Drift Management Technology to Overcome Performance Degradation of AI Analysis Models).
文摘As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems(ITS).This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering(GOAVNET)algorithm,an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks(VANETs),leveraging the Grasshopper Optimization Algorithm(GOA)to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems(ITS).The proposed GOA-VNET employs an iterative and interactive optimization mechanism to dynamically adjust node positions and cluster configurations,ensuring robust adaptability to varying vehicular densities and transmission ranges.Key features of GOA-VNET include the utilization of attraction zone,repulsion zone,and comfort zone parameters,which collectively enhance clustering efficiency and minimize congestion within Regions of Interest(ROI).By managing cluster configurations and node densities effectively,GOA-VNET ensures balanced load distribution and seamless data transmission,even in scenarios with high vehicular densities and varying transmission ranges.Comparative evaluations against the Whale Optimization Algorithm(WOA)and Grey Wolf Optimization(GWO)demonstrate that GOA-VNET consistently outperforms these methods by achieving superior clustering efficiency,reducing the number of clusters by up to 10%in high-density scenarios,and improving data transmission reliability.Simulation results reveal that under a 100-600 m transmission range,GOA-VNET achieves an average reduction of 8%-15%in the number of clusters and maintains a 5%-10%improvement in packet delivery ratio(PDR)compared to baseline algorithms.Additionally,the algorithm incorporates a heat transfer-inspired load-balancing mechanism,ensuring equitable distribution of nodes among cluster leaders(CLs)and maintaining a stable network environment.These results validate GOA-VNET as a reliable and scalable solution for VANETs,with significant potential to support next-generation ITS.Future research could further enhance the algorithm by integrating multi-objective optimization techniques and exploring broader applications in complex traffic scenarios.
基金the Institute for Information and Communications Technology Planning and Evaluation(IITP)funded by the Korea Government(MSIT)under Grant 20190007960022002(2020000000110).
文摘In network-based intrusion detection practices,there are more regular instances than intrusion instances.Because there is always a statistical imbalance in the instances,it is difficult to train the intrusion detection system effectively.In this work,we compare intrusion detection performance by increasing the rarely appearing instances rather than by eliminating the frequently appearing duplicate instances.Our technique mitigates the statistical imbalance in these instances.We also carried out an experiment on the training model by increasing the instances,thereby increasing the attack instances step by step up to 13 levels.The experiments included not only known attacks,but also unknown new intrusions.The results are compared with the existing studies from the literature,and show an improvement in accuracy,sensitivity,and specificity over previous studies.The detection rates for the remote-to-user(R2L)and user-to-root(U2L)categories are improved significantly by adding fewer instances.The detection of many intrusions is increased from a very low to a very high detection rate.The detection of newer attacks that had not been used in training improved from 9%to 12%.This study has practical applications in network administration to protect from known and unknown attacks.If network administrators are running out of instances for some attacks,they can increase the number of instances with rarely appearing instances,thereby improving the detection of both known and unknown new attacks.
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)by the NRF grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401).
文摘In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.
文摘We have studied the effect of magnesia （MgO） addition （0, 5, 10, and 20 mol%） in zirconia at pH values （7, 9, 11）. The magnesia doped zirconia （MgO-ZrO2） has been synthesized by a co-precipitation method using ammonium hydroxide as a mineralizer. As-prepared samples were characterized by XRD, FE-SEM, and TG-DSC. The XRD results showed that the quantity of tetragonal phase was increased with increasing pH value during synthesis. On the other hand, a decrease in the crystallite size of tetragonal phase was observed with increasing pH value. Therefore, the FE-SEM micrograph showed a clear decline in the particle size with increasing pH value. As-precipitated at pH-11, the addition of 10 mol% of MgO showed nearly pure tetragonal phase with a crystallite size of-34.16 nm.
基金This work was supported by a research fund from Chosun University,2023。
文摘Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security.It involves constructing machine learning models using datasets spread across several data centers,including medical facilities,clinical research facilities,Internet of Things devices,and even mobile devices.The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information,reducing the risk of data loss,privacy breaches,or data exposure.The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources,such as patient records,medical imaging,wearable devices,and clinical research surveys.This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare.It evaluates the effectiveness of federated learning strategies in the field of healthcare.It offers a systematic analysis of federated learning in the healthcare domain,encompassing the evaluation metrics employed.In addition,this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.
文摘BACKGROUND Neuralgic amyotrophy(NA)is a rare disease with sudden upper limb pain followed by affected muscle weakness.The most commonly affected area in NA is the upper part of the brachial plexus,and the paraspinal muscles are rarely affected(1.5%),making these cases difficult to distinguish from cervical radiculopathy.CASE SUMMARY A 76-year-old male presented to the emergency department with left hip pain post-fall.After undergoing left femoral neck fracture surgery,he experienced sudden left shoulder pain for 10 days with subsequent left arm weakness.Cervical spine computed tomography revealed mild right asymmetric intervertebral disc bulging with a decreased C5-6disc space.Three weeks later,an electrodiagnostic study confirmed brachial plexopathy findings involving the cervical root.Magnetic resonance neurography was performed for a differential diagnosis.Contrast enhancement was identified at the upper trunk of the brachial plexus,including the C5 nerve root.A suprascapular nerve hourglass-like focal constriction(HLFC)was also identified,confirming NA.After being diagnosed with NA,the patient received 15 mg prednisolone,twice daily,for 3 weeks.Physical therapy was initiated,including left arm strengthening exercises and electrical stimulation therapy.Left shoulder muscle strength significantly improved one CONCLUSION NA's unique features like HLFC and paraspinal involvement are crucial for accurate diagnosis,avoiding confusion with cervical radiculopathy.
基金funded by the Commercialization Promotion Agency for R&D Outcomes(2023-21040102-00)conducted with the support of the Korea Institute of Industrial Technology as Regional Bearing Industrial Development&Competitiveness Support on Manufacturing Technology(Kitech-UR-24-0015).
文摘The importance of reducing energy-related environmental and economic issues by extending the lifetime and efficiency of mechanical systems has increased.The use of ultralow friction composite materials is one approach to eliminate frictional wear.Polytetrafluoroethylene(PTFE)has excellent low friction properties and has been used to reduce frictional wear in various industrial fields.However,degradation of PTFE in natural environments poses challenges owing to its stable chemical structure,which is characterized by strong C-F bonds.Furthermore,PTFE can accumulate in the living body and environment over a long period of time.Consequently,it is resistant to physiological filtration or decomposition.Hence,it is sometimes called a"forever chemical".Therefore,PTFE,which is a type of poly-and perfluoroalkyl substance(PFAS),is increasingly being adopted as a regulated substance.This review focuses on several aspects of PTFE and PFAS,reasons for their adoption as regulated chemicals,and research onalternatives toPTFE,particularlytheuseof liquid lubricants.
文摘Diabetic Retinopathy(DR)is a significant blinding disease that poses serious threat to human vision rapidly.Classification and severity grading of DR are difficult processes to accomplish.Traditionally,it depends on ophthalmoscopically-visible symptoms of growing severity,which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity.This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization(OPSO)algorithm-based Convolutional Neural Network(CNN)Model EOPSO-CNN in order to perform DR detection and grading.The proposed EOPSO-CNN model involves three main processes such as preprocessing,feature extraction,and classification.The proposed model initially involves preprocessing stage which removes the presence of noise in the input image.Then,the watershed algorithm is applied to segment the preprocessed images.Followed by,feature extraction takes place by leveraging EOPSO-CNN model.Finally,the extracted feature vectors are provided to a Decision Tree(DT)classifier to classify the DR images.The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way.The simulation outcome offered the maximum classification with accuracy,sensitivity,and specificity values being 98.47%,96.43%,and 99.02%respectively.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2022R1C1C1006181,2022R1A6A3A13071217,and RS-2023-00272063).
文摘Dysregulation of cellular processes,such as cell division and proliferation,is a hallmark of cancer and is driven by the aberrant expression of cell cyclerelated genes[1].Aurora kinase B(AURKB),due to its pivotal role in mitotic progression,has been implicated in various cancers.Overexpression or hyperactivation of AURKB significantly contributes to tumorigenesis and cancer progression[2].Although mechanisms that enhance AURKB activity,including binding to INCENP,autophosphorylation[3],and ubiquitination by TRAF6[4],have been extensively investigated,regulation of AURKB synthesis,particularly mRNA translation,remains unclear.The translation of eukaryotic mRNAs typically occurs either through cap-dependent scanning or through direct ribosomal binding to specialized RNA elements known as internal ribosome entry sites(IRES).IRES-mediated translation is strongly influenced by specific RNA-binding proteins,known as IRES trans-acting factors(ITAFs).SYNCRIP(Synaptotagmin-binding cytoplasmic RNA-interacting protein),also known as hnRNP Q,has been identified as an ITAF[5],integrating various aspects of RNA metabolism with key cellular processes.Here,we aim to elucidate the mechanism of AURKB mRNA translation and investigate whether SYNCRIP regulates AURKB mRNA translation in lung cancer.
