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
As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose signific...As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose significant risks by exploiting shared host resources,enabling privilege escalation,or launching large-scale attacks such as cryptomining and botnet activities.Therefore,developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems.To this end,we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter(eBPF)to monitor container activities directly within the Linux kernel.The framework simultaneously collects flow-based network metadata and host-based system-call traces,transforms them into machine-learning features,and applies multi-class classification models to distinguish malicious containers from benign ones.Using six malicious and four benign container scenarios,our evaluation shows that runtime detection is feasible with high accuracy:flow-based detection achieved 87.49%,while host-based detection using system-call sequences reached 98.39%.The performance difference is largely due to similar communication patterns exhibited by certain malware families which limit the discriminative power of flow-level features.Host-level monitoring,by contrast,exposes fine-grained behavioral characteristics,such as file-system access patterns,persistence mechanisms,and resource-management calls that do not appear in network metadata.Our results further demonstrate that both monitoring modality and preprocessing strategy directly influence model performance.More importantly,combining flow-based and host-based telemetry in a complementary hybrid approach resolves classification ambiguities that arise when relying on a single data source.These findings underscore the potential of eBPF-based hybrid analysis for achieving accurate,low-overhead,and behavior-aware runtime security in containerized environments,and they establish a practical foundation for developing adaptive and scalable detection mechanisms in modern cloud systems.展开更多
With the growing complexity and decentralization of network systems,the attack surface has expanded,which has led to greater concerns over network threats.In this context,artificial intelligence(AI)-based network intr...With the growing complexity and decentralization of network systems,the attack surface has expanded,which has led to greater concerns over network threats.In this context,artificial intelligence(AI)-based network intrusion detection systems(NIDS)have been extensively studied,and recent efforts have shifted toward integrating distributed learning to enable intelligent and scalable detection mechanisms.However,most existing works focus on individual distributed learning frameworks,and there is a lack of systematic evaluations that compare different algorithms under consistent conditions.In this paper,we present a comprehensive evaluation of representative distributed learning frameworks—Federated Learning(FL),Split Learning(SL),hybrid collaborative learning(SFL),and fully distributed learning—in the context of AI-driven NIDS.Using recent benchmark intrusion detection datasets,a unified model backbone,and controlled distributed scenarios,we assess these frameworks across multiple criteria,including detection performance,communication cost,computational efficiency,and convergence behavior.Our findings highlight distinct trade-offs among the distributed learning frameworks,demonstrating that the optimal choice depends strongly on systemconstraints such as bandwidth availability,node resources,and data distribution.This work provides the first holistic analysis of distributed learning approaches for AI-driven NIDS and offers practical guidelines for designing secure and efficient intrusion detection systems in decentralized environments.展开更多
The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials off...The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.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
Non-suicidal self-injury(NSSI)is a prevalent and concerning issue in adolescent mental health,often intertwined with depressive symptoms.Despite extensive research on NSSI,a comprehensive understanding of its multifac...Non-suicidal self-injury(NSSI)is a prevalent and concerning issue in adolescent mental health,often intertwined with depressive symptoms.Despite extensive research on NSSI,a comprehensive understanding of its multifaceted nature and the intricate interplay of risk and resilience factors remains crucial.This Letter to the Editor examines a novel study by Yang et al,which utilized latent profile analysis and network analysis to delineate distinct NSSI subtypes within a Chinese adolescent population and investigate the underlying dynamics of associated factors.The study identifies three distinct NSSI subtypes:NSSI with depression,NSSI without depression,and neither,underscoring bullying as a prominent risk factor.Concurrently,the findings emphasized the pivotal role of emotional regulation and family support as protective factors.The focus of this article is to contextualize these findings within the broader framework of adolescent mental health and to highlight their implications for developing targeted interventions.These insights not only advance our understanding of adolescent NSSI but also provide a foundation for the development of targeted interventions that address the identified risk and protective factors.By focusing on these critical areas,mental health professionals can implement more effective strategies to mitigate NSSI behaviors and cultivate resilience in this vulnerable population.展开更多
OBJECTIVE:To determine direct targeting of localized adiposity through Morus alba Linne bark injection based on pharmacology network analysis.METHODS:Male C57BL/6J mice were fed a high-fat diet(HFD)to induce obesity.A...OBJECTIVE:To determine direct targeting of localized adiposity through Morus alba Linne bark injection based on pharmacology network analysis.METHODS:Male C57BL/6J mice were fed a high-fat diet(HFD)to induce obesity.After 6 weeks on HFD,the water extract of Morus alba L.bark(MAB,2 mg/mL)was locally injected into one inguinal fat pad,while saline was injected into the other side,3 times/week for 6 weeks(n=6/group).The water extract of MAB was freeze-dried and then diluted in saline before use.RESULTS:HFD-fed mice treated with local MAB topical injection showed reduced adipocyte weight and size in inguinal fat pads by dual-energy X-ray absorptiometry.No toxicity changes seen in liver,spleen,kidney tissue,or alanine aminotransferase/aspartate aminotransferase levels in serum by MAB injection.Protein levels of phosphorylated insulin receptor substrate-1 and glucose transporter type 4,and mRNA expression of adiponectin,were increased in inguinal adipose tissue injected with MAB locally.Locally MAB injection led to a decrease in glucose-6-phosphatase and phosphoenolpyruvate carboxykinase,linked to gluconeogenesis,while forkhead box protein O1,which regulates these factors,was increased.Moreover,there was an increase in adenosine 5‘-monophosphate-activated protein kinase,related to lipogenesis,as well as elevated levels of hormone-sensitive lipase and fatty acid synthase,both associated with lipolysis.These results support the'insulin signaling pathway'and'regulation of lipolysis in adipocytes'identified in the Kyoto Encyclopedia of Genes and Genomes pathway through network analysis.CONCLUSION:This study suggests that MAB topical injection exhibits localized fat reduction by inhibiting insulin resistance,gluconeogenesis and lipogenesis mediator,while activating lipolysis enzymes within targeted adipose site.展开更多
The Internet of Things(IoT)is extensively applied across various industrial domains,such as smart homes,factories,and intelligent transportation,becoming integral to daily life.Establishing robust policies for managin...The Internet of Things(IoT)is extensively applied across various industrial domains,such as smart homes,factories,and intelligent transportation,becoming integral to daily life.Establishing robust policies for managing and governing IoT devices is imperative.Secure authentication for IoT devices in resource-constrained environments remains challenging due to the limitations of conventional complex protocols.Prior methodologies enhanced mutual authentication through key exchange protocols or complex operations,which are impractical for lightweight devices.To address this,our study introduces the privacy-preserving software-defined range proof(SDRP)model,which achieves secure authentication with low complexity.SDRP minimizes the overhead of confidentiality and authentication processes by utilizing range proof to verify whether the attribute information of a user falls within a specific range.Since authentication is performed using a digital ID sequence generated from indirect personal data,it can avoid the disclosure of actual individual attributes.Experimental results demonstrate that SDRP significantly improves security efficiency,increasing it by an average of 93.02%compared to conventional methods.It mitigates the trade-off between security and efficiency by reducing leakage risk by an average of 98.7%.展开更多
As cyberattacks become increasingly sophisticated and intelligent,demand for machine-learning-based anomaly detection systems is growing.However,conventional systems generally assume a trusted server environment,where...As cyberattacks become increasingly sophisticated and intelligent,demand for machine-learning-based anomaly detection systems is growing.However,conventional systems generally assume a trusted server environment,where traffic data is collected and analyzed in plaintext.This assumption introduces inherent privacy risks,as privacy-sensitive information may be exposed if the server is compromised or misused.To address this limitation,privacy-preserving anomaly detection approaches have been actively studied,enabling anomaly detection to be performed directly on encrypted traffic without revealing privacy-sensitive data.While these approaches offer strong confidentiality guarantees,they suffer from significant drawbacks,including substantial computational overhead,high latency,and degraded detection accuracy.To overcome these limitations,we propose a privacy-aware anomaly detection(PAAD)model that adaptively applies homomorphic encryption based on the privacy sensitivity of incoming traffic.Instead of encrypting all data indiscriminately,PAAD dynamically determines whether traffic should be processed in plaintext or ciphertext and performs homomorphic inference only for privacy-sensitive data.This selective encryption strategy effectively balances privacy protection and system efficiency.Extensive experiments conducted under diverse network environments demonstrate that the proposed PAAD model significantly outperforms conventional anomaly detection models.In particular,PAAD improves detection accuracy by up to 73%,reduces latency by up to 8.6 times,and achieves negligible information leakage,highlighting its practicality for real-world privacy-sensitive network monitoring scenarios.展开更多
Objective:Cisplatin is a widely used chemotherapeutic agent due to its ability to damage DNA in the treatment of cancer.However,its clinical application is often limited by adverse effects on normal tissues,especially...Objective:Cisplatin is a widely used chemotherapeutic agent due to its ability to damage DNA in the treatment of cancer.However,its clinical application is often limited by adverse effects on normal tissues,especially the kidneys.Understanding the molecular mechanisms of cisplatin-induced nephrotoxicity is crucial for developing strategies to mitigate its side effects.In this study,we aimed to elucidate the molecular mechanisms underlying cisplatin-induced DNA damage and apoptosis in human renal epithelial cells,with a focus on key signaling pathways and mediators that drive nephrotoxicity.Methods:To explore these mechanisms,human proximal tubule epithelial cells(HK-2)were treated with cisplatin.The study assessed DNA damage response(DDR)and stress-related protein expression,cell cycle distribution,and apoptosis.Activation of mitogen-activated protein kinases(MAPKs),particularly Extracellular signal-regulated Kinase(ERK),was analyzed,along with the expression and functional role of activating transcription factor 3(ATF3)and tumor protein p53(p53).Results:Cisplatin treatment upregulated DDR and stress response proteins,induced S phase arrest,and increased the SubG1 population,indicating apoptotic cell death.ERK was identified as a critical mediator of cisplatin-induced DNA damage and stress responses.ATF3 expression was significantly elevated in an ERK-dependent manner and required p53 activation.Knockdown of ATF3 reduced cisplatin-induced DNA damage,highlighting its role in the cytotoxic response.Conclusions:Cisplatin induces nephrotoxicity through ERK-and p53-dependent upregulation of ATF3,which is associated with DNA damage and cell death,suggesting a modulatory role in the cellular stress response.These findings provide novel insights into the molecular basis of cisplatin-induced renal injury and suggest potential therapeutic targets to alleviate its adverse effects.展开更多
Antimony-doped tin oxide(ATO) nanoparticles with an average size of ～ 6 nm were prepared by co-precipitation and subsequent heat treatment. Graphitic carbon nitride(g-CN)/ATO hybrid nanocomposite was designed by the ...Antimony-doped tin oxide(ATO) nanoparticles with an average size of ～ 6 nm were prepared by co-precipitation and subsequent heat treatment. Graphitic carbon nitride(g-CN)/ATO hybrid nanocomposite was designed by the combination of thermally synthesized g-CN and ATO nanoparticles by ultrasonication. The materials were characterized using N2 adsorption/desorption(BET), X-ray diffraction(XRD), scanning electron microscopy(SEM), energy dispersive spectroscopy(EDS), transmission electron microscopy(TEM) and Fourier transform infrared spectroscopy(FTIR). A mixture of five volatile organic compounds(VOCs, chloroform, benzene, toluene, xylene and styrene) was used to compare the adsorption capacity of the samples. The adsorption capacity of ATO nanoparticles was improved by the addition of g-CN. Experimental data showed that, among the five VOCs,chloroform was the least adsorbed, regardless of the samples. The g-CN/ATO showed nearly three times greater adsorption capacity for the VOC mixture than pure ATO. The unchanged efficiency of VOC adsorption during cyclic use demonstrated the completely reversible adsorption and desorption behavior of the nanocomposite at room conditions. This economically and environmentally friendly material can be a practical solution for outdoor and indoor VOC removal.展开更多
Antioxidant activities of W and E extracts obtained from dried boxthorn (Lycium chinensis) fruit were measured based on DPPH radical scavenging and reducing powers, and their relationships with total phenolics, flavon...Antioxidant activities of W and E extracts obtained from dried boxthorn (Lycium chinensis) fruit were measured based on DPPH radical scavenging and reducing powers, and their relationships with total phenolics, flavonoid content, and antioxidant activity were investigated. A linear correlation among antioxidant activity, total phenolics, and flavonoid content was observed in concentration-dependent mode. Both extracts showed > 95% DPPH radical-scavenging activity and the higher reducing power of 3200 ppm at the same concentration. The antioxidant potential of both extracts were compared with those of commercial antioxidants such as BHA, BHT, TBHQ, ferulic acid, and α-tocopherol using H2O2 scavenging activity, inhibition of linoleic acid peroxidation, inhibition of hemolysis of rat erythrocyte induced by peroxyl radicals, and inhibition of Fe2+-induced lipid peroxidation using rat brain tissue. In the H2O2 scavenging activity, E extract showed a comparable significant antioxidant power, comparable to commercial antioxidants, and no signifi-cant difference (P > 0.05) was found between W and E extracts on inhibition of the linoleic acid peroxidation. Whereas W extract exhibited a significant power in the hemolysis of rat erythrocytes, none was observed in E extract. In the Fe-induced lipid peroxidation using rat brain tissue, no significant difference (P > 0.05) was found between both ex-tracts, showing a comparable activity with those of synthetic antioxidants. Both W and E extracts of dried boxthorn (Lycium chinensis) fruit may have a potential as natural antioxidants to replace synthetic antioxidants.展开更多
As time and space constraints decrease due to the development of wireless communication network technology,the scale and scope of cyber-attacks targeting the Internet of Things(IoT)are increasing.However,it is difficu...As time and space constraints decrease due to the development of wireless communication network technology,the scale and scope of cyber-attacks targeting the Internet of Things(IoT)are increasing.However,it is difficult to apply high-performance security modules to the IoT owing to the limited battery,memory capacity,and data transmission performance depend-ing on the size of the device.Conventional research has mainly reduced power consumption by lightening encryption algorithms.However,it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security perfor-mance.In this study,we propose wake-up security(WuS),a low-power security architecture that can utilize high-performance security algorithms in an IoT environment.By introducing a small logic that performs anomaly detection on the IoT platform and executes the security module only when necessary according to the anomaly detection result,WuS improves security and power efficiency while using a relatively high-complexity security module in a low-power environment compared to the conventional method of periodically exe-cuting a high-performance security module.In this study,a Python simulator based on the UNSW-NB15 dataset is used to evaluate the power consumption,latency,and security of the proposed method.The evaluation results reveal that the power consumption of the proposed WuS mechanism is approxi-mately 51.8%and 27.2%lower than those of conventional high-performance security and lightweight security modules,respectively.Additionally,the laten-cies are approximately 74.8%and 65.9%lower,respectively.Furthermore,the WuS mechanism achieved a high detection accuracy of approximately 96.5%or greater,proving that the detection efficiency performance improved by approximately 33.5%compared to the conventional model.The performance evaluation results for the proposed model varied depending on the applied anomaly-detection model.Therefore,they can be used in various ways by selecting suitable models based on the performance levels required in each industry.展开更多
Comparative antioxidant activities of the water and ethanol extracts obtained from dried citrus fruit (Citrus unshiu) peel were determined using chemical and biochemical in vitro assays. Chemical assays were used for ...Comparative antioxidant activities of the water and ethanol extracts obtained from dried citrus fruit (Citrus unshiu) peel were determined using chemical and biochemical in vitro assays. Chemical assays were used for evaluation of 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging, hydrogen peroxide scavenging, and reducing power of both extracts and their total phenolic, flavonoid and tannin contents and antioxidant activities were investigated. Biochemical assays were performed to evaluate the inhibition activities of AAPH-induced rat RBC hemolysis and Fe2+-induced lipid peroxidation using rat brain tissue cells. Linear correlation between the antioxidant activities of both extracts were determined by chemical assays, and total phenolic, flavonoid and tannin contents was observed in concentration-dependent mode. Both extracts showed >95% DPPH radical scavenging and >85% hydrogen peroxide scavenging, and higher reducing capacity at the same level of 3200 ppm. In the inhibition activity of AAPH-induced hemolysis, water extracts showed a strong activity in concentration-dependent mode up to 1600 ppm with no statistical difference found between 1600 and 3200 ppm (P > 0.05). In the inhibition activity of Fe2+-induced lipid peroxidation, ethanol extracts showed the higher inhibition percentage of lipid peroxidation than those of water extracts at the same concentration with no significant difference (P > 0.05) found in the range of 800 to 3200 ppm. The extracts of dried Citrus unshiu peel may be considered as potential antioxidant ingredients of functional food depending on the conditions at which reactive oxygen species are implicated.展开更多
The wide use of manganese dioxide(MnO_(2))as an electrode in all-solid-state asymmetric supercapacitors(ASCs)remains challenging because of its low electrical conductivity.This complication can be circumvented by intr...The wide use of manganese dioxide(MnO_(2))as an electrode in all-solid-state asymmetric supercapacitors(ASCs)remains challenging because of its low electrical conductivity.This complication can be circumvented by introducing trivalent gadolinium(Gd)ions into the MnO_(2).Herein,we describe the successful hydrothermal synthesis of crystalline Gd-doped MnO_(2) nanorods with Ni(OH)_(2) nanosheets as cathode,which we combined with Fe_(3)O_(4)/GO nanospheres as anode for all-solid-state ASCs.Electrochemical tests dem on strate that Gd dopi ng sign ifica ntly affected the electrochemical activities of the MnO_(2),which was further enhanced by introducing Ni(OH)_(2).The GdMnO_(2)/Ni(OH)_(2) electrode offers sufficient surface electrochemical activity and exhibits excellent specific capacity of 121.8 mA h g^(-1),at 1A g^(-1),appealing rate performance,and ultralong lifetime stability(99.3%retention after 10,000 discharge tests).Furthermore,the GdMnO_(2)/Ni(OH)_(2)//PVA/KOH//Fe_(3)O_(4)/GO solid-state ASC device offers an impressive specific energy density(60.25 W h kg^(-1))at a high power density(2332 W kg^(-1)).This investigation thus shows its large potential in developing novel approaches to energy storage devices.展开更多
In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need t...In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need to apply various technologies for automation and control.This fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous devices.One of the major protocols widely used today in industrial electronic devices is Modbus.However,data generated by Modbus devices cannot be understood by IoT applications using different protocols,so it should be applied in a couple with an IoT service layer platform.oneM2M,a global IoT standard,can play the role of interconnecting various protocols,as it provides flexible tools suitable for building an interworking framework for industrial services.Therefore,in this paper,we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M standards.In the proposed architecture,we introduce the way to model Modbus data as oneM2M resources,rules to map them to each other,procedures required to establish interoperable communication,and optimization methods for this architecture.We analyze our solution and provide an evaluation by implementing it based on a solar power management use case.The results demonstrate that our model is feasible and can be applied to real case scenarios.展开更多
With the introduction of 5G technology,the application of Internet of Things(IoT)devices is expanding to various industrial fields.However,introducing a robust,lightweight,low-cost,and low-power security solution to t...With the introduction of 5G technology,the application of Internet of Things(IoT)devices is expanding to various industrial fields.However,introducing a robust,lightweight,low-cost,and low-power security solution to the IoT environment is challenging.Therefore,this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT environment.The first method,compressed sensing and learning(CSL),compresses an event log in a bitmap format to quickly detect attacks.Then,the attack log is detected using a machine-learning classification model.The second method,precise re-learning after CSL(Ra-CSL),comprises a two-step training.It uses CSL as the 1st step analyzer,and the 2nd step analyzer is applied using the original dataset for a log that is detected as an attack in the 1st step analyzer.In the experiment,the bitmap rule was set based on the boundary value,which was 99.6%true positive on average for the attack and benign data found by analyzing the training data.Experimental results showed that the CSL was effective in reducing the training and detection time,and Ra-CSL was effective in increasing the detection rate.According to the experimental results,the data compression technique reduced the memory size by up to 20%and the training and detection times by 67%when compared with the conventional technique.In addition,the proposed technique improves the detection accuracy;the Naive Bayes model with the highest performance showed a detection rate of approximately 99%.展开更多
This paper proposes the multiple-input multiple-output(MIMO)detection scheme by using the deep neural network(DNN)based ensemble machine learning for higher error performance in wireless communication systems.For the ...This paper proposes the multiple-input multiple-output(MIMO)detection scheme by using the deep neural network(DNN)based ensemble machine learning for higher error performance in wireless communication systems.For the MIMO detection based on the ensemble machine learning,all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models.In the offline learning,the received signals and channel coefficients are set to input data,and the labels which correspond to transmit symbols are set to output data.In the online learning,the perfectly learned models are used for signal detection where the models have fixed bias and weights.For performance improvement,the proposed scheme uses the majority vote and the maximum probability as the methods of the model combinations for obtaining diversity gains at the MIMO receiver.The simulation results show that the proposed scheme has improved symbol error rate(SER)performance without additional receive antennas.展开更多
We investigated whether inhibiting phosphorylated p70S6K （p-p70S6K） suppresses the proliferation and growth of noninvasive low-grade urothelial carcinoma （LG-URCa） in vitro and whether p-p70S6K can serve as a pred...We investigated whether inhibiting phosphorylated p70S6K （p-p70S6K） suppresses the proliferation and growth of noninvasive low-grade urothelial carcinoma （LG-URCa） in vitro and whether p-p70S6K can serve as a predictive biomarker for the recurrence of noninvasive LG-URCa of the bladder in patients. We constructed a tissue microarray （TMA） for 95 LG-URCa and 35 benign urothelium samples and performed immunohistochemical staining for p-p70S6K and p-4E-BP1. A Cox regression model was used to investigate the predictive factors for recurrence of LG-URCa. We investigated the dose-dependent antiproliferative effect of rapamycin, its antiproliferative effect and the growth-inhibition effect of p70S6K siRNA transfection in RT4 and 253J cell lines. The pT1 staged group （P 〈 0.05; hazard ratio （HR）, 2.415） and the high p-p70S6K staining group （P 〈 0.05; HR, 2.249） were independent factors for predicting recurrence. Rapamycin inhibited RT4 and 253J cell proliferation in a dose-dependent manner （r = -0.850, P 〈 0.001 in RT4 cells; r = -0.835, P 〈 0.001 in 253J cells）. RT4 and 253J cell proliferation and growth were inhibited by the transfection of p70S6K siRNA and rapamycin, respectively （P 〈 0.05）. Transfection of p70S6K siRNA resulted in inhibitory effects on cell proliferation and growth that were similar to those of rapamycin. Our results suggest that inhibiting p70S6K phosphorylation is important to prevent recurrence and that p70S6K phosphorylation can be used as a molecular biomarker to predict recurrence of certain LG-URCa of the bladder.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
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 National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00351898 and No.RS-2025-02263915)the MOTIE under Training Industrial Security Specialist forHigh-Tech Industry(RS-2024-00415520)supervised by theKorea Institute for Advancement of Technology(KIAT)+1 种基金the MSIT under the ICAN(ICT Challenge and Advanced Network of HRD)program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As containerized environments become increasingly prevalent in cloud-native infrastructures,the need for effective monitoring and detection of malicious behaviors has become critical.Malicious containers pose significant risks by exploiting shared host resources,enabling privilege escalation,or launching large-scale attacks such as cryptomining and botnet activities.Therefore,developing accurate and efficient detection mechanisms is essential for ensuring the security and stability of containerized systems.To this end,we propose a hybrid detection framework that leverages the extended Berkeley Packet Filter(eBPF)to monitor container activities directly within the Linux kernel.The framework simultaneously collects flow-based network metadata and host-based system-call traces,transforms them into machine-learning features,and applies multi-class classification models to distinguish malicious containers from benign ones.Using six malicious and four benign container scenarios,our evaluation shows that runtime detection is feasible with high accuracy:flow-based detection achieved 87.49%,while host-based detection using system-call sequences reached 98.39%.The performance difference is largely due to similar communication patterns exhibited by certain malware families which limit the discriminative power of flow-level features.Host-level monitoring,by contrast,exposes fine-grained behavioral characteristics,such as file-system access patterns,persistence mechanisms,and resource-management calls that do not appear in network metadata.Our results further demonstrate that both monitoring modality and preprocessing strategy directly influence model performance.More importantly,combining flow-based and host-based telemetry in a complementary hybrid approach resolves classification ambiguities that arise when relying on a single data source.These findings underscore the potential of eBPF-based hybrid analysis for achieving accurate,low-overhead,and behavior-aware runtime security in containerized environments,and they establish a practical foundation for developing adaptive and scalable detection mechanisms in modern cloud systems.
基金supported by the Research year project of the KongjuNational University in 2025 and the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2024-00444170,Research and International Collaboration on Trust Model-Based Intelligent Incident Response Technologies in 6G Open Network Environment).
文摘With the growing complexity and decentralization of network systems,the attack surface has expanded,which has led to greater concerns over network threats.In this context,artificial intelligence(AI)-based network intrusion detection systems(NIDS)have been extensively studied,and recent efforts have shifted toward integrating distributed learning to enable intelligent and scalable detection mechanisms.However,most existing works focus on individual distributed learning frameworks,and there is a lack of systematic evaluations that compare different algorithms under consistent conditions.In this paper,we present a comprehensive evaluation of representative distributed learning frameworks—Federated Learning(FL),Split Learning(SL),hybrid collaborative learning(SFL),and fully distributed learning—in the context of AI-driven NIDS.Using recent benchmark intrusion detection datasets,a unified model backbone,and controlled distributed scenarios,we assess these frameworks across multiple criteria,including detection performance,communication cost,computational efficiency,and convergence behavior.Our findings highlight distinct trade-offs among the distributed learning frameworks,demonstrating that the optimal choice depends strongly on systemconstraints such as bandwidth availability,node resources,and data distribution.This work provides the first holistic analysis of distributed learning approaches for AI-driven NIDS and offers practical guidelines for designing secure and efficient intrusion detection systems in decentralized environments.
基金supported by the IITP(Institute of Information & Communications Technology Planning & Evaluation)-ITRC(Information Technology Research Center) grant funded by the Korea government(Ministry of Science and ICT) (IITP-2025-RS-2024-00437191, and RS-2025-02303505)partly supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Ministry of Education. (No. 2022R1A6C101A774)the Deanship of Research and Graduate Studies at King Khalid University, Saudi Arabia, through Large Research Project under grant number RGP-2/527/46
文摘The growing global energy demand and worsening climate change highlight the urgent need for clean,efficient and sustainable energy solutions.Among emerging technologies,atomically thin two-dimensional(2D)materials offer unique advantages in photovoltaics due to their tunable optoelectronic properties,high surface area and efficient charge transport capabilities.This review explores recent progress in photovoltaics incorporating 2D materials,focusing on their application as hole and electron transport layers to optimize bandgap alignment,enhance carrier mobility and improve chemical stability.A comprehensive analysis is presented on perovskite solar cells utilizing 2D materials,with a particular focus on strategies to enhance crystallization,passivate defects and improve overall cell efficiency.Additionally,the application of 2D materials in organic solar cells is examined,particularly for reducing recombination losses and enhancing charge extraction through work function modification.Their impact on dye-sensitized solar cells,including catalytic activity and counter electrode performance,is also explored.Finally,the review outlines key challenges,material limitations and performance metrics,offering insight into the future development of nextgeneration photovoltaic devices encouraged by 2D materials.
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 the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education,No.RS-2023-00237287。
文摘Non-suicidal self-injury(NSSI)is a prevalent and concerning issue in adolescent mental health,often intertwined with depressive symptoms.Despite extensive research on NSSI,a comprehensive understanding of its multifaceted nature and the intricate interplay of risk and resilience factors remains crucial.This Letter to the Editor examines a novel study by Yang et al,which utilized latent profile analysis and network analysis to delineate distinct NSSI subtypes within a Chinese adolescent population and investigate the underlying dynamics of associated factors.The study identifies three distinct NSSI subtypes:NSSI with depression,NSSI without depression,and neither,underscoring bullying as a prominent risk factor.Concurrently,the findings emphasized the pivotal role of emotional regulation and family support as protective factors.The focus of this article is to contextualize these findings within the broader framework of adolescent mental health and to highlight their implications for developing targeted interventions.These insights not only advance our understanding of adolescent NSSI but also provide a foundation for the development of targeted interventions that address the identified risk and protective factors.By focusing on these critical areas,mental health professionals can implement more effective strategies to mitigate NSSI behaviors and cultivate resilience in this vulnerable population.
基金Supported by Korea Health Technology R&D Project through the National Research Foundation of Korea,funded by the Korean Government(Project Number:NRF-2019R1I1A2A01063598)Undergraduate Research Program of the College of Korean Medicine,Kyung Hee University,Republic of Korea,in 2023(Project Number:2023)。
文摘OBJECTIVE:To determine direct targeting of localized adiposity through Morus alba Linne bark injection based on pharmacology network analysis.METHODS:Male C57BL/6J mice were fed a high-fat diet(HFD)to induce obesity.After 6 weeks on HFD,the water extract of Morus alba L.bark(MAB,2 mg/mL)was locally injected into one inguinal fat pad,while saline was injected into the other side,3 times/week for 6 weeks(n=6/group).The water extract of MAB was freeze-dried and then diluted in saline before use.RESULTS:HFD-fed mice treated with local MAB topical injection showed reduced adipocyte weight and size in inguinal fat pads by dual-energy X-ray absorptiometry.No toxicity changes seen in liver,spleen,kidney tissue,or alanine aminotransferase/aspartate aminotransferase levels in serum by MAB injection.Protein levels of phosphorylated insulin receptor substrate-1 and glucose transporter type 4,and mRNA expression of adiponectin,were increased in inguinal adipose tissue injected with MAB locally.Locally MAB injection led to a decrease in glucose-6-phosphatase and phosphoenolpyruvate carboxykinase,linked to gluconeogenesis,while forkhead box protein O1,which regulates these factors,was increased.Moreover,there was an increase in adenosine 5‘-monophosphate-activated protein kinase,related to lipogenesis,as well as elevated levels of hormone-sensitive lipase and fatty acid synthase,both associated with lipolysis.These results support the'insulin signaling pathway'and'regulation of lipolysis in adipocytes'identified in the Kyoto Encyclopedia of Genes and Genomes pathway through network analysis.CONCLUSION:This study suggests that MAB topical injection exhibits localized fat reduction by inhibiting insulin resistance,gluconeogenesis and lipogenesis mediator,while activating lipolysis enzymes within targeted adipose site.
基金funding from the Korea Institute for Advancement of Technology(KIAT)through a grant provided by the Korean Government Ministry of Trade,Industry,and Energy(MOTIE)(RS-2024-00415520,Training Industrial Security Specialist for High-Tech Industry)Additional support was received from the Ministry of Science and ICT(MSIT)under the ICAN(ICT Challenge and Advanced Network of HRD)program(No.IITP-2022-RS-2022-00156310)overseen by the Institute of Information&Communication Technology Planning and Evaluation(IITP).
文摘The Internet of Things(IoT)is extensively applied across various industrial domains,such as smart homes,factories,and intelligent transportation,becoming integral to daily life.Establishing robust policies for managing and governing IoT devices is imperative.Secure authentication for IoT devices in resource-constrained environments remains challenging due to the limitations of conventional complex protocols.Prior methodologies enhanced mutual authentication through key exchange protocols or complex operations,which are impractical for lightweight devices.To address this,our study introduces the privacy-preserving software-defined range proof(SDRP)model,which achieves secure authentication with low complexity.SDRP minimizes the overhead of confidentiality and authentication processes by utilizing range proof to verify whether the attribute information of a user falls within a specific range.Since authentication is performed using a digital ID sequence generated from indirect personal data,it can avoid the disclosure of actual individual attributes.Experimental results demonstrate that SDRP significantly improves security efficiency,increasing it by an average of 93.02%compared to conventional methods.It mitigates the trade-off between security and efficiency by reducing leakage risk by an average of 98.7%.
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry[grant number RS-2024-00415520]supervised by the Korea Institute for Advancement of Technology(KIAT)Ministry of Science and ICT(MSIT)under the ICAN(ICT Challenge and Advanced Network of HRD)program[grant number IITP-2022-RS-2022-00156310]+1 种基金National Research Foundation of Korea(NRF)grant[RS-2025-00518150]the Information Security Core Technology Development program[grant number RS-2024-00437252]supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As cyberattacks become increasingly sophisticated and intelligent,demand for machine-learning-based anomaly detection systems is growing.However,conventional systems generally assume a trusted server environment,where traffic data is collected and analyzed in plaintext.This assumption introduces inherent privacy risks,as privacy-sensitive information may be exposed if the server is compromised or misused.To address this limitation,privacy-preserving anomaly detection approaches have been actively studied,enabling anomaly detection to be performed directly on encrypted traffic without revealing privacy-sensitive data.While these approaches offer strong confidentiality guarantees,they suffer from significant drawbacks,including substantial computational overhead,high latency,and degraded detection accuracy.To overcome these limitations,we propose a privacy-aware anomaly detection(PAAD)model that adaptively applies homomorphic encryption based on the privacy sensitivity of incoming traffic.Instead of encrypting all data indiscriminately,PAAD dynamically determines whether traffic should be processed in plaintext or ciphertext and performs homomorphic inference only for privacy-sensitive data.This selective encryption strategy effectively balances privacy protection and system efficiency.Extensive experiments conducted under diverse network environments demonstrate that the proposed PAAD model significantly outperforms conventional anomaly detection models.In particular,PAAD improves detection accuracy by up to 73%,reduces latency by up to 8.6 times,and achieves negligible information leakage,highlighting its practicality for real-world privacy-sensitive network monitoring scenarios.
基金supported by the research grant of Gyeongsang National University in 2023supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00516213)+3 种基金the Brain Pool Program of the National Research Foundation(NRF)of Korea funded by theKorea government(MSIT)(RS-2025-25439144)AKorea Basic Science Institute(National Research Facilities and Equipment Center)grant funded by the Ministry of Education(2022R1A6C10B724)supported by the Regional Innovation System&Education(RISE)programthrough the RISE Center,Gyeongsangnam-do Provincial Government,Republic of Korea(2025-RISE-16-001)Learning&Academic research institution for Master’s⋅PhD students,and Postdocs(LAMP)Program of the National Research Foundation of Korea(NRF)grant funded by the Ministry of Education(RS-2023-00301974 and RS-2023-00301914).
文摘Objective:Cisplatin is a widely used chemotherapeutic agent due to its ability to damage DNA in the treatment of cancer.However,its clinical application is often limited by adverse effects on normal tissues,especially the kidneys.Understanding the molecular mechanisms of cisplatin-induced nephrotoxicity is crucial for developing strategies to mitigate its side effects.In this study,we aimed to elucidate the molecular mechanisms underlying cisplatin-induced DNA damage and apoptosis in human renal epithelial cells,with a focus on key signaling pathways and mediators that drive nephrotoxicity.Methods:To explore these mechanisms,human proximal tubule epithelial cells(HK-2)were treated with cisplatin.The study assessed DNA damage response(DDR)and stress-related protein expression,cell cycle distribution,and apoptosis.Activation of mitogen-activated protein kinases(MAPKs),particularly Extracellular signal-regulated Kinase(ERK),was analyzed,along with the expression and functional role of activating transcription factor 3(ATF3)and tumor protein p53(p53).Results:Cisplatin treatment upregulated DDR and stress response proteins,induced S phase arrest,and increased the SubG1 population,indicating apoptotic cell death.ERK was identified as a critical mediator of cisplatin-induced DNA damage and stress responses.ATF3 expression was significantly elevated in an ERK-dependent manner and required p53 activation.Knockdown of ATF3 reduced cisplatin-induced DNA damage,highlighting its role in the cytotoxic response.Conclusions:Cisplatin induces nephrotoxicity through ERK-and p53-dependent upregulation of ATF3,which is associated with DNA damage and cell death,suggesting a modulatory role in the cellular stress response.These findings provide novel insights into the molecular basis of cisplatin-induced renal injury and suggest potential therapeutic targets to alleviate its adverse effects.
基金supported by a grant from the Korean Ministry of Education, Science, and Technology (MEST)Republic of Korea through the National Research Foundation (NRF) (No. 2017-R1C1B2011968)
文摘Antimony-doped tin oxide(ATO) nanoparticles with an average size of ～ 6 nm were prepared by co-precipitation and subsequent heat treatment. Graphitic carbon nitride(g-CN)/ATO hybrid nanocomposite was designed by the combination of thermally synthesized g-CN and ATO nanoparticles by ultrasonication. The materials were characterized using N2 adsorption/desorption(BET), X-ray diffraction(XRD), scanning electron microscopy(SEM), energy dispersive spectroscopy(EDS), transmission electron microscopy(TEM) and Fourier transform infrared spectroscopy(FTIR). A mixture of five volatile organic compounds(VOCs, chloroform, benzene, toluene, xylene and styrene) was used to compare the adsorption capacity of the samples. The adsorption capacity of ATO nanoparticles was improved by the addition of g-CN. Experimental data showed that, among the five VOCs,chloroform was the least adsorbed, regardless of the samples. The g-CN/ATO showed nearly three times greater adsorption capacity for the VOC mixture than pure ATO. The unchanged efficiency of VOC adsorption during cyclic use demonstrated the completely reversible adsorption and desorption behavior of the nanocomposite at room conditions. This economically and environmentally friendly material can be a practical solution for outdoor and indoor VOC removal.
文摘Antioxidant activities of W and E extracts obtained from dried boxthorn (Lycium chinensis) fruit were measured based on DPPH radical scavenging and reducing powers, and their relationships with total phenolics, flavonoid content, and antioxidant activity were investigated. A linear correlation among antioxidant activity, total phenolics, and flavonoid content was observed in concentration-dependent mode. Both extracts showed > 95% DPPH radical-scavenging activity and the higher reducing power of 3200 ppm at the same concentration. The antioxidant potential of both extracts were compared with those of commercial antioxidants such as BHA, BHT, TBHQ, ferulic acid, and α-tocopherol using H2O2 scavenging activity, inhibition of linoleic acid peroxidation, inhibition of hemolysis of rat erythrocyte induced by peroxyl radicals, and inhibition of Fe2+-induced lipid peroxidation using rat brain tissue. In the H2O2 scavenging activity, E extract showed a comparable significant antioxidant power, comparable to commercial antioxidants, and no signifi-cant difference (P > 0.05) was found between W and E extracts on inhibition of the linoleic acid peroxidation. Whereas W extract exhibited a significant power in the hemolysis of rat erythrocytes, none was observed in E extract. In the Fe-induced lipid peroxidation using rat brain tissue, no significant difference (P > 0.05) was found between both ex-tracts, showing a comparable activity with those of synthetic antioxidants. Both W and E extracts of dried boxthorn (Lycium chinensis) fruit may have a potential as natural antioxidants to replace synthetic antioxidants.
基金supplemented by a paper presented at the 6th International Symposium on Mobile Internet Security(MobiSec 2022).
文摘As time and space constraints decrease due to the development of wireless communication network technology,the scale and scope of cyber-attacks targeting the Internet of Things(IoT)are increasing.However,it is difficult to apply high-performance security modules to the IoT owing to the limited battery,memory capacity,and data transmission performance depend-ing on the size of the device.Conventional research has mainly reduced power consumption by lightening encryption algorithms.However,it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security perfor-mance.In this study,we propose wake-up security(WuS),a low-power security architecture that can utilize high-performance security algorithms in an IoT environment.By introducing a small logic that performs anomaly detection on the IoT platform and executes the security module only when necessary according to the anomaly detection result,WuS improves security and power efficiency while using a relatively high-complexity security module in a low-power environment compared to the conventional method of periodically exe-cuting a high-performance security module.In this study,a Python simulator based on the UNSW-NB15 dataset is used to evaluate the power consumption,latency,and security of the proposed method.The evaluation results reveal that the power consumption of the proposed WuS mechanism is approxi-mately 51.8%and 27.2%lower than those of conventional high-performance security and lightweight security modules,respectively.Additionally,the laten-cies are approximately 74.8%and 65.9%lower,respectively.Furthermore,the WuS mechanism achieved a high detection accuracy of approximately 96.5%or greater,proving that the detection efficiency performance improved by approximately 33.5%compared to the conventional model.The performance evaluation results for the proposed model varied depending on the applied anomaly-detection model.Therefore,they can be used in various ways by selecting suitable models based on the performance levels required in each industry.
文摘Comparative antioxidant activities of the water and ethanol extracts obtained from dried citrus fruit (Citrus unshiu) peel were determined using chemical and biochemical in vitro assays. Chemical assays were used for evaluation of 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging, hydrogen peroxide scavenging, and reducing power of both extracts and their total phenolic, flavonoid and tannin contents and antioxidant activities were investigated. Biochemical assays were performed to evaluate the inhibition activities of AAPH-induced rat RBC hemolysis and Fe2+-induced lipid peroxidation using rat brain tissue cells. Linear correlation between the antioxidant activities of both extracts were determined by chemical assays, and total phenolic, flavonoid and tannin contents was observed in concentration-dependent mode. Both extracts showed >95% DPPH radical scavenging and >85% hydrogen peroxide scavenging, and higher reducing capacity at the same level of 3200 ppm. In the inhibition activity of AAPH-induced hemolysis, water extracts showed a strong activity in concentration-dependent mode up to 1600 ppm with no statistical difference found between 1600 and 3200 ppm (P > 0.05). In the inhibition activity of Fe2+-induced lipid peroxidation, ethanol extracts showed the higher inhibition percentage of lipid peroxidation than those of water extracts at the same concentration with no significant difference (P > 0.05) found in the range of 800 to 3200 ppm. The extracts of dried Citrus unshiu peel may be considered as potential antioxidant ingredients of functional food depending on the conditions at which reactive oxygen species are implicated.
基金the National Research Foundation of Korea(NRF),the Ministry of education,Korea(Project No.NRF2020R1F1A1061754)。
文摘The wide use of manganese dioxide(MnO_(2))as an electrode in all-solid-state asymmetric supercapacitors(ASCs)remains challenging because of its low electrical conductivity.This complication can be circumvented by introducing trivalent gadolinium(Gd)ions into the MnO_(2).Herein,we describe the successful hydrothermal synthesis of crystalline Gd-doped MnO_(2) nanorods with Ni(OH)_(2) nanosheets as cathode,which we combined with Fe_(3)O_(4)/GO nanospheres as anode for all-solid-state ASCs.Electrochemical tests dem on strate that Gd dopi ng sign ifica ntly affected the electrochemical activities of the MnO_(2),which was further enhanced by introducing Ni(OH)_(2).The GdMnO_(2)/Ni(OH)_(2) electrode offers sufficient surface electrochemical activity and exhibits excellent specific capacity of 121.8 mA h g^(-1),at 1A g^(-1),appealing rate performance,and ultralong lifetime stability(99.3%retention after 10,000 discharge tests).Furthermore,the GdMnO_(2)/Ni(OH)_(2)//PVA/KOH//Fe_(3)O_(4)/GO solid-state ASC device offers an impressive specific energy density(60.25 W h kg^(-1))at a high power density(2332 W kg^(-1)).This investigation thus shows its large potential in developing novel approaches to energy storage devices.
基金the support of the Korea Research Foundation with the funding of the Ministry of Science and Information and Communication Technology(No.2018-0-88457,development of translucent solar cells and Internet of Things technology for Solar Signage).
文摘In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need to apply various technologies for automation and control.This fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous devices.One of the major protocols widely used today in industrial electronic devices is Modbus.However,data generated by Modbus devices cannot be understood by IoT applications using different protocols,so it should be applied in a couple with an IoT service layer platform.oneM2M,a global IoT standard,can play the role of interconnecting various protocols,as it provides flexible tools suitable for building an interworking framework for industrial services.Therefore,in this paper,we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M standards.In the proposed architecture,we introduce the way to model Modbus data as oneM2M resources,rules to map them to each other,procedures required to establish interoperable communication,and optimization methods for this architecture.We analyze our solution and provide an evaluation by implementing it based on a solar power management use case.The results demonstrate that our model is feasible and can be applied to real case scenarios.
基金supported by a Korea Institute for Advancement of Technology(KIAT)Grant funded by theKorean Government(MOTIE)(P0008703,The Competency Development Program for Industry Specialists)the MSIT under the ICAN(ICT Challenge and Advanced Network ofHRD)program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information Communication Technology Planning and Evaluation(IITP).
文摘With the introduction of 5G technology,the application of Internet of Things(IoT)devices is expanding to various industrial fields.However,introducing a robust,lightweight,low-cost,and low-power security solution to the IoT environment is challenging.Therefore,this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT environment.The first method,compressed sensing and learning(CSL),compresses an event log in a bitmap format to quickly detect attacks.Then,the attack log is detected using a machine-learning classification model.The second method,precise re-learning after CSL(Ra-CSL),comprises a two-step training.It uses CSL as the 1st step analyzer,and the 2nd step analyzer is applied using the original dataset for a log that is detected as an attack in the 1st step analyzer.In the experiment,the bitmap rule was set based on the boundary value,which was 99.6%true positive on average for the attack and benign data found by analyzing the training data.Experimental results showed that the CSL was effective in reducing the training and detection time,and Ra-CSL was effective in increasing the detection rate.According to the experimental results,the data compression technique reduced the memory size by up to 20%and the training and detection times by 67%when compared with the conventional technique.In addition,the proposed technique improves the detection accuracy;the Naive Bayes model with the highest performance showed a detection rate of approximately 99%.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C2005777)was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2020R1A6A1A03038540)。
文摘This paper proposes the multiple-input multiple-output(MIMO)detection scheme by using the deep neural network(DNN)based ensemble machine learning for higher error performance in wireless communication systems.For the MIMO detection based on the ensemble machine learning,all learning models for the DNN are generated in offline and the detection is performed in online by using already learned models.In the offline learning,the received signals and channel coefficients are set to input data,and the labels which correspond to transmit symbols are set to output data.In the online learning,the perfectly learned models are used for signal detection where the models have fixed bias and weights.For performance improvement,the proposed scheme uses the majority vote and the maximum probability as the methods of the model combinations for obtaining diversity gains at the MIMO receiver.The simulation results show that the proposed scheme has improved symbol error rate(SER)performance without additional receive antennas.
文摘We investigated whether inhibiting phosphorylated p70S6K （p-p70S6K） suppresses the proliferation and growth of noninvasive low-grade urothelial carcinoma （LG-URCa） in vitro and whether p-p70S6K can serve as a predictive biomarker for the recurrence of noninvasive LG-URCa of the bladder in patients. We constructed a tissue microarray （TMA） for 95 LG-URCa and 35 benign urothelium samples and performed immunohistochemical staining for p-p70S6K and p-4E-BP1. A Cox regression model was used to investigate the predictive factors for recurrence of LG-URCa. We investigated the dose-dependent antiproliferative effect of rapamycin, its antiproliferative effect and the growth-inhibition effect of p70S6K siRNA transfection in RT4 and 253J cell lines. The pT1 staged group （P 〈 0.05; hazard ratio （HR）, 2.415） and the high p-p70S6K staining group （P 〈 0.05; HR, 2.249） were independent factors for predicting recurrence. Rapamycin inhibited RT4 and 253J cell proliferation in a dose-dependent manner （r = -0.850, P 〈 0.001 in RT4 cells; r = -0.835, P 〈 0.001 in 253J cells）. RT4 and 253J cell proliferation and growth were inhibited by the transfection of p70S6K siRNA and rapamycin, respectively （P 〈 0.05）. Transfection of p70S6K siRNA resulted in inhibitory effects on cell proliferation and growth that were similar to those of rapamycin. Our results suggest that inhibiting p70S6K phosphorylation is important to prevent recurrence and that p70S6K phosphorylation can be used as a molecular biomarker to predict recurrence of certain LG-URCa of the bladder.
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.
