The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
Background： Although the onset of anemia during infectious disease is commonly correlated with production of inflammatory cytokines, the mechanisms by which cytokines induce anemia are poorly defined. This study focu...Background： Although the onset of anemia during infectious disease is commonly correlated with production of inflammatory cytokines, the mechanisms by which cytokines induce anemia are poorly defined. This study focused on the mechanism research. Methods： Different types of mice were infected perorally with Toxoplasma gondii strain ME49. At the indicated times, samples fi＇om each mouse were harvested, processed, and analyzed individually. Blood samples were analyzed using a Coulter Counter and red blood cell （RBC） survival was measured by biotinylation. Levels of tumor necrosis factor-α （TNF-α）, inducible nitric oxide synthase （iNOS）, and inducible protein 10 （IP-10） mRNA in liver tissue were measured by real-time polymerase chain reaction. Results： T. gondii-infected mice exhibited anemia due to a decrease in both erythropoiesis and survival time of RBC in the circulation （P 〈 0.02）. In addition, infection-stimulated anemia was associated with fecal occult, supporting previous literature that hemorrhage is a consequence of T. gondii infection in mice. Infection-induced anemia was abolished in interferon gamma （IFNy） and I FNy receptor deficient mice （P 〈 0.05） but was still evident in mice lacking TNF-α, iNOS, phagocyte NADPH oxidase or IP-10 （P 〈 0.02）. Neither signal transducer and activator of transcription 1 （STAT1） deficient mice nor 129S6 controls exhibited decreased erythropoiesis, but rather suffered from an anemia resulting solely from increased loss of circulating RBC. Conclusions： Infection-stimulated decrease in erythropoiesis and losses of RBC have distinct mechanistic bases. These results show that during T. gondii infection, IFNγ is responsible foran anemia that results from both a decrease in erythropoiesis and a STAT1 independent loss of circulating RBC.展开更多
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
文摘Background： Although the onset of anemia during infectious disease is commonly correlated with production of inflammatory cytokines, the mechanisms by which cytokines induce anemia are poorly defined. This study focused on the mechanism research. Methods： Different types of mice were infected perorally with Toxoplasma gondii strain ME49. At the indicated times, samples fi＇om each mouse were harvested, processed, and analyzed individually. Blood samples were analyzed using a Coulter Counter and red blood cell （RBC） survival was measured by biotinylation. Levels of tumor necrosis factor-α （TNF-α）, inducible nitric oxide synthase （iNOS）, and inducible protein 10 （IP-10） mRNA in liver tissue were measured by real-time polymerase chain reaction. Results： T. gondii-infected mice exhibited anemia due to a decrease in both erythropoiesis and survival time of RBC in the circulation （P 〈 0.02）. In addition, infection-stimulated anemia was associated with fecal occult, supporting previous literature that hemorrhage is a consequence of T. gondii infection in mice. Infection-induced anemia was abolished in interferon gamma （IFNy） and I FNy receptor deficient mice （P 〈 0.05） but was still evident in mice lacking TNF-α, iNOS, phagocyte NADPH oxidase or IP-10 （P 〈 0.02）. Neither signal transducer and activator of transcription 1 （STAT1） deficient mice nor 129S6 controls exhibited decreased erythropoiesis, but rather suffered from an anemia resulting solely from increased loss of circulating RBC. Conclusions： Infection-stimulated decrease in erythropoiesis and losses of RBC have distinct mechanistic bases. These results show that during T. gondii infection, IFNγ is responsible foran anemia that results from both a decrease in erythropoiesis and a STAT1 independent loss of circulating RBC.
