Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Spinal cord injuries have overwhelming physical and occupational implications for patients.Moreover,the extensive and long-term medical care required for spinal cord injury significantly increases healthcare costs and...Spinal cord injuries have overwhelming physical and occupational implications for patients.Moreover,the extensive and long-term medical care required for spinal cord injury significantly increases healthcare costs and resources,adding a substantial burden to the healthcare system and patients'families.In this context,chondroitinase ABC,a bacterial enzyme isolated from Proteus vulgaris that is modified to facilitate expression and secretion in mammals,has emerged as a promising therapeutic agent.It works by degrading chondroitin sulfate proteoglycans,cleaving the glycosaminoglycanchains of chondroitin sulfate proteoglycans into soluble disaccharides or tetrasaccharides.Chondroitin sulfate proteoglycans are potent axon growth inhibitors and principal constituents of the extracellular matrix surrounding glial and neuronal cells attached to glycosaminoglycan chains.Chondroitinase ABC has been shown to play an effective role in promoting recovery from acute and chronic spinal cord injury by improving axonal regeneration and sprouting,enhancing the plasticity of perineuronal nets,inhibiting neuronal apoptosis,and modulating immune responses in various animal models.In this review,we introduce the classification and pathological mechanisms of spinal cord injury and discuss the pathophysiological role of chondroitin sulfate proteoglycans in spinal cord injury.We also highlight research advancements in spinal cord injury treatment strategies,with a focus on chondroitinase ABC,and illustrate how improvements in chondroitinase ABC stability,enzymatic activity,and delivery methods have enhanced injured spinal cord repair.Furthermore,we emphasize that combination treatment with chondroitinase ABC further enhances therapeutic efficacy.This review aimed to provide a comprehensive understanding of the current trends and future directions of chondroitinase ABC-based spinal cord injury therapies,with an emphasis on how modern technologies are accelerating the optimization of chondroitinase ABC development.展开更多
Workers who conduct regular facility inspections in radioactive environments will inevitably be affected by radiation.Therefore,it is important to optimize the inspection path to ensure that workers are exposed to the...Workers who conduct regular facility inspections in radioactive environments will inevitably be affected by radiation.Therefore,it is important to optimize the inspection path to ensure that workers are exposed to the least amount of radiation.This study proposes a discrete Rao-combined artificial bee colony(ABC)algorithm for planning inspection paths with minimum exposure doses in radioactive environments with obstacles.In this algorithm,retaining the framework of the traditional ABC algorithm,we applied the directional solution update rules of Rao algorithms at the employed bee stage and onlooker bee stage to increase the exploitation ability of the algorithm and implement discretion using the swap operator and swap sequence.To increase the randomness of solution generation,the chaos algorithm was used at the initialization stage.The K-opt operation technique was introduced at the scout bee stage to increase the exploration ability of the algorithm.For path planning in an environment with complex structural obstacles,an obstacle detour technique using a recursive algorithm was applied.To evaluate the performance of the proposed algorithm,we performed experimental simulations in three hypothetical environments and compared the results with those of improved particle swarm optimization,chaos particle swarm optimization,improved ant colony optimization,and discrete Rao’s algorithms.The experimental results show the high performance of the proposed discrete Rao-combined ABC algorithm and its obstacle detour capability.展开更多
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金supported by the National Natural Science Foundation of China,No.82002645China Postdoctoral Science Foundation,No.2022M722321Jiangsu Funding Program for Excellent Postdoctoral Talent,No.2022ZB552(all to YH)。
文摘Spinal cord injuries have overwhelming physical and occupational implications for patients.Moreover,the extensive and long-term medical care required for spinal cord injury significantly increases healthcare costs and resources,adding a substantial burden to the healthcare system and patients'families.In this context,chondroitinase ABC,a bacterial enzyme isolated from Proteus vulgaris that is modified to facilitate expression and secretion in mammals,has emerged as a promising therapeutic agent.It works by degrading chondroitin sulfate proteoglycans,cleaving the glycosaminoglycanchains of chondroitin sulfate proteoglycans into soluble disaccharides or tetrasaccharides.Chondroitin sulfate proteoglycans are potent axon growth inhibitors and principal constituents of the extracellular matrix surrounding glial and neuronal cells attached to glycosaminoglycan chains.Chondroitinase ABC has been shown to play an effective role in promoting recovery from acute and chronic spinal cord injury by improving axonal regeneration and sprouting,enhancing the plasticity of perineuronal nets,inhibiting neuronal apoptosis,and modulating immune responses in various animal models.In this review,we introduce the classification and pathological mechanisms of spinal cord injury and discuss the pathophysiological role of chondroitin sulfate proteoglycans in spinal cord injury.We also highlight research advancements in spinal cord injury treatment strategies,with a focus on chondroitinase ABC,and illustrate how improvements in chondroitinase ABC stability,enzymatic activity,and delivery methods have enhanced injured spinal cord repair.Furthermore,we emphasize that combination treatment with chondroitinase ABC further enhances therapeutic efficacy.This review aimed to provide a comprehensive understanding of the current trends and future directions of chondroitinase ABC-based spinal cord injury therapies,with an emphasis on how modern technologies are accelerating the optimization of chondroitinase ABC development.
文摘Workers who conduct regular facility inspections in radioactive environments will inevitably be affected by radiation.Therefore,it is important to optimize the inspection path to ensure that workers are exposed to the least amount of radiation.This study proposes a discrete Rao-combined artificial bee colony(ABC)algorithm for planning inspection paths with minimum exposure doses in radioactive environments with obstacles.In this algorithm,retaining the framework of the traditional ABC algorithm,we applied the directional solution update rules of Rao algorithms at the employed bee stage and onlooker bee stage to increase the exploitation ability of the algorithm and implement discretion using the swap operator and swap sequence.To increase the randomness of solution generation,the chaos algorithm was used at the initialization stage.The K-opt operation technique was introduced at the scout bee stage to increase the exploration ability of the algorithm.For path planning in an environment with complex structural obstacles,an obstacle detour technique using a recursive algorithm was applied.To evaluate the performance of the proposed algorithm,we performed experimental simulations in three hypothetical environments and compared the results with those of improved particle swarm optimization,chaos particle swarm optimization,improved ant colony optimization,and discrete Rao’s algorithms.The experimental results show the high performance of the proposed discrete Rao-combined ABC algorithm and its obstacle detour capability.
