Efficient planning of activities is essential for modern industrial assembly lines to uphold manufacturing standards,prevent project constraint violations,and achieve cost-effective operations.While exact solutions to...Efficient planning of activities is essential for modern industrial assembly lines to uphold manufacturing standards,prevent project constraint violations,and achieve cost-effective operations.While exact solutions to such challenges can be obtained through Integer Programming(IP),the dependence of the search space on input parameters often makes IP computationally infeasible for large-scale scenarios.Heuristic methods,such as Genetic Algorithms,can also be applied,but they frequently produce suboptimal solutions in extensive cases.This paper introduces a novel mathematical model of a generic industrial assembly line formulated as a Markov Decision Process(MDP),without imposing assumptions on the type of assembly line a notable distinction from most existing models.The proposed model is employed to create a virtual environment for training Deep Reinforcement Learning(DRL)agents to optimize task and resource scheduling.To enhance the efficiency of agent training,the paper proposes two innovative tools.The first is an action-masking technique,which ensures the agent selects only feasible actions,thereby reducing training time.The second is a multi-agent approach,where each workstation is managed by an individual agent,as a result,the state and action spaces were reduced.A centralized training framework with decentralized execution is adopted,offering a scalable learning architecture for optimizing industrial assembly lines.This framework allows the agents to learn offline and subsequently provide real-time solutions during operations by leveraging a neural network that maps the current factory state to the optimal action.The effectiveness of the proposed scheme is validated through numerical simulations,demonstrating significantly faster convergence to the optimal solution compared to a comparable model-based approach.展开更多
Existing orthogonal space-time block coding(OSTBC)schemes for backscatter communication systems cannot achieve a full transmission code rate when the tag is equipped with more than two antennas.In this paper,we propos...Existing orthogonal space-time block coding(OSTBC)schemes for backscatter communication systems cannot achieve a full transmission code rate when the tag is equipped with more than two antennas.In this paper,we propose a quasi-orthogonal spacetime block code(QOSTBC)that can achieve a full transmission code rate for backscatter communication systems with a four-antenna tag and then extend the scheme to support tags with 2i antennas.Specifically,we first present the system model for the backscatter system.Next,we propose the QOSTBC scheme to encode the tag signals.Then,we provide the corresponding maximum likelihood detection algorithms to recover the tag signals.Finally,simulation results are provided to demonstrate that our proposed QOSTBC scheme and the detection algorithm can achieve a better transmission code rate or symbol error rate performance for backscatter communication systems compared with benchmark schemes.展开更多
With the rapid development of network technologies,a large number of deployed edge devices and information systems generate massive amounts of data which provide good support for the advancement of data-driven intelli...With the rapid development of network technologies,a large number of deployed edge devices and information systems generate massive amounts of data which provide good support for the advancement of data-driven intelligent models.However,these data often contain sensitive information of users.Federated learning(FL),as a privacy preservation machine learning setting,allows users to obtain a well-trained model without sending the privacy-sensitive local data to the central server.Despite the promising prospect of FL,several significant research challenges need to be addressed before widespread deployment,including network resource allocation,model security,model convergence,etc.In this paper,we first provide a brief survey on some of these works that have been done on FL and discuss the motivations of the Communication Networks(CNs)and FL to mutually enable each other.We analyze the support of network technologies for FL,which requires frequent communication and emphasizes security,as well as the studies on the intelligence of many network scenarios and the improvement of network performance and security by the methods based on FL.At last,some challenges and broader perspectives are explored.展开更多
This research investigated the removal capacity of polymeric sub-micron ion-exchange resins(SMR) for removal of lead, copper, zinc, and nickel from natural waters in competition with natural organic matter(NOM). Polym...This research investigated the removal capacity of polymeric sub-micron ion-exchange resins(SMR) for removal of lead, copper, zinc, and nickel from natural waters in competition with natural organic matter(NOM). Polymeric SMR particles were created and tested to ensure that they were adequately dispersed in the solution. They removed little NOM(10%or less) from river water and wastewater, indicating that competition from NOM was not a major concern. SMR were able to remove 82% ± 0.2% of lead, 46% ± 0.6% of copper, 55% ±20% of zinc, and 17% ± 2% of nickel from river water spiked with 500 μg/L of each. Similarly,in wastewater, they were able to remove 86% ± 0.1% of lead, 38% ± 0.8% of copper, 28% ± 1%of zinc, and 11% ± 1% of nickel.展开更多
Stroke is the leading cause of long-term disability worldwide.There are two main types of stroke,hemorrhagic and ischemic.A hemorrhagic stroke is when there a bleed in the brain,whereas an ischemic stroke is the resul...Stroke is the leading cause of long-term disability worldwide.There are two main types of stroke,hemorrhagic and ischemic.A hemorrhagic stroke is when there a bleed in the brain,whereas an ischemic stroke is the result of blockage of blood flow to the brain,which leads to degeneration,neurotoxicity,inflammation,and apoptosis.This damage not only affects the ischemic core,but also neuronal,astrocyte,and synaptic survival in the peri-infarct region and connected areas(Kerr et al.,2011;Jadavji et al.,2018).The prevalence of ischemic stroke is predicted to increase as the global population ages(Mukherjee and Patil,2011).Between 1970 and 2008 there has been a 100%increase in stroke incidence in low income countries.For example,the estimated losses in gross domestic product,as the result of vascular diseases,including stroke,have ranged from$20 million in Ethiopia to$1 billion in China and India(Mukherjee and Patil,2011).展开更多
Currently,ischemic stroke is the most prevalent form of stroke compared to hemorrhagic and there is a high incidence in older adults.Nutrition is a modifiable risk factor for stroke.B-vitamins are part of a metabolic ...Currently,ischemic stroke is the most prevalent form of stroke compared to hemorrhagic and there is a high incidence in older adults.Nutrition is a modifiable risk factor for stroke.B-vitamins are part of a metabolic network that integrates nutritional signals with biosynthesis,redox homeostasis,and epigenetics.These vitamins play an essential role in the regulation of cell proliferation,stress resistance,and embryo development.A deficiency in vitamin B12 is common in older adults and has been reported to be implicated in ischemic stroke.The aim of this review was to investigate whether vitamin B12 deficiencies impact the risk and outcome of ischemic stroke.Clinical data from our literature review strongly suggest that a deficiency in vitamin B12 is a risk factor for ischemic stroke and possible outcome.Our survey of the literature has identified that there is a gap in the understanding of the mechanisms through which a vitamin B12 deficiency leads to an increased risk of stroke and outcome.A vitamin B12 deficiency can increase homocysteine levels,which are a well-established risk factor for ischemic stroke.Another potential mechanism through which vitamin B12 deficient may impact neurological function and increase risk of stroke,is changes in myelination,however this link requires further investigation.Further studies are required in model systems to understand how a vitamin B12 deficiency changes the brain.展开更多
This paper focuses on the problem of adaptive finitetime fault-tolerant control for a class of non-lower-triangular nonlinear systems.The faults encountered in the control system include the actuator faults and the ab...This paper focuses on the problem of adaptive finitetime fault-tolerant control for a class of non-lower-triangular nonlinear systems.The faults encountered in the control system include the actuator faults and the abrupt system fault.By applying backstepping design and neural networks approximation,an adaptive finite-time fault-tolerant control scheme is developed.It is shown that the proposed controller ensures that all signals in the closed-loop system are semi-globally practically finite-time stable and the track-ing error converges to a small neighborhood around the origin within finite time.The simulation is carried out to explain the validity of the developed strategy.展开更多
基金supported in part by the National Sciences and Engineering Research Council of Canada(NSERC)under the grants RGPIN-2022-04937。
文摘Efficient planning of activities is essential for modern industrial assembly lines to uphold manufacturing standards,prevent project constraint violations,and achieve cost-effective operations.While exact solutions to such challenges can be obtained through Integer Programming(IP),the dependence of the search space on input parameters often makes IP computationally infeasible for large-scale scenarios.Heuristic methods,such as Genetic Algorithms,can also be applied,but they frequently produce suboptimal solutions in extensive cases.This paper introduces a novel mathematical model of a generic industrial assembly line formulated as a Markov Decision Process(MDP),without imposing assumptions on the type of assembly line a notable distinction from most existing models.The proposed model is employed to create a virtual environment for training Deep Reinforcement Learning(DRL)agents to optimize task and resource scheduling.To enhance the efficiency of agent training,the paper proposes two innovative tools.The first is an action-masking technique,which ensures the agent selects only feasible actions,thereby reducing training time.The second is a multi-agent approach,where each workstation is managed by an individual agent,as a result,the state and action spaces were reduced.A centralized training framework with decentralized execution is adopted,offering a scalable learning architecture for optimizing industrial assembly lines.This framework allows the agents to learn offline and subsequently provide real-time solutions during operations by leveraging a neural network that maps the current factory state to the optimal action.The effectiveness of the proposed scheme is validated through numerical simulations,demonstrating significantly faster convergence to the optimal solution compared to a comparable model-based approach.
基金supported by Beijing Municipal Natural Science Foundation(L222002)the Natural Science Foundation of China(U22B2004).
文摘Existing orthogonal space-time block coding(OSTBC)schemes for backscatter communication systems cannot achieve a full transmission code rate when the tag is equipped with more than two antennas.In this paper,we propose a quasi-orthogonal spacetime block code(QOSTBC)that can achieve a full transmission code rate for backscatter communication systems with a four-antenna tag and then extend the scheme to support tags with 2i antennas.Specifically,we first present the system model for the backscatter system.Next,we propose the QOSTBC scheme to encode the tag signals.Then,we provide the corresponding maximum likelihood detection algorithms to recover the tag signals.Finally,simulation results are provided to demonstrate that our proposed QOSTBC scheme and the detection algorithm can achieve a better transmission code rate or symbol error rate performance for backscatter communication systems compared with benchmark schemes.
基金supported by National Key Research and Development Program of China(No.2023YFB2704200)Beijing Natural Science Foundation(No.4254064).
文摘With the rapid development of network technologies,a large number of deployed edge devices and information systems generate massive amounts of data which provide good support for the advancement of data-driven intelligent models.However,these data often contain sensitive information of users.Federated learning(FL),as a privacy preservation machine learning setting,allows users to obtain a well-trained model without sending the privacy-sensitive local data to the central server.Despite the promising prospect of FL,several significant research challenges need to be addressed before widespread deployment,including network resource allocation,model security,model convergence,etc.In this paper,we first provide a brief survey on some of these works that have been done on FL and discuss the motivations of the Communication Networks(CNs)and FL to mutually enable each other.We analyze the support of network technologies for FL,which requires frequent communication and emphasizes security,as well as the studies on the intelligence of many network scenarios and the improvement of network performance and security by the methods based on FL.At last,some challenges and broader perspectives are explored.
基金funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN 06246)Ontario Research Fund (ORF) (203364)
文摘This research investigated the removal capacity of polymeric sub-micron ion-exchange resins(SMR) for removal of lead, copper, zinc, and nickel from natural waters in competition with natural organic matter(NOM). Polymeric SMR particles were created and tested to ensure that they were adequately dispersed in the solution. They removed little NOM(10%or less) from river water and wastewater, indicating that competition from NOM was not a major concern. SMR were able to remove 82% ± 0.2% of lead, 46% ± 0.6% of copper, 55% ±20% of zinc, and 17% ± 2% of nickel from river water spiked with 500 μg/L of each. Similarly,in wastewater, they were able to remove 86% ± 0.1% of lead, 38% ± 0.8% of copper, 28% ± 1%of zinc, and 11% ± 1% of nickel.
基金funded by the Natural Sciences and Engineering Research Council(NSERC)Canada(to NMJ)
文摘Stroke is the leading cause of long-term disability worldwide.There are two main types of stroke,hemorrhagic and ischemic.A hemorrhagic stroke is when there a bleed in the brain,whereas an ischemic stroke is the result of blockage of blood flow to the brain,which leads to degeneration,neurotoxicity,inflammation,and apoptosis.This damage not only affects the ischemic core,but also neuronal,astrocyte,and synaptic survival in the peri-infarct region and connected areas(Kerr et al.,2011;Jadavji et al.,2018).The prevalence of ischemic stroke is predicted to increase as the global population ages(Mukherjee and Patil,2011).Between 1970 and 2008 there has been a 100%increase in stroke incidence in low income countries.For example,the estimated losses in gross domestic product,as the result of vascular diseases,including stroke,have ranged from$20 million in Ethiopia to$1 billion in China and India(Mukherjee and Patil,2011).
基金supported by Midwestern University Startup Funds(to NMJ)American Heart Association,No.20AIREA35050015(to NMJ)。
文摘Currently,ischemic stroke is the most prevalent form of stroke compared to hemorrhagic and there is a high incidence in older adults.Nutrition is a modifiable risk factor for stroke.B-vitamins are part of a metabolic network that integrates nutritional signals with biosynthesis,redox homeostasis,and epigenetics.These vitamins play an essential role in the regulation of cell proliferation,stress resistance,and embryo development.A deficiency in vitamin B12 is common in older adults and has been reported to be implicated in ischemic stroke.The aim of this review was to investigate whether vitamin B12 deficiencies impact the risk and outcome of ischemic stroke.Clinical data from our literature review strongly suggest that a deficiency in vitamin B12 is a risk factor for ischemic stroke and possible outcome.Our survey of the literature has identified that there is a gap in the understanding of the mechanisms through which a vitamin B12 deficiency leads to an increased risk of stroke and outcome.A vitamin B12 deficiency can increase homocysteine levels,which are a well-established risk factor for ischemic stroke.Another potential mechanism through which vitamin B12 deficient may impact neurological function and increase risk of stroke,is changes in myelination,however this link requires further investigation.Further studies are required in model systems to understand how a vitamin B12 deficiency changes the brain.
基金supported in part by the National Natural Science Foundation of China(61773072,61773051,61761166011,61773073)in part by the Innovative Talents Project of Liaoning Province of China(LR2016040)in part by the Natural Science Foundation of Liaoning Province of China(20180550691,20180550590)
文摘This paper focuses on the problem of adaptive finitetime fault-tolerant control for a class of non-lower-triangular nonlinear systems.The faults encountered in the control system include the actuator faults and the abrupt system fault.By applying backstepping design and neural networks approximation,an adaptive finite-time fault-tolerant control scheme is developed.It is shown that the proposed controller ensures that all signals in the closed-loop system are semi-globally practically finite-time stable and the track-ing error converges to a small neighborhood around the origin within finite time.The simulation is carried out to explain the validity of the developed strategy.
