The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the exis...The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.展开更多
Dear Editor,This letter focuses on the distributed cooperative regulation problem for a class of networked re-entrant manufacturing systems(RMSs).The networked system is structured with a three-tier architecture:the p...Dear Editor,This letter focuses on the distributed cooperative regulation problem for a class of networked re-entrant manufacturing systems(RMSs).The networked system is structured with a three-tier architecture:the production line,the manufacturing layer and the workshop layer.The dynamics of re-entrant production lines are governed by hyperbolic partial differential equations(PDEs)based on the law of mass conservation.展开更多
OBJECTIVE: To analyze part of the mechanism of electroacupuncture on Sishencong(EX-HN1) for strokerelated sleep disorders(SSD) and post-stroke cognitive impairment(PSCI). METHODS: Using a randomized controlled trial(R...OBJECTIVE: To analyze part of the mechanism of electroacupuncture on Sishencong(EX-HN1) for strokerelated sleep disorders(SSD) and post-stroke cognitive impairment(PSCI). METHODS: Using a randomized controlled trial(RCT) design, 72 patients were assigned to the electroacupuncture(EA) group or the sham acupuncture(SA) group. A healthy control(HC) group was also included. Both groups were given routine rehabilitation treatment. Then, patients in the EA group were given additional electroacupuncture at Sishencong(EX_HN1). Meanwhile, patients in the SA group were given a flat-head needle sham/placebo treatment placed at the bilateral Jianyu (LI15) and Binao(LI14) line midpoints and the Jianyu(LI15) and Jianzhen(SI9) line midpoints. Before and after treatment, scales were collected and analyzed. In the second phase of the study, some subjects from the EA group were selected for functional magnetic resonance imaging(f MRI) data acquisition and comparative analysis with the HC group using a non-RCT design. RESULTS: The EA group performed better than the SA group on the Pittsburgh sleep quality index(PSQI), Montreal cognitive assessment basic(Mo CA_B), selfrating anxiety scale(SAS), and self-rating depression scale(SDS). Analysis of the f MRI showed that lowfrequency(2 Hz) electroacupuncture stimulation at Sishencong(EX_HN1) can restrain frontal sup medial right(SFGmed.R), precuneus right(PCUN.R), and posterior cingulate cortex right(PCC.R) and enhance angular left(ANG.L), parietal inf left(IPL.L) and occipital mid left(MOG.L). The functional connectivity(FC) of SFGmed.R was positively correlated with PSQI. Electroacupuncture stimulation at Sishencong(EX_HN1) can reduce the side efficiency of the whole brain connection with the Thalamus.L, Hippocampus.L, and Occipital.Mid.L. CONCLUSIONS: Low frequency(2 Hz) electroacupuncture stimulation at Sishencong(EX_HN1) can simultaneously improve sleep quality, negative emotions, and cognitive functions, the first two of which may be related to SFGmed.R restraint. Electroacupuncture can make some brain areas approach the physiological bias state, which is characterized by dominant hemispheric enhancement and non-dominant hemispheric weakening. The reduced whole brain connection side efficiency with some key nodes of the brain net may relate to sleep quality improvements in SSD patients.展开更多
Regulation plays a pivotal role in mitigating the spread of rumors, serving as a vital tool for maintaining social stability and facilitating its evolution. A central challenge lies in establishing an effective regula...Regulation plays a pivotal role in mitigating the spread of rumors, serving as a vital tool for maintaining social stability and facilitating its evolution. A central challenge lies in establishing an effective regulatory framework despite limited resources available for combating rumor propagation. To address this challenge, this paper proposes a dynamic and adaptive regulatory system. First, based on observed regulatory patterns in real-world social networks, the rumor propagation process is divided into two distinct phases: regulation and intervention. Regulatory intensity is introduced as an indicator of user state transitions. Unlike traditional, non-adaptive regulatory models that allocate costs uniformly,the adaptive model facilitates flexible cost distribution through a manageable individual regulatory intensity. Moreover,by introducing adaptive strength, the two cost allocation models are integrated within a unified framework, leading to the development of a dynamic model for rumor suppression. Finally, simulation experiments on Barabási–Albert(BA)networks demonstrate that the adaptive regulatory mechanism significantly reduces both the scope and duration of rumor propagation. Furthermore, when traditional non-adaptive regulatory models show limited effectiveness, the adaptive model effectively curbs rumor propagation by optimizing cost allocation between regulatory and intervention processes, and by adjusting per-unit cost benefit differentials.展开更多
We are deeply interested in the recent findings onβ-arrestin 2.Liu et al demonstrated thatβ-arrestin 2 knockout provides significant protection in diabetic nephropathy,underscoring its potential as a promising thera...We are deeply interested in the recent findings onβ-arrestin 2.Liu et al demonstrated thatβ-arrestin 2 knockout provides significant protection in diabetic nephropathy,underscoring its potential as a promising therapeutic target for diabetic nephropathy treatment.Furthermore,the role ofβ-arrestin 2 in metabolic regulation is equally critical,particularly in insulin signaling,hepatic glucose production,and adipose tissue function.Althoughβ-arrestin 2 plays a distinct role in metabolism and kidney protection,its tissue-specific regulation opens up valuable avenues for developing targeted therapeutic strategies centered onβ-arrestin 2.展开更多
The aviation oxygen mask,which has a small volume of less than 1L and strong air tightness,imposes extremely high requirements on control performance of the oxygen regulator.Based on analyses of the operation principl...The aviation oxygen mask,which has a small volume of less than 1L and strong air tightness,imposes extremely high requirements on control performance of the oxygen regulator.Based on analyses of the operation principle of oxygen supply system,the dynamic model is established through the combination of mechanism analysis and experimental data.Considering that the traditional fixed-parameter controllers are difficult to meet the control requirements with changes in pulmonary ventilation,this paper presents an online feedback controller based on neural network compensation(NNC),with connection weights that can be updated without pre-training.Then mathematical simulations at different respiratory parameters,such as respiratory rate,are performed to verify the superior lower inspiratory resistance of controller with NNC.In terms of hardware,an embedded AI control platform is to complete the experimental verification.Furthermore,the work may have downward compatibility to achieve stable oxygen supply in civil fields,such as medical ventilators,high-altitude expeditions.展开更多
In the energy regulation based varibable-speed electrohydraulic drive system, the supply energy and the demanded energy, which will affect the control performance greatly, are crucial. However, they are hard to be obt...In the energy regulation based varibable-speed electrohydraulic drive system, the supply energy and the demanded energy, which will affect the control performance greatly, are crucial. However, they are hard to be obtained via conventional methods for some reasons. This paper tries to a new route： the definitive numerical values of the supply energy and the demanded energy are not required, except for their relationship which is called energy state. A three-layer back propagation（BP） neural network was built up to act as an energy analysis unit to deduce the energy state. The neural network has three inputs： the reference displacement, the actual displacement of cylinder rod and the system flowrate supply. The output of the neural network is energy state. A Chebyshev type II filter was designed to calculate the cylinder speed for the estimation of system flowrate supply. The training and testing samples of neural network were collected by the system accurate simulation model. After off-line training, the neural network was tested by the testing data. And the testing result demonstrates that the designed neural network was successful. Then, the neural network acts as the energy analysis unit in real-time experiments of cylinder position control, where it works efficiently under square-wave and sine-wave reference displacement. The experimental results validate its feasibility and adaptability. Only a position sensor and some pressure sensors, which are cheap and have quick dynamic response, are necessary for the system control. And the neural network plays the role of identifying the energy state.展开更多
Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery...Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.展开更多
Objective:Epigenetic abnormalities have a critical role in breast cancer by regulating gene expression;however,the intricate interrelationships and key roles of approximately 400 epigenetic regulators in breast cancer...Objective:Epigenetic abnormalities have a critical role in breast cancer by regulating gene expression;however,the intricate interrelationships and key roles of approximately 400 epigenetic regulators in breast cancer remain elusive.It is important to decipher the comprehensive epigenetic regulatory network in breast cancer cells to identify master epigenetic regulators and potential therapeutic targets.Methods:We employed high-throughput sequencing-based high-throughput screening(HTS^(2))to effectively detect changes in the expression of 2,986 genes following the knockdown of 400 epigenetic regulators.Then,bioinformatics analysis tools were used for the resulting gene expression signatures to investigate the epigenetic regulations in breast cancer.Results:Utilizing these gene expression signatures,we classified the epigenetic regulators into five distinct clusters,each characterized by specific functions.We discovered functional similarities between BAZ2B and SETMAR,as well as CLOCK and CBX3.Moreover,we observed that CLOCK functions in a manner opposite to that of HDAC8 in downstream gene regulation.Notably,we constructed an epigenetic regulatory network based on the gene expression signatures,which revealed 8 distinct modules and identified 10 master epigenetic regulators in breast cancer.Conclusions:Our work deciphered the extensive regulation among hundreds of epigenetic regulators.The identification of 10 master epigenetic regulators offers promising therapeutic targets for breast cancer treatment.展开更多
When large-scale distributed renewable energy power generation systems are connected to the power grid,the risk of grid voltage fluctuations and exceeding the limit increases greatly.Fortunately,the on-load tap change...When large-scale distributed renewable energy power generation systems are connected to the power grid,the risk of grid voltage fluctuations and exceeding the limit increases greatly.Fortunately,the on-load tap changer(OLTC)can adjust the transformer winding tap to maintain the secondary side voltage within the normal range.However,the inevitable delay in switching transformer taps makes it difficult to respond quickly to voltage fluctuations.Moreover,switching the transformer taps frequently will decrease the service life of OLTC.In order to solve this critical issue,a cooperative voltage regulation strategy applied between the battery energy storage systems(BESSs)and OLTSs.is proposed By adjusting the charge and discharge power of BESSs,the OLTC can frequently switch the transformer taps to achieve rapid voltage regulation.The effectiveness of the proposed coordinated regulation strategy is verified in the IEEE 33 node distribution systems.The simulation results show that the proposed coordinated regulation strategy can stabilize the voltage of the distribution network within a normal range and reduce the frequency of tap switching,as such elongating the service life of the equipment.展开更多
First, the paper analyzes the advantages and disadvantages of all kinds of reactive power compensation technology, and then proposes a principle and integrated control strategy of the composite operation of TSC and SV...First, the paper analyzes the advantages and disadvantages of all kinds of reactive power compensation technology, and then proposes a principle and integrated control strategy of the composite operation of TSC and SVG, also the paper designs and develops the main controller of Network based composite power quality regulation device, based on RTDS, the real-time digital simulation model of The Device is established, and finally the prototype of the device is developed with the function of filter and split-phase compensation. The main controller determines the cooperative operation of both TSC and SVG, and the switching strategy of TSC. The simulation result in RTDS can verify the precision of the measure system and the validity of the control logic, the prototype has finished the type test according to the national standard.展开更多
Although the significant roles of magnetic induction and electromagnetic radiation in the neural system have been widely studied,their influence on Parkinson’s disease(PD)has yet to be well explored.By virtue of the ...Although the significant roles of magnetic induction and electromagnetic radiation in the neural system have been widely studied,their influence on Parkinson’s disease(PD)has yet to be well explored.By virtue of the magnetic flux variable,this paper studies the transition of firing patterns induced by magnetic induction and the regulation effect of external magnetic radiation on the firing activities of the subthalamopallidal network in basal ganglia.We find:(i)The network reproduces five typical waveforms corresponding to the severity of symptoms:weak cluster,episodic,continuous cluster,episodic,and continuous wave.(ii)Magnetic induction is a double-edged sword for the treatment of PD.Although the increase of magnetic coefficient may lead the physiological firing activity to transfer to pathological firing activity,it also can regulate the pathological intensity firing activity with excessiveβ-band power transferring to the physiological firing pattern with weakβ-band power.(iii)External magnetic radiation could inhibit continuous tremulous firing andβ-band power of subthalamic nucleus(STN),which means the severity of symptoms weakened.Especially,the bi-parameter plane of the regulation region shows that a short pulse period of magnetic radiation and a medium level of pulse percentage can well regulate pathological oscillation.This work helps to understand the firing activity of the subthalamopallidal network under electromagnetic effect.It may also provide insights into the mechanisms behind the electromagnetic therapy of PD-related firing activity.展开更多
The advent of the Next Generation Network (NGN), a new service-driven network, urges the telecom service operators to consider transforming from single-service providers to full-service providers. During the transform...The advent of the Next Generation Network (NGN), a new service-driven network, urges the telecom service operators to consider transforming from single-service providers to full-service providers. During the transformation, they should be concerned about the network user number and the network quality as well as the value added network information. The low threshold for service provision brings a new breed of service providers, which impacts upon the current regulation policy. To adapt to the development of the NGN, it is a necessity to improve the regulation policy in terms of service operators management, user management, Quality of Service (QoS) assurance, service monitoring, charging, and settlement. Meanwhile, regulatory authorities should establish a new body as quickly as possible to meet the trend of the NGN convergence. The new regulatory body would be responsible for regulating operators who will be awarded full-service licenses, and managing new service providers effectively to guarantee the user’s interests.展开更多
With the national electrification level getting higher and higher, peoples dependence on electric energy in production and life is also getting higher and higher. Therefore, people will pay more and more attention to ...With the national electrification level getting higher and higher, peoples dependence on electric energy in production and life is also getting higher and higher. Therefore, people will pay more and more attention to the stable operation of the power system. Electricity is also indispensable to the mechanical work in industry, to all kinds of mechanical work in rural areas, and to all kinds of household appliances in daily life. Therefore, to ensure the stable operation of the urban distribution network can not only meet the needs of peoples lives, but also the inevitable needs of economic and social production development. This paper focuses on the in-depth analysis of the operation and management of the urban distribution network, and also gives some suggestions on the operation and management of the county distribution network.展开更多
In this paper,a dynamic flow-regulation algorithm-oriented network overload control is proposed.It can proportion-ally distribute the load between the high-degree nodes and the low-degree nodes.According to the theore...In this paper,a dynamic flow-regulation algorithm-oriented network overload control is proposed.It can proportion-ally distribute the load between the high-degree nodes and the low-degree nodes.According to the theoretical analysis,the net-work transmission performance of the proposed algorithm is in inverse proportion to the usage rate of the high-degree nodes.Simulations show that the new algorithm is more flexible and can enhance the network capability in most circumstances compared with the shortest path routing algorithm.Moreover,the compari-son with the efficient routing algorism also reveals the prominent performance of the new algorithm.展开更多
Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a s...Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.展开更多
Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In ...Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.展开更多
The acute effect of acupuncture on Alzheimer＇s disease,i.e.,on brain activation during treatment,has been reported.However,the effect of long-term acupuncture on brain activation in Alzheimer＇s disease is unclear.Th...The acute effect of acupuncture on Alzheimer＇s disease,i.e.,on brain activation during treatment,has been reported.However,the effect of long-term acupuncture on brain activation in Alzheimer＇s disease is unclear.Therefore,in this study,we performed long-term needling at Zusanli（ST36）or a sham point（1.5 mm lateral to ST36）in a rat Alzheimer＇s disease model,for 30 minutes,once per day,for 30 days.The rats underwent 18F-fluorodeoxyglucose positron emission tomography scanning.Positron emission tomography images were processed with SPM2.The brain areas activated after needling at ST36 included the left hippocampus,the left orbital cortex,the left infralimbic cortex,the left olfactory cortex,the left cerebellum and the left pons.In the sham-point group,the activated regions were similar to those in the ST36 group.However,the ST36 group showed greater activation in the cerebellum and pons than the sham-point group.These findings suggest that long-term acupuncture treatment has targeted regulatory effects on multiple brain regions in rats with Alzheimer＇s disease.展开更多
Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actu...Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly. In this paper, we propose a novel approach based on combining neuro-fu^zy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory nctworks＋ but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without lhctitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast, The results show that this approach can work effectively.展开更多
The integration of distributed energy resources(DERs)has escalated the challenge of voltage magnitude regulation in distribution networks.Model-based approaches,which rely on complex sequential mathematical formulatio...The integration of distributed energy resources(DERs)has escalated the challenge of voltage magnitude regulation in distribution networks.Model-based approaches,which rely on complex sequential mathematical formulations,cannot meet the real-time demand.Deep reinforcement learning(DRL)offers an alternative by utilizing offline training with distribution network simulators and then executing online without computation.However,DRL algorithms fail to enforce voltage magnitude constraints during training and testing,potentially leading to serious operational violations.To tackle these challenges,we introduce a novel safe-guaranteed reinforcement learning algorithm,the Dist Flow safe reinforcement learning(DF-SRL),designed specifically for real-time voltage magnitude regulation in distribution networks.The DF-SRL algorithm incorporates a Dist Flow linearization to construct an expert-knowledge-based safety layer.Subsequently,the DF-SRL algorithm overlays this safety layer on top of the agent policy,recalibrating unsafe actions to safe domains through a quadratic programming formulation.Simulation results show the DF-SRL algorithm consistently ensures voltage magnitude constraints during training and real-time operation(test)phases,achieving faster convergence and higher performance,which differentiates it apart from(safe)DRL benchmark algorithms.展开更多
基金funded by the State Grid Corporation Science and Technology Project(5108-202218280A-2-391-XG).
文摘The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.
文摘Dear Editor,This letter focuses on the distributed cooperative regulation problem for a class of networked re-entrant manufacturing systems(RMSs).The networked system is structured with a three-tier architecture:the production line,the manufacturing layer and the workshop layer.The dynamics of re-entrant production lines are governed by hyperbolic partial differential equations(PDEs)based on the law of mass conservation.
基金the National Natural Science Foundation of China Project:the Study of Central Mechanism of Electroacupuncture on Sishencong (EX-HN1) Improving Sleep Architecture and Neurocognitive Function Using Electroencephalogram-Functional Magnetic Resonance Imaging (No. 81774426)Major Scientific Research Project of Wuxi Health Commission:Clinical Study of Intelligent Hand Rehabilitation Training System for Nerve Function Reconstruction of Patients with Hand Dysfunction after Cerebral Infarction (No. Z202121)。
文摘OBJECTIVE: To analyze part of the mechanism of electroacupuncture on Sishencong(EX-HN1) for strokerelated sleep disorders(SSD) and post-stroke cognitive impairment(PSCI). METHODS: Using a randomized controlled trial(RCT) design, 72 patients were assigned to the electroacupuncture(EA) group or the sham acupuncture(SA) group. A healthy control(HC) group was also included. Both groups were given routine rehabilitation treatment. Then, patients in the EA group were given additional electroacupuncture at Sishencong(EX_HN1). Meanwhile, patients in the SA group were given a flat-head needle sham/placebo treatment placed at the bilateral Jianyu (LI15) and Binao(LI14) line midpoints and the Jianyu(LI15) and Jianzhen(SI9) line midpoints. Before and after treatment, scales were collected and analyzed. In the second phase of the study, some subjects from the EA group were selected for functional magnetic resonance imaging(f MRI) data acquisition and comparative analysis with the HC group using a non-RCT design. RESULTS: The EA group performed better than the SA group on the Pittsburgh sleep quality index(PSQI), Montreal cognitive assessment basic(Mo CA_B), selfrating anxiety scale(SAS), and self-rating depression scale(SDS). Analysis of the f MRI showed that lowfrequency(2 Hz) electroacupuncture stimulation at Sishencong(EX_HN1) can restrain frontal sup medial right(SFGmed.R), precuneus right(PCUN.R), and posterior cingulate cortex right(PCC.R) and enhance angular left(ANG.L), parietal inf left(IPL.L) and occipital mid left(MOG.L). The functional connectivity(FC) of SFGmed.R was positively correlated with PSQI. Electroacupuncture stimulation at Sishencong(EX_HN1) can reduce the side efficiency of the whole brain connection with the Thalamus.L, Hippocampus.L, and Occipital.Mid.L. CONCLUSIONS: Low frequency(2 Hz) electroacupuncture stimulation at Sishencong(EX_HN1) can simultaneously improve sleep quality, negative emotions, and cognitive functions, the first two of which may be related to SFGmed.R restraint. Electroacupuncture can make some brain areas approach the physiological bias state, which is characterized by dominant hemispheric enhancement and non-dominant hemispheric weakening. The reduced whole brain connection side efficiency with some key nodes of the brain net may relate to sleep quality improvements in SSD patients.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 62266030 and 61863025)。
文摘Regulation plays a pivotal role in mitigating the spread of rumors, serving as a vital tool for maintaining social stability and facilitating its evolution. A central challenge lies in establishing an effective regulatory framework despite limited resources available for combating rumor propagation. To address this challenge, this paper proposes a dynamic and adaptive regulatory system. First, based on observed regulatory patterns in real-world social networks, the rumor propagation process is divided into two distinct phases: regulation and intervention. Regulatory intensity is introduced as an indicator of user state transitions. Unlike traditional, non-adaptive regulatory models that allocate costs uniformly,the adaptive model facilitates flexible cost distribution through a manageable individual regulatory intensity. Moreover,by introducing adaptive strength, the two cost allocation models are integrated within a unified framework, leading to the development of a dynamic model for rumor suppression. Finally, simulation experiments on Barabási–Albert(BA)networks demonstrate that the adaptive regulatory mechanism significantly reduces both the scope and duration of rumor propagation. Furthermore, when traditional non-adaptive regulatory models show limited effectiveness, the adaptive model effectively curbs rumor propagation by optimizing cost allocation between regulatory and intervention processes, and by adjusting per-unit cost benefit differentials.
基金Supported by National Natural Science Foundation of China,No.82471616,No.82170418,and No.82271618Natural Science Foundation of Hubei Province,No.2022CFA015+2 种基金Central Guiding Local Science and Technology Development Project,No.2022BGE237Key Research and Development Program of Hubei Province,No.2022BCE001,and No.2023BCB139Hubei Provincial Health Commission Project,No.WJ2023M151。
文摘We are deeply interested in the recent findings onβ-arrestin 2.Liu et al demonstrated thatβ-arrestin 2 knockout provides significant protection in diabetic nephropathy,underscoring its potential as a promising therapeutic target for diabetic nephropathy treatment.Furthermore,the role ofβ-arrestin 2 in metabolic regulation is equally critical,particularly in insulin signaling,hepatic glucose production,and adipose tissue function.Althoughβ-arrestin 2 plays a distinct role in metabolism and kidney protection,its tissue-specific regulation opens up valuable avenues for developing targeted therapeutic strategies centered onβ-arrestin 2.
基金supported by the National Natural Science Foundation of China(Nos.61973172,62003177,62003175 and 61973175).
文摘The aviation oxygen mask,which has a small volume of less than 1L and strong air tightness,imposes extremely high requirements on control performance of the oxygen regulator.Based on analyses of the operation principle of oxygen supply system,the dynamic model is established through the combination of mechanism analysis and experimental data.Considering that the traditional fixed-parameter controllers are difficult to meet the control requirements with changes in pulmonary ventilation,this paper presents an online feedback controller based on neural network compensation(NNC),with connection weights that can be updated without pre-training.Then mathematical simulations at different respiratory parameters,such as respiratory rate,are performed to verify the superior lower inspiratory resistance of controller with NNC.In terms of hardware,an embedded AI control platform is to complete the experimental verification.Furthermore,the work may have downward compatibility to achieve stable oxygen supply in civil fields,such as medical ventilators,high-altitude expeditions.
基金supported by National Natural Science Foundation of China (Grant No. 50505042)
文摘In the energy regulation based varibable-speed electrohydraulic drive system, the supply energy and the demanded energy, which will affect the control performance greatly, are crucial. However, they are hard to be obtained via conventional methods for some reasons. This paper tries to a new route： the definitive numerical values of the supply energy and the demanded energy are not required, except for their relationship which is called energy state. A three-layer back propagation（BP） neural network was built up to act as an energy analysis unit to deduce the energy state. The neural network has three inputs： the reference displacement, the actual displacement of cylinder rod and the system flowrate supply. The output of the neural network is energy state. A Chebyshev type II filter was designed to calculate the cylinder speed for the estimation of system flowrate supply. The training and testing samples of neural network were collected by the system accurate simulation model. After off-line training, the neural network was tested by the testing data. And the testing result demonstrates that the designed neural network was successful. Then, the neural network acts as the energy analysis unit in real-time experiments of cylinder position control, where it works efficiently under square-wave and sine-wave reference displacement. The experimental results validate its feasibility and adaptability. Only a position sensor and some pressure sensors, which are cheap and have quick dynamic response, are necessary for the system control. And the neural network plays the role of identifying the energy state.
基金Shaanxi Province key Research and Development Plan-Listed project(2022-JBGS-07)。
文摘Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.
基金supported by grants from the National Natural Science Foundation of China(Grant No.82172723)the Natural Science Foundation of Sichuan(Grant Nos.2023NSFSC1828 and 2022NSFSC1289)+2 种基金the“Xinglin Scholar”Scientific Research Promotion Plan of Chengdu University of Transitional Chinese Medicine(Grant No.BSH2021003)the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(Grant No.ZYYCXTD-D-202209)the Research Funding of Department of Science and Technology of Qinghai Province(Grant No.2023-ZJ-729)。
文摘Objective:Epigenetic abnormalities have a critical role in breast cancer by regulating gene expression;however,the intricate interrelationships and key roles of approximately 400 epigenetic regulators in breast cancer remain elusive.It is important to decipher the comprehensive epigenetic regulatory network in breast cancer cells to identify master epigenetic regulators and potential therapeutic targets.Methods:We employed high-throughput sequencing-based high-throughput screening(HTS^(2))to effectively detect changes in the expression of 2,986 genes following the knockdown of 400 epigenetic regulators.Then,bioinformatics analysis tools were used for the resulting gene expression signatures to investigate the epigenetic regulations in breast cancer.Results:Utilizing these gene expression signatures,we classified the epigenetic regulators into five distinct clusters,each characterized by specific functions.We discovered functional similarities between BAZ2B and SETMAR,as well as CLOCK and CBX3.Moreover,we observed that CLOCK functions in a manner opposite to that of HDAC8 in downstream gene regulation.Notably,we constructed an epigenetic regulatory network based on the gene expression signatures,which revealed 8 distinct modules and identified 10 master epigenetic regulators in breast cancer.Conclusions:Our work deciphered the extensive regulation among hundreds of epigenetic regulators.The identification of 10 master epigenetic regulators offers promising therapeutic targets for breast cancer treatment.
基金Supported by the Postdoctoral Science Foundation of China(No.2022M710039)。
文摘When large-scale distributed renewable energy power generation systems are connected to the power grid,the risk of grid voltage fluctuations and exceeding the limit increases greatly.Fortunately,the on-load tap changer(OLTC)can adjust the transformer winding tap to maintain the secondary side voltage within the normal range.However,the inevitable delay in switching transformer taps makes it difficult to respond quickly to voltage fluctuations.Moreover,switching the transformer taps frequently will decrease the service life of OLTC.In order to solve this critical issue,a cooperative voltage regulation strategy applied between the battery energy storage systems(BESSs)and OLTSs.is proposed By adjusting the charge and discharge power of BESSs,the OLTC can frequently switch the transformer taps to achieve rapid voltage regulation.The effectiveness of the proposed coordinated regulation strategy is verified in the IEEE 33 node distribution systems.The simulation results show that the proposed coordinated regulation strategy can stabilize the voltage of the distribution network within a normal range and reduce the frequency of tap switching,as such elongating the service life of the equipment.
文摘First, the paper analyzes the advantages and disadvantages of all kinds of reactive power compensation technology, and then proposes a principle and integrated control strategy of the composite operation of TSC and SVG, also the paper designs and develops the main controller of Network based composite power quality regulation device, based on RTDS, the real-time digital simulation model of The Device is established, and finally the prototype of the device is developed with the function of filter and split-phase compensation. The main controller determines the cooperative operation of both TSC and SVG, and the switching strategy of TSC. The simulation result in RTDS can verify the precision of the measure system and the validity of the control logic, the prototype has finished the type test according to the national standard.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11972292,12172291,and 12072265)the 111 Project(Grant No.BP0719007)。
文摘Although the significant roles of magnetic induction and electromagnetic radiation in the neural system have been widely studied,their influence on Parkinson’s disease(PD)has yet to be well explored.By virtue of the magnetic flux variable,this paper studies the transition of firing patterns induced by magnetic induction and the regulation effect of external magnetic radiation on the firing activities of the subthalamopallidal network in basal ganglia.We find:(i)The network reproduces five typical waveforms corresponding to the severity of symptoms:weak cluster,episodic,continuous cluster,episodic,and continuous wave.(ii)Magnetic induction is a double-edged sword for the treatment of PD.Although the increase of magnetic coefficient may lead the physiological firing activity to transfer to pathological firing activity,it also can regulate the pathological intensity firing activity with excessiveβ-band power transferring to the physiological firing pattern with weakβ-band power.(iii)External magnetic radiation could inhibit continuous tremulous firing andβ-band power of subthalamic nucleus(STN),which means the severity of symptoms weakened.Especially,the bi-parameter plane of the regulation region shows that a short pulse period of magnetic radiation and a medium level of pulse percentage can well regulate pathological oscillation.This work helps to understand the firing activity of the subthalamopallidal network under electromagnetic effect.It may also provide insights into the mechanisms behind the electromagnetic therapy of PD-related firing activity.
文摘The advent of the Next Generation Network (NGN), a new service-driven network, urges the telecom service operators to consider transforming from single-service providers to full-service providers. During the transformation, they should be concerned about the network user number and the network quality as well as the value added network information. The low threshold for service provision brings a new breed of service providers, which impacts upon the current regulation policy. To adapt to the development of the NGN, it is a necessity to improve the regulation policy in terms of service operators management, user management, Quality of Service (QoS) assurance, service monitoring, charging, and settlement. Meanwhile, regulatory authorities should establish a new body as quickly as possible to meet the trend of the NGN convergence. The new regulatory body would be responsible for regulating operators who will be awarded full-service licenses, and managing new service providers effectively to guarantee the user’s interests.
文摘With the national electrification level getting higher and higher, peoples dependence on electric energy in production and life is also getting higher and higher. Therefore, people will pay more and more attention to the stable operation of the power system. Electricity is also indispensable to the mechanical work in industry, to all kinds of mechanical work in rural areas, and to all kinds of household appliances in daily life. Therefore, to ensure the stable operation of the urban distribution network can not only meet the needs of peoples lives, but also the inevitable needs of economic and social production development. This paper focuses on the in-depth analysis of the operation and management of the urban distribution network, and also gives some suggestions on the operation and management of the county distribution network.
基金Supported by the National Natural Science Foundation of China (60970114 , 41104010)the National Natural Science Foundation of Hubei Province (ZRZ0041)+2 种基金the Science and Technology Project of Wuhan City (201110921292)the Open Fund of the Ministry of Public Security of the Information Network Security Key Laboratory (C10607)the Independent Scientific Research Projects of the Wuhan University Graduate Student (201121102020002)
文摘In this paper,a dynamic flow-regulation algorithm-oriented network overload control is proposed.It can proportion-ally distribute the load between the high-degree nodes and the low-degree nodes.According to the theoretical analysis,the net-work transmission performance of the proposed algorithm is in inverse proportion to the usage rate of the high-degree nodes.Simulations show that the new algorithm is more flexible and can enhance the network capability in most circumstances compared with the shortest path routing algorithm.Moreover,the compari-son with the efficient routing algorism also reveals the prominent performance of the new algorithm.
基金Projects(90820302,60805027)supported by the National Natural Science Foundation of ChinaProject(200805330005)supported by the Research Fund for the Doctoral Program of Higher Education,ChinaProject(2009FJ4030)supported by Academician Foundation of Hunan Province,China
文摘Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.
基金supported by the National Natural Science Foundation of China(No.62276204)Open Foundation of Science and Technology on Electronic Information Control Laboratory,Natural Science Basic Research Program of Shanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470).
文摘Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.
基金supported by the National Basic Research Program of China(973 Program),No.2006CB504505,2012CB518504the National Natural Science Foundation of China,No.90709027+1 种基金the Student's Platform for Innovation and Entrepreneurship Training Program of Southern Medical University of China,No.201512121165the Doctoral Foundation of Guangdong Medical University of China,No.2XB13058
文摘The acute effect of acupuncture on Alzheimer＇s disease,i.e.,on brain activation during treatment,has been reported.However,the effect of long-term acupuncture on brain activation in Alzheimer＇s disease is unclear.Therefore,in this study,we performed long-term needling at Zusanli（ST36）or a sham point（1.5 mm lateral to ST36）in a rat Alzheimer＇s disease model,for 30 minutes,once per day,for 30 days.The rats underwent 18F-fluorodeoxyglucose positron emission tomography scanning.Positron emission tomography images were processed with SPM2.The brain areas activated after needling at ST36 included the left hippocampus,the left orbital cortex,the left infralimbic cortex,the left olfactory cortex,the left cerebellum and the left pons.In the sham-point group,the activated regions were similar to those in the ST36 group.However,the ST36 group showed greater activation in the cerebellum and pons than the sham-point group.These findings suggest that long-term acupuncture treatment has targeted regulatory effects on multiple brain regions in rats with Alzheimer＇s disease.
基金Acknowledgement This paper is supported by National Natural Science Foundation of China （Grant No. 60973092 and No. 60873146）, the National High Technology Research and Development Program of China （Grant No.2009 AA02Z307）, the ＂211 Project＂ of Jilin University, the Key Laboratory for Symbol Computation and Knowledge Engineering （Ministry of Education, China）, and the Key Laboratory for New Technology of Biological Recognition of Jilin Province （No. 20082209）.
文摘Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly. In this paper, we propose a novel approach based on combining neuro-fu^zy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory nctworks＋ but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without lhctitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast, The results show that this approach can work effectively.
基金part of the DATALESs project(with project number 482.20.602)jointly financed by the Netherlands Organization for Scientific Research(NWO)the National Natural Science Foundation of China(NSFC)。
文摘The integration of distributed energy resources(DERs)has escalated the challenge of voltage magnitude regulation in distribution networks.Model-based approaches,which rely on complex sequential mathematical formulations,cannot meet the real-time demand.Deep reinforcement learning(DRL)offers an alternative by utilizing offline training with distribution network simulators and then executing online without computation.However,DRL algorithms fail to enforce voltage magnitude constraints during training and testing,potentially leading to serious operational violations.To tackle these challenges,we introduce a novel safe-guaranteed reinforcement learning algorithm,the Dist Flow safe reinforcement learning(DF-SRL),designed specifically for real-time voltage magnitude regulation in distribution networks.The DF-SRL algorithm incorporates a Dist Flow linearization to construct an expert-knowledge-based safety layer.Subsequently,the DF-SRL algorithm overlays this safety layer on top of the agent policy,recalibrating unsafe actions to safe domains through a quadratic programming formulation.Simulation results show the DF-SRL algorithm consistently ensures voltage magnitude constraints during training and real-time operation(test)phases,achieving faster convergence and higher performance,which differentiates it apart from(safe)DRL benchmark algorithms.
