Captopril reverses chronic unpredictable mild stress-induced depression-like behavior in rats via bradykinin-B2r signaling pathway

Purpose: To investigate the effect of captopril on chronic unpredictable mild stress (CUMS)-induced depression-like behavior in mice, and the involvement of the bradykinin-B2r signaling pathway in the process. Methods: Sixty healthy male C57BL/6J mice were assigned to control, model and high-, medium- and low-dose captopril groups and given the drug at doses of 9, 18 and 36 mg/kg, respectively. Open field and elevated cross maze tests were carried out, and escape latency in Morris water maze test was also test. The expressions of bradykinin B2R signal pathway proteins were assayed. Results: Open arm residence time and open arm entry times were significantly higher in captopril-exposed mice than in model mice, while 5-day escape latency values were significantly less in captopril-treated mice than in model group (p < 0.05). Protein expressions of B2R, bpnf and Cdc42 in captopril groups were significantly higher than those in model group (p < 0.05). Conclusion: Captopril mitigates CUMS-mediated depression-like disease in mice by regulating bradykinin B2R signal pathway. Therefore, captopril may play an antidepressant role by activating the expressions of B2R, bpnf and Cdc42

Power Fiber to the Home Opens Up a New Approach of Integration of Three Networks

AbstractThis thesis analyzes the development trend of power fiber to the home (PFTTH) and the current domestic situation of the integration of three networks. It develops its research about the integration system constitution of three networks based on power fiber to the home and proposes the research, development and implementation of power fiber to the home, which not only establishes new “information highway” based on the smart power grid, but also guarantees the realization of the state's planning objective of the integration of three networks to enable the rapid development of information industry in Chin

Energy-Efficient Train Control with Onboard Energy Storage Systems considering Stochastic Regenerative Braking Energy

With the rapid development of energy storage technology, onboard energy storage systems(OESS) have been applied in modern railway systems to help reduce energy consumption. In addition, regenerative braking energy utilization is becoming increasingly important to avoid energy waste in the railway systems, undermining the sustainability of urban railway transportation. However, the intelligent energy management of the trains equipped with OESSs considering regenerative braking energy utilization is still rare in the field. This paper considers the stochastic characteristics of the regenerative braking power distributed in railway power networks. It concurrently optimizes the train trajectory with OESS and regenerative braking energy utilization. The expected regenerative braking power distribution can be obtained based on the Monte-Carlo simulation of the train timetable. Then, the integrated optimization using mixed integer linear programming (MILP) can be conducted and combined with the expected available regenerative braking energy. A generic four-station railway system powered by one traction substation is modeled and simulated for the study. The results show that by applying the proposed method, 68.8% of the expected regenerative braking energy in the environment will be further utilized. The expected amount of energy from the traction substation is reduced by 22.0% using the proposed train control method to recover more regenerative braking energy from improved energy interactions between trains and OESSs

Energy-Efficient Train Control with Onboard Energy Storage Systems considering Stochastic Regenerative Braking Energy

With the rapid development of energy storage technology, onboard energy storage systems(OESS) have been applied in modern railway systems to help reduce energy consumption. In addition, regenerative braking energy utilization is becoming increasingly important to avoid energy waste in the railway systems, undermining the sustainability of urban railway transportation. However, the intelligent energy management of the trains equipped with OESSs considering regenerative braking energy utilization is still rare in the field. This paper considers the stochastic characteristics of the regenerative braking power distributed in railway power networks. It concurrently optimizes the train trajectory with OESS and regenerative braking energy utilization. The expected regenerative braking power distribution can be obtained based on the Monte-Carlo simulation of the train timetable. Then, the integrated optimization using mixed integer linear programming (MILP) can be conducted and combined with the expected available regenerative braking energy. A generic four-station railway system powered by one traction substation is modeled and simulated for the study. The results show that by applying the proposed method, 68.8% of the expected regenerative braking energy in the environment will be further utilized. The expected amount of energy from the traction substation is reduced by 22.0% using the proposed train control method to recover more regenerative braking energy from improved energy interactions between trains and OESSs

Interrelation of structure and operational states in cascading failure of overloading lines in power grids

As the modern power system is expected to develop to a more intelligent and efficientversion, i.e. the smart grid, or to be the central backbone of energy internet for freeenergyinteractions,securityconcernsrelatedtocascadingfailureshavebeenraisedwithconsideration of catastrophic results. The researches of topological analysis based oncomplex networks have made great contributions in revealing structural vulnerabilitiesof power grids including cascading failure analysis. However, existing literature withinappropriate assumptions in modeling still cannot distinguish the effects between thestructure and operational state to give meaningful guidance for system operation. Thispaper is to reveal the interrelation between network structure and operational statesin cascading failure and give quantitative evaluation by integrating both perspectives.For structure analysis, cascading paths will be identified by extended betweenness andquantitatively described by cascading drop and cascading gradient. Furthermore, theoperational state for cascading paths will be described by loading level. Then, the riskof cascading failure along a specific cascading path can be quantitatively evaluatedconsideringthesetwofactors.Themaximumcascadinggradientofallpossiblecascadingpaths can be used as an overall metric to evaluate the entire power grid for its featuresrelated to cascading failure. The proposed method is tested and verified on IEEE30-bussystem and IEEE118-bus system, simulation evidences presented in this paper suggest

Energy-Efficient Train Control with Onboard Energy Storage Systems considering Stochastic Regenerative Braking Energy

With the rapid development of energy storage technology, onboard energy storage systems(OESS) have been applied in modern railway systems to help reduce energy consumption. In addition, regenerative braking energy utilization is becoming increasingly important to avoid energy waste in the railway systems, undermining the sustainability of urban railway transportation. However, the intelligent energy management of the trains equipped with OESSs considering regenerative braking energy utilization is still rare in the field. This paper considers the stochastic characteristics of the regenerative braking power distributed in railway power networks. It concurrently optimizes the train trajectory with OESS and regenerative braking energy utilization. The expected regenerative braking power distribution can be obtained based on the Monte-Carlo simulation of the train timetable. Then, the integrated optimization using mixed integer linear programming (MILP) can be conducted and combined with the expected available regenerative braking energy. A generic four-station railway system powered by one traction substation is modeled and simulated for the study. The results show that by applying the proposed method, 68.8% of the expected regenerative braking energy in the environment will be further utilized. The expected amount of energy from the traction substation is reduced by 22.0% using the proposed train control method to recover more regenerative braking energy from improved energy interactions between trains and OESSs