Role of slow pathway after nodal fast pathway ablation on the basic and rate-dependent properties of the isolated rabbit atrioventricular node

Introduction: The aim of this study was to obtain new insights into the possible relations between functional properties of slow concealed pathway and rate-dependent properties of the AV-node. Methods: Rate-dependent nodal properties of recovery, facilitation, and fatigue were characterized by stimulation protocols in one group of isolated superfused AV-Nodal rabbits (n=7). Small miniature lesions were made by delivering constant voltage (110 V-100 s) with unipolar silver electrode. Results: Fast pathway ablation significantly decreased facilitation and had no effect on fatigue and nodal refractoriness. The most important effect of fast pathway ablation was prolongation of the minimum conduction time. Conclusion: The FP-ablation revealed the presence of the concealed SP. Rate-dependent property of the node is dependent to dynamic interaction between concealed slow with slow pathway. The fast pathway was involved in the mechanism of facilitation

Assessing prioritizing the key factors affecting job involvement of the employees among holding company of production, transmission and distribution of electricity management (Tavanir)

The aim of this study is to assess and prioritize the key factors affecting job involvement of the employees of the holding company of production, transmission and distribution of electricity management (Tavanir) and for this purpose 224 of the managers, deputies and employees of the holding company of production, transmission and distribution of electricity management (Tavanir) of the city of Tehran have been selected with the use of simple random sampling method and have responded to the author-made questionnaire. Finally, the obtained data from the research questionnaires have been analyzed with the use of two-variable linear regression and Friedman test. The results indicated that perceived organizational support and personal interaction networks have an effect on employees’ personality characteristics and also the personal interaction networks has a positive and significant effect (p<0.05) on perceived organizational support of employees. The results of the Friedman test did not indicate a significant difference between the priority of each of these factors among manages and deputies, while the priority of each of them is different for the employees

Assessing prioritizing the key factors affecting job involvement of the employees among holding company of production, transmission and distribution of electricity management (Tavanir)

The aim of this study is to assess and prioritize the key factors affecting job involvement of the employees of the holding company of production, transmission and distribution of electricity management (Tavanir) and for this purpose 224 of the managers, deputies and employees of the holding company of production, transmission and distribution of electricity management (Tavanir) of the city of Tehran have been selected with the use of simple random sampling method and have responded to the author-made questionnaire. Finally, the obtained data from the research questionnaires have been analyzed with the use of two-variable linear regression and Friedman test. The results indicated that perceived organizational support and personal interaction networks have an effect on employees’ personality characteristics and also the personal interaction networks has a positive and significant effect (p<0.05) on perceived organizational support of employees. The results of the Friedman test did not indicate a significant difference between the priority of each of these factors among manages and deputies, while the priority of each of them is different for the employees

Assessing prioritizing the key factors affecting job involvement of the employees among holding company of production, transmission and distribution of electricity management (Tavanir)

The aim of this study is to assess and prioritize the key factors affecting job involvement of the employees of the holding company of production, transmission and distribution of electricity management (Tavanir) and for this purpose 224 of the managers, deputies and employees of the holding company of production, transmission and distribution of electricity management (Tavanir) of the city of Tehran have been selected with the use of simple random sampling method and have responded to the author-made questionnaire. Finally, the obtained data from the research questionnaires have been analyzed with the use of two-variable linear regression and Friedman test. The results indicated that perceived organizational support and personal interaction networks have an effect on employees’ personality characteristics and also the personal interaction networks has a positive and significant effect (p<0.05) on perceived organizational support of employees. The results of the Friedman test did not indicate a significant difference between the priority of each of these factors among manages and deputies, while the priority of each of them is different for the employees

Flood routing via a copula-based approach

Abstract Floods are among the most common natural disasters that if not controlled may cause severe damage and high costs. Flood control and management can be done using structural measures that should be designed based on the flood design studies. The simulation of outflow hydrograph using inflow hydrograph can provide useful information. In this study, a copula-based approach was applied to simulate the outflow hydrograph of various floods, including the Wilson River flood, the River Wye flood and the Karun River flood. In this regard, two-dimensional copula functions and their conditional density were used. The results of evaluating the dependence structure of the studied variables (inflow and outflow hydrographs) using Kendall's tau confirmed the applicability of copula functions for bivariate modeling of inflow and outflow hydrographs. The simulation results were evaluated using the root-mean-square error, the sum of squared errors and the Nash–Sutcliffe efficiency coefficient (NSE). The results showed that the copula-based approach has high performance. In general, the copula-based approach has been able to simulate the peak flow and the rising and falling limbs of the outflow hydrographs well. Also, all simulated data are at the 95% confidence interval. The NSE values for the copula-based approach are 0.99 for all three case studies. According to NSE values and violin plots, it can be seen that the performance of the copula-based approach in simulating the outflow hydrograph in all three case studies is acceptable and shows a good performance

Analyzing the conditional behavior of rainfall deficiency and groundwater level deficiency signatures by using copula functions

Abstract The complex hydrological events such as storm, flood and drought are often characterized by a number of correlated random variables. Copulas can model the dependence structure independently of the marginal distribution functions and provide multivariate distributions with different margins and the dependence structure. In this study, the conditional behavior of two signatures was investigated by analyzing the joint signatures of groundwater level deficiency and rainfall deficiency in Naqadeh sub-basin in Lake Urmia Basin using copula functions. The study results of joint changes in the two signatures showed that a 90–135 mm reduction in rainfall in the area increased groundwater level between 1.2 and 1.7 m. The study results of the conditional density of bivariate copulas in the estimation of groundwater level deficiency values by reducing rainfall showed that changes in values of rainfall deficiency signature in the sub-basin led to the generation of probability curves of groundwater level deficiency signature. Regarding the maximum groundwater level deficiency produced, the relationship between changes in rainfall deficiency and groundwater level deficiency was calculated in order to estimate the groundwater level deficiency signature values. The conditional density function presented will be an alternative method to the conditional return period

Optimal power control in green wireless sensor networks with wireless energy harvesting, wake-up radio and transmission control

Wireless sensor networks (WSNs) are autonomous networks of spatially distributed sensor nodes which are capable of wirelessly communicating with each other in a multi-hop fashion. Among different metrics, network lifetime and utility and energy consumption in terms of carbon footprint are key parameters that determine the performance of such a network and entail a sophisticated design at different abstraction levels. In this paper, wireless energy harvesting (WEH), wake-up radio (WUR) scheme and error control coding (ECC) are investigated as enabling solutions to enhance the performance of WSNs while reducing its carbon footprint. Specifically, a utility-lifetime maximization problem incorporating WEH, WUR and ECC, is formulated and solved using distributed dual subgradient algorithm based on Lagrange multiplier method. It is discussed and verified through simulation results to show how the proposed solutions improve network utility, prolong the lifetime and pave the way for a greener WSN by reducing its carbon footprint

A novel method for maximum power point tracking of the grid-connected three-phase solar systems based on the PV current prediction

In this paper, it is first attempted to provide a small signal model of the photovoltaic (PV) system, DC-DC boost converter, and pulse width modulation (PWM) generator. Then, a technique is provided for maximum power point tracking (MPPT) in grid-connected solar systems based on variable and adaptive perturbation and observation with predictive control of the PV current. An innovative aspect of the proposed predictive current control method is to use the current controller to achieve the value of PV impedance, which has been used in DC-DC boost converter. The proposed method is to obtain the coming current value on the basis of the current predictive model. The goal of the proposed method is to make the DC-DC boost converter inductor current track the current reference. Voltage and current ripple minimization is added to the cost function simultaneously as a system constraint to optimize system performance. This reduces the amount of voltage and current fluctuations around the maximum power point. The proposed method is capable of detecting rapid changes in solar radiation. A sudden and simultaneous increase in voltage and current is detected by the algorithm and then the duty cycle becomes increasing instead of decreasing. The simulation is carried out in MATLAB Simulink environment in real-time for a 26.6 kW three-phase grid-connected solar system. The simulation results of current predictive control are compared with perturbation and observation techniques and linear voltage and current proportional integral derivative (PID) controller-based adaptive control. The results show that the total harmonic distortion (THD%) of the inverter voltage with proposed method has been reduced by 0.16% compared to the PID method. In addition, the THD% of the current in the proposed method is reduced by 0.1% compared to the PID method. The solar system output voltage variation of the proposed method is less than 5 V

Wireless energy harvesting for Internet of Things

Internet of Things (IoT) is an emerging computing concept that describes a structure in which everyday physical objects, each provided with unique identifiers, are connected to the Internet without requiring human interaction. Long-term and self-sustainable operation are key components for realization of such a complex network, and entail energy-aware devices that are potentially capable of harvesting their required energy from ambient sources. Among different energy harvesting methods such as vibration, light and thermal energy extraction, wireless energy harvesting (WEH) has proven to be one of the most promising solutions by virtue of its simplicity, ease of implementation and availability. In this article, we present an overview of enabling technologies for efficient WEH, analyze the life-time of WEH-enabled IoT devices, and briefly study the future trends in the design of efficient WEH systems and research challenges that lie ahead

http://www.agrimet.ir/article_69418_f4e2bbe100cda91f8c910d33a104a621.pdf

Incomplete rainfall datasets with missing gaps is a major challenge in climatology and water resource studies. In the present study, two intelligent models, namely Genetic Programing (GP) and Support Vector Machines (SVM) were used to reconstruct the monthly rainfall data of four rain-gauges located in Hamedan province, Iran during the period of 1992 to 2011. The incomplete rainfall data was reconstructed first by using the data of one, two and three stations respectively. The results showed that increasing the memory and the number of stations involved in the training phase, will improve the performance of the models. In reconstruction of monthly precipitation data of Sarabi and Maryanj stations, the Support Vector Machine method showed better performance with RMSE of 12.9 mm and 11.4 mm, and correlation coefficients (r) of 0.93 and 0.95, respectively. The corresponding values of RMSE for GP approach were 13 mm and 12.21 mm, which indicated the superior performance of SVM