Data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) with multiple inputs and multiple outputs. DEA-based Malmquist productivity index measures the productivity change over time. We propose a Dynamic DEA model involving Network structure in each period within the framework a DEA. We have previously published the Network DEA (NDEA) and the Dynamic DEA (DDEA) models separately. Hence, this article is a composite of these two models. Vertically, we deal with multiple divisions connected by links of Network structure within each period and, horizontally, we combine the Network structure by means of carry-over activities between succeeding periods. We also introduce Dynamic Malmquist index by which we can compare divisional performances over time.

The paper discusses the current state of research concerning railway Network timetabling and traffic management. Timetable effectiveness is governed by frequency, regularity, accurate running, recovery and layover times, as well as minimal headway, buffer times and waiting times. Analytic (queuing) models and stochastic microsimulation are predominantly used for estimation of waiting times and capacity consumption anlong corridors and in stations, while combinatorial models and stability analysis are suitable for Network timetable optimisation. Efficient traffic management can be achieved by real-time monitoring, fusion, analysis and rescheduling of railway traffic in case of disturbances. Real-time simulation, optimisation and impact evaluation of dispatching measures can improve the effectiveness of rescheduling and traffic management. The display of Dynamic signal and track occupancy data in driver cabins, as Route Lint developed by ProRail, can support anticipative actions of the driver in order to reduce knock-on delays and increase throughput.

This paper deals with the problem of optimizing two-stage structure decision making units (DMUs) where the activity and the performance of two-stage DMU in one period e ect on its efficiency in the next period. To evaluate such systems the e ect of activities in one period on ones in the next term must be considered. To do so, here a Dynamic DEA approach presented to measure the performance of such Network units. According to the results of proposed Dynamic model the inefficiencies of DMUs improve considerably. Additionally, in models which measure efficiency score, undesirable outputs are mostly treated as inputs, which do not reect the true production process. This paper proposes an alternative method in dealing with bad outputs. Statistical analysis of sub-efficiencies, i.e. efficiency score of each stage, during all periods represents useful information about the total performance of the stage over all periods.

Entering into the interbank market in order to balance profitability and liquidity risk management, depending on the conditions of short-term activities, banks are required to equip resources through this market or to lend short-term loans to other banks. Banks' commitments to each other mainly arise in the interbank market, which can lead to increased systemic risk due to the spillover effect. Therefore, the objective of this paper is to analyze the Network Dynamic stability of the Iranian overnight money market through methods of statistical mechanics applied to complex Networks. The results show that the Network structure changes during time depending economic conditions. Systemic risk measures such as clustering coefficient, average short path, heterogeneity and centrality, show that the Networks systemic risk increases and then by occurring default and crisis in one bank, default spillover during the domino effect in whole Network. Also, in the event of failure, the most vulnerable group is to privatized and specialist governmental banks, and the private banks, due to the high volume of exchanges and net negative flows, can put a considerable systemic risk to the interbank market Network. Morever, the signals of speculative activity by private banks are found.

Social Network monitoring (SNM) can play a significant role in everyone’ s life. Recent studies show the importance and increasing interests in the subject by modeling and monitoring the communications between Network members over time by treating the collected observations as longitudinal data. Typically, the tendency for modeling social Networks, considering the dependency of an outcome variable on the covariates, is growing recently. However, these studies fail to incorporate the possible correlation between responses in the proposed models. In this paper, we use generalized linear mixed models (GLMMs), also referred to as random effects models, to model a social Network according to the attributes of nodes in which the nodes take a role of random effect or hidden effect in modeling. In order to estimate the regression parameters, Monte Carlo expectation maximization (MCEM) algorithm is used to maximize the likelihood function. In our simulation studies, we applied root mean square error (RMSE) and standard deviation criteria to select an appropriate model for the simulated data. Results indicate zero inflated Poisson mixed as an appropriate model for the data. In addition, compared to the other studies, our simulation study demonstrates an improvement in the average run length (ARL).

Due to the lack of measurements in many regions, wave characteristics are estimated using different methods. Wave climate hindcasting/forecasting is mostly conducted by numerical models or empirical methods. Until now, different empirical methods have been developed for wave hindcasting. However, with the development of high speed processors, several sophisticated numerical models have been developed for wave prediction. These models are mostly phase-averaged spectral wave models developed in three generations. In the last two decades, third generation wave models have been used widely in academic and practical projects. In this regard, Port and Maritime Organization has produced his own model, PMO Dynamic. This model has been developed as a part of first three phases of Monitoring and Modeling of Study of Iranian Coasts project. PMO Dynamic package is a software available for engineering purposes. It has several modules that have been developed for different objectives. Wave model is the module which is used for the generation and transformation of wind waves in coastal areas. In this paper, in order to test the PMO Dynamic model capabilities, it has been applied for the prediction of wave parameters in Bushehr Bay and the results have been compared with MIKE21 SW model and measured data.