In this paper, it is supposed that a project can be shown as a metagraph. The duration of activities are discrete Random variables with known probability function, also one kind of consumable and constrained resource is required to execute each activity of the project. Besides, the probability functions of activity durations depend on the amount of the resource allocated to it. Clearly, the amount of resource which can be allocated to each activity is limited to speci c values. In this paper, it is assumed that the due date of project is known and de nite. The objective of this paper is to maximize the probability of completion of stochastic metagraph before the due date of the project. As respects, solving the problem by using the analytical method is very challenging; therefore, we developed a new heuristic method including two sub-algorithms for solving the problem. Twenty examples were designed for examining the proposed algorithm.

In this paper, a location allocation (LA) problem in construction and demolition (C & D) waste management (WM) is studied. A bi-level model for this problem under a fuzzy Random environment is presented where the upper level is the governments who sets up the processing centers, and the lower level are the administrators of different construction projects who control C & D waste and the after treatment materials supply. This model using an improved particle swarm optimization program based on a fuzzy Random simulation (IPSO-based FRS) is able to handle practical issues. A case study is presented to illustrate the effectiveness of the proposed approach. Conclusions and future research directions are discussed.

Balancing between demand and supply of grids is the most important task of the power systems operators and control systems. Otherwise, the possibility of frequency instability and severe damages to equipment are present. Primary frequency control (PFC) is the first and main control action in the grid in front of the active power imbalance disturbances. In this paper, the effects of the spinning reserve characteristics and the grid dynamic parameters, on PFC performance and maximum frequency decline (frequency nadir), are investigated. Then, a comprehensive equation is presented to determine the maximum frequency deviation after a large power imbalance in the grid. This equation considers all effective factors such as volume and speed of the primary frequency reserve (PFR), grid inertia constant, grid load level, and the frequency-dependent loads. The correctness of the presented equation is verified through different simulations. Finally, a comprehensive scheme is proposed for the primary frequency control reserve allocation in the grid, in the form of a few equations and instructions.

Time-frequency filtering is an acceptable technique for attenuating noise in 2-D (time-space) and 3-D (time-space-space) reflection seismic data. The common approach for this purpose is transforming each seismic signal from 1-D time domain to a 2-D time-frequency domain and then denoising the signal by a designed filter and finally transforming back the filtered signal to original time domain. The technique is efficient for ground roll and also Random noise attenuation. However, if we deal with a large data set and a great number of contaminated signals with ground roll noise, a much move consuming time will be required. In this paper, time-frequency filtering is formulated and carried out by a different approach. The data is transformed from original timespace domain into several single-frequency time-space domains, and the filters to reduce noise is designed in the new domains. The transform is easily and completely invertible. The employed time frequency analysis method is a high-resolution version of S-transform. Application to synthetic and real shot gathers confirms the good performance and efficiency of the method for attenuating ground roll noise and Random noise.

Purpose: Perceptual decision-making is the act of choosing one option from a set of alternatives based on available sensory information. Regarding the serious role of this act in human personal and social lives, the neurophysiological analysis of the brain during this type of decisions is of great interest. In this research, the underlying neural mechanism of these decisions is investigated using a perceptual decision-making Electroencephalogram (EEG) dataset with a perceptual discrimination task. Materials and Methods: An online available dataset containing the pre-processed EEG signals of 24 healthy participants during the perceptual decision-making task of Random Dot Kinematograms was used. After a secondary pre-processing stage, clean EEG signal was divided into 1. 3-second segments and averaged for Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP) calculations. The task engagement index was also calculated and averaged among all participants. Results: According to the results, the amplitude of the N200 component in O1 and O2 channels was larger for correct choices than incorrect ones. Furthermore, in the O2 channel, it was observed that the average alpha power near 200 milli-seconds after stimulus onset was slightly higher in high and low confidence choices than medium confidence choices. The beta band power in the PO2 channel was also higher for correct choices rather than incorrect ones in this interval. Moreover, the results represented that the task engagement index was higher in medium confidence choices, especially in occipital and parieto-occipital channels. Conclusion: The larger N200 amplitude and the higher beta power for correct choices, and the lower alpha power for medium confidence choices may be due to more attention of the individuals to the stimuli. This phenomenon can be observed in the task engagement indices as well. This could be because the user expended more effort in medium confidence to bring one of the choices to the decision threshold.