a. a. saberi

Energy production and consumption play an important role in the domestic and international strategic decisions globally. Monitoring the electric energy consumption is essential for the short- and long-term of sustainable development planned in different countries. One of the advanced methods and/or algorithms applied in this prediction is the meta-heuristic algorithm. The meta-heuristic algorithms can minimize the errors and standard deviations in the data processing. Statistically, there are numerous methods applicable in the uncertainty analysis and in realizing the errors in the datasets, if any. In this article, the Mean Absolute Percentage Error (MAPE) is used in the error’s minimization within the relevant algorithms, and the used dataset is actually relating to the past fifty years, say from 1972 to 2021. For this purpose, the three algorithms such as the Imputation–Regularized Optimization (IRO), Colliding Bodies Optimization (CBO), and Enhanced Colliding Bodies Optimization (ECBO) have been used. Each one of the algorithms has been implemented for the two linear and exponential models. Among this combination of the six models, the linear model of the ECBO meta-heuristic algorithm has yielded the least error. The magnitude of this error is about 3.7%. The predicted energy consumption with the winning model planned for the year 2030 is about 459 terawatt-hours. The important socio-economical parameters are used in predicting the energy consumption, where these parameters include the electricity price, Gross Domestic Product (GDP), previous year's consumption, and also the population. Application of the meta-heuristic algorithms could help the electricity generation industries to calculate the energy consumption of the approaching years with the least error. Researchers should use various algorithms to minimize this error and make the more realistic prediction.

Optimization has always been a human concern from ancient times to the present day, also in light of advances in computing equipment and systems, optimization techniques have become increasingly important in different applications. The role of metaheuristic algorithms in optimizing and solving engineering problems is expanding every day, optimization has also had many applications in water engineering. Every year, the effects of climate change and the water crisis deepen and worsen in many parts of the world, and existing water management becomes much more vital and critical. One of the main centers for water management and control dams reservoirs. In this paper, applying the CBO metaheuristic algorithm, the results of optimization in the operation of the Haraz dam reservoir in northern Iran, which has previously been done with FA and GA algorithms and standard operation system (SOP), are reviewed and compared. With the implementation of the CBO algorithm, all results and key outputs such as program runtime, annual water shortages, and vulnerabilities are much better than previous calculations, all the results are mentioned in the text of the article, but for example, the annual water shortage has reached about 38% of the FA algorithm, about 25% of the GA algorithm and about 13% of the SOP method. The numerical results demonstrate that the CBO algorithm has merits in solving challenging optimization problems and using this innovative algorithm can be an important starting point in the operation of dam reservoirs around the world.

We develop a model to study how invasion front depends on the relevant properties of a cellular environment. To do so, we use a nonlinear reaction-diffusion equation, the Fisher equation, to model the population dynamics. Our study is intended to understand how heterogeneity in the cellular environment's stiffness, as well as spatial correlations in its morphology,  given that the existence of both has been demonstrated by experiments, affects the properties of  the invasion front. It is demonstrated that three important factors affect the properties of the front; these are  the spatial distribution of the local diffusion coefficients, the correlations between them, and R/D, the ratio of  the cells' duplication rate R to  the cells' average diffusion coefficient D. Analyzing the scaling properties of  the Fisher equation invasion front, we show that , contrary to several previous claims, invasion fronts, including those of tumors and cancerous cells colonies, cannot be described by the well-known model of kinetic growth, such as the Kardar-Parisi-Zhang equation.

Collagen fibers can both inhibit and promote cellular migration, based on their orientation. Physical models such as Random walk can regenerate cellular migration. As such, we proposed a model based on (biased) random walk to study migration on collagen fibers. We took mean squared displacement as the determinant factor for metastatic risk and found that direction of fibers, spatial correlations and migration mode together regulate the risk and we proposed an approach to quantify an existing prognostic approach.