To forecast the electricity load of a city or country and facilitate the strategic decision-making, it is common to collect the historical data from different zones of the city or different cities of the country. However, normally all the zones or different sectors’ load (residential, industrial, and commercial) are not important equally. In other words, a certain zone or a sector may have the most effect on decision making. Therefore, the simple algebraic sum of the different zones’ Forecasting may not be meaningful for the ultimate objective. There are different methods for aggregation of the different zones’ forecasts. The most convenient method is the simple algebraic sum of the different zones’ forecasts, which is not only inefficient but also needs more details about the effective factors on the electricity demand in each zone. In this paper, different aggregation approaches such as bottom-up, top-down, optimal combination methods are presented. It should be mentioned that any research paper in the field of the electrical power system and load Forecasting have not studied the Hierarchical Forecasting; therefore, presenting the Hierarchical method for load Forecasting is a strict innovation of this paper. The Auto-Regressive Integrated Moving Average (ARIMA) and Exponential Smoothing methods are embedded in proposed aggregation approaches. The proposed methods are applied to forecast Australian electric load in short-term and long-term horizons.

This article presents a new method for detecting heterogeneities in wind data set to predict wind speed based on the well-known Hidden Markov Model (HMM). In the proposed method, the HMM categorizes the wind time series into some groups in which each group represents a wind regime. Each regime uses an internal first-order Markov Chain (MC) for Forecasting, and the combination of all regimes outputs generates the final wind speed forecast. The model proposed in this study is called “Hierarchical Markov Model ”. The first layer detects and separates wind regimes as heterogenic groups of wind data by the use of wind direction data, based on  HMM, and the second layer forecasts the wind speed using MC. The proposed model is implemented and tested using real data. Its effectiveness in terms of temporal stationary index is compared with that of a first-order MC-based method. The results showed that more than 70% improvement can be achieved in wind speed prediction by the proposed method. Moreover, it gives a probability distribution function of wind speed prediction, which is sharper than the one obtained with the first-order MC; means that more precise prediction

In this paper, conventional SAPO-34 and Cerium Hierarchical SAPO-34 zeolites are synthesized using a hydrothermal method for methanol-to-propylene (MTP) reaction. The Ce-H-SAPO-34 catalyst shows a slightly larger crystal size, bimodal pore size distribution (microporous and mesoporous) and lower surface acidity compared to conventional SAPO-34 catalysts. According to these physicochemical properties and the previously suggested reaction mechanisms on the conventional SAPO-34 catalyst, a new reaction network is introduced for Ce-H-SAPO-34 catalyst. This was based on the data collected from a catalytic micro reactor in the temperature range of 390-450° C and at atmospheric pressure. The lumped kinetic model and the reaction rate expression have been introduced by considering the reaction network. The parameters were then estimated based on the experimental data using genetic algorithms. Comparing the experiments with the predicted data suggests a good correlation between the experimental data and the model.

In this paper, a new interconnection network structure called Hierarchical extended Fibonacci cubes interconnection networks HEFC1(n) is proposed, and its properties are evaluated. A set of the HEFC1(n)s constructed by the proposed method is a two level conventional Hierarchical network. For each n (dimension of extended Fibonacci cube-EFC1(n)), a HEFC1(n)   interconnection network can be constructed. All EFC1(n) for n>4 are recursively constructible, hence HEFC1(n)s for n>4 are also recursively constructible with some extra EFC1(n-1)s and EFC1(n-2)s. Furthermore, since a HEFC1(n) has a Hierarchically structured character and the feature of uniformity, a wide variety of inter-cluster connections are possible. The comparisons of HEFC1(n)s with some of the traditional cubic and Hierarchical cubic networks are presented in this study. The routing in HEFC1(n+2)s is as easy as routing in HCN(n,n) and the scalability of HEFC1(n+2) is better than the scalability of HCN(n,n) and H(2n). H(2n) and HCN(n,n) are symmetric interconnection networks, however, HEFC1(n+2) is not a symmetric interconnection network and HEFC1(n+2)s have a recurrent structure as H(2n) and HCN(n,n). The cost of HEFC1(n+2) is better than the costs of HCN(n,n) and H(2n).The edge connectivity of HEFC1(n+2) is worse than the edge connectivity of H(2n) and HCN(n,n). This makes the VLSI design of HEFC1(n+2) simpler than the VLSI designs of H(2n) and HCN(n,n).

In a Fuzzy Identity-Based Encryption (FIBE) scheme, a user with the private key for an identity ID is able to decrypt a ciphertext encrypted with another identity ID if and only if ID and ID' are within a certain distance of each other as judged by some metric. The existing literature on FIBE assumes that all attributes are equally important. However, this assumption may not be appropriate in some situations. In this paper, we consider the problem of FIBE with attributes of different importance level. In this setting, the set of possible attributes admits a Hierarchical structure such that, during decryption process, attributes in lower levels can be replaced by those in higher levels. To solve this case, a new scheme called Hierarchical Fuzzy Identity-Based Encryption (HFIBE) was introduced then it was provided with a formal definition of security and an implementation method. The security of our proposed scheme is in the Selective-ID security model under the Decisional Modified Bilinear Diffe-Hellman assumption.

In world economy, crude oil is considered as one of the most strategic commodities playing a vital role in the determination of many regional and global equations. So, it is well known that an intense fluctuation of the oil price causes large recession in OPEC countries. So many researchers attempt to forecast crude oil price while oil market is one of the most complex, turbulent and chaotic international financial markets. In present research, gray system theory is utilized to model and forecast the price of crude oil. The results represent that gray Forecasting model significantly improves the accuracy of the Forecasting operation.

The Hierarchical product of graphs is a variant of the Cartesian product. It is associative, not commutative, and finite connected graphs have unique first prime factors with respect to it. We present examples of infinite graphs with different first prime factors, and show that homogeneous trees of finite degree have unique prime factoriza-tions with respect to the Hierarchical product. On the way, we pose two problems.