This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Intelligent building (IB) technologies have widespread applications in the building design and development. In this regard, it is necessary to develop intelligent building assessment models in order to satisfy the clients, professionals, and occupants' growing demands. To this end, this paper proposes an integrated analytic hierarchy process (AHP) and preference degree approach (PDA) under the fuzzy environment for the purpose of intelligent building assessment. Fuzzy AHP is employed to determine the local weights of performance criteria and the final weights of the intelligent building alternatives. Since, the final weights of intelligent buildings (IBs) are in the form of fuzzy numbers, fuzzy PDA is utilized to prioritize the intelligent buildings. Finally, fuzzy AHP-fuzzy PDA is proposed to assess the performance of five intelligent building alternatives in Isfahan, Iran.

In order to facilitate processes and provide innovative services, Most of organizations pay special attention to using data center services to host new platforms, services and tools to manage and run their systems. In the field of data center cybersecurity, all aspects need to be considered to identify the causes and then solve the problems. Therefore, in this study, we seek to identify and provide management codes that consider all internal and external aspects of cyber security. In this research, using NIST cybersecurity framework and existing localized framework for Identify management codes of Comprehensive framework for data center cybersecurity by systematic literature review (SLR), 1831 documents was extracted by searching the Science direct, Scopus and ISC articles. After necessary examinations, 63 of them were identified as related documents. They were analyzed and coded using the Meta-synthesis method. Finally, the extracted features were classified into 5 main category and 23 sub categories and 108 codes, which were related to data center cyber security, i. e. Identification, protection, detection, response and retrieval.

The Special Issue of the Journal of Information Technology Management (JITM) is publishing very selective papers on information management, technology in higher education, integrated systems, enterprise management, cultural thoughts, strategic contributions, management information systems, and cloud computing. We received numerous papers for this special issue but after an extensive peer-review process, 10 papers were finally selected for publication. The current special issue consisted of areas viz Integrated Intelligent Systems, Analytical Systems, and Enterprise Management.

Education is a continually developing activity, it shapes the way an individual develops their attitudes, thoughts and behavior. Education is not just about being literate but it is an overall development of the person in every aspect of their lives. A society's educational system can be greatly linked to its culture as the culture that one is in have an affect on the type of curriculum the institutions will develop in order to cater to every individual of that particular culture. Education moulds and shapes a society and it is influenced by the culture of the particular country or society. The educational system acts as a point of reference for the society's needs and demands. The principles that dominate this article is culture and education are interrelated and interconnected. Every educational paradigm is influenced by the culture of the society in which it operates. This essay underlines the extensive connection between culture and education. As a result, the goal of this essay is to depict these features from a Philosophical-Psychological Approach.

Introduction: Sediment transportation and accurate estimation of its rate is a significant issue for river engineers and researchers. So far, various and complex relationships have been proposed to predict the amount of suspended sediment transport rate, such as velocity and critical shear stress based equations. However, the complex nature of sediment transport and lack of validated models make it difficult to model the suspended sediment concentration and suspended sediment discharge carried by rivers. Although the developed models led to promising results in sediment transport prediction, due to the importance of sediment transport and its impact on hydraulic structures it is necessary to use other methods with higher efficiency. On the other hand, in recent years, the Meta model approaches have been applied in investigating the hydraulic and hydrologic complex phenomena. Hybrid models involving signal decomposition have also been shown to be effective in improving the prediction accuracy of time series prediction methods, as indicated in. Complementary Ensemble Empirical Mode Decomposition analysis is one of the widely used signal decomposition methods for hydrological time series prediction. Decomposition of time series reduces the difficulty of forecasting, thereby improving forecasting accuracy. In this study, due to the complexity of the sediment and erosion phenomenon and the effect of different parameters in estimating, time series pre-processing methods along with support vector machine (SVM) and Gaussian process regression (GPR) kernel based approaches were used to estimate suspended sediment load of a natural river at two consecutive hydrometric stations. For this purpose, different models were defined based on hydraulic and sediment particles characteristics. Moreover, the capability of integrated pre-processing and postprocessing methods in two states of inter-station and between-stations was investigated. First, the Wavelet Transform (WT) method was used for data pre-processing then, the high-frequency sub-series were selected and re-decomposed using the Empirical Mode Decomposition (EMD). Finally, the most effective sub-series were imposed as inputs for kernel-based models. In addition, to assess the reliability of the superior model, Monte Carlo uncertainty analysis was used. The results showed that the GPR model had a desirable degree of uncertainty in modeling. Materials and Methods: In this study, data of two stations of Housatonic River was used. The distance between stations was approximately 50 km. The first station is located near Great Brighton, Massachusetts, and the second station is in Connecticut. The basin area for the stations is 282 and 634 square miles, respectively. The flow path is from the first station to the second station. SVM and GPR models are based on the assumption that adjacent observations should convey information about each other. Gaussian processes are a way of specifying a prior directly over function space. This is a natural generalization of the Gaussian distribution whose mean and covariance are a vector and matrix, respectively. Due to prior knowledge about the data and functional dependencies, no validation process is required for generalization, and GP regression models are able to understand the predictive distribution corresponding to the test input. Wavelet Transform (WT) uses a fl, exible window function (mother wavelet) in signal processing. The fl, exible window function can be changed over time according to the signal shape and compactness. After using WT, the signal will decompose into two approximations (largescale or low-frequency component) and detailed (small-scale component) components. EEMD was proposed to solve the mode mixing issue of empirical mode decomposition (EMD) which specifi, es the true IMF as the mean of an ensemble of trials. Each trial consists of the decomposition results of the signal plus a white noise of fi, nite amplitude. EMD can be used to decompose any complex signal into finite intrinsic mode functions and a residue, resulting in subtasks with simpler frequency components and stronger correlations that are easier to analyze and forecast. Another important feature of empirical model of decomposition is that it can be used for noise reduction of noisy time series, which can be effective in improving the accuracy of model predictions. In the uncertainty analysis method, two elements are used to test the robustness and to analyze the models uncertainty. The first one is the percentage of the studied outputs which are in the range of 95PPU and the next one is the average distance between the upper (XU) and lower (XL) uncertainty bands. In this regard, the considered model should be run many times (1000 times in this study), and the empirical cumulative distribution probability of the models be calculated. The upper and lower bands are considered 2. 5% and 97. 5% probabilities of the cumulative distribution, respectively. Results and Discussion: In order to evaluate and review the performance of the tested models and determine the accuracy of the selected models, three performance criteria named Correlation Coefficient (CC), Determination Coefficient (DC), and Root Mean Square Errors (RSME) were used. The obtained results indicated that the accuracy of the applied integrated models was higher than the single SVM and GPR models. The use of integrated methods decreased the error criteria between 20 to 25 %. The obtained results for the uncertainty analysis showed that in suspended sediment load modeling the observed and predicted values were within the 95 PPU band in most of the cases. Moreover, it was found that the amount of d-Factors for train and test datasets were smaller than the standard deviation of the observed data. Therefore, based on the results, it could be induced that the suspended sediment modeling via integrated WT-EEMD-GPR model led to an allowable degree of uncertainty. Conclusion: Comparison of the developed models’,accuracy revealed that integrated GPR and SVM models had higher performance compared with single GPR and SVM models in predicting the suspended sediment discharge. The use of these two methods approximately decreased the error criteria between 20 to 25 %. According to the results, for the models that were developed based on the station data, the model with the input parameters of Dwt, Dwt-1, and Dst-1 and in the case of investigating the relationship between the stations, the model with the input parameters of Dst-2, Dwt-1, and Dst-1 were superior models. Also, based on the uncertainty analysis, the integrated GPR model had an allowable degree of uncertainty in suspended sediment modeling. However, it should be noted that the used methods are data sensitive models. Therefore, further studies using data ranges out of this study and field data should be carried out to determine the merits of the models to estimate suspended sediment load in the real conditions of flow.