The design of an observer-based robust tracking controller is investigated and successfully applied to control an Activated Sludge Process (ASP) in this study. To this end, the Takagi-Sugeno (TS) fuzzy modeling is used to describe the dynamics of a nonlinear system with disturbance. Since the states of the system are not fully available, a fuzzy observer is designed. Based on the observed states and a reference state model, a reduced fuzzy controller for trajectory tracking purposes is then proposed. While the controller and the observer are developed, the design goal is to achieve the convergence and a guaranteed H ∞ performance. By using Lyapunov and H ∞ theories, sufficient conditions for synthesis of a fuzzy observer and a fuzzy controller for TS fuzzy systems are derived. Using some special manipulations, these conditions are reformulated in terms of linear matrix inequalities (LMIs) problem. Finally, the robust and effective tracking performance of the proposed controller is tested through simulations to control the dissolved oxygen and the substrate concentrations in an activated sludge process.Access control is a critical aspect for improving the privacy and security of IoT systems. A consortium is a public or private association or a group of two or more institutes, businesses, and companies that collaborate to achieve common goals or form a resource pool to enable the sharing economy aspect. However, most access control methods are based on centralized solutions, which may lead to problems like data leakage and single-point failure. Blockchain technology has its intrinsic feature of distribution, which can be used to tackle the centralized problem of traditional access control schemes. Nevertheless, blockchain itself comes with certain limitations like the lack of scalability and poor performance. To bridge the gap of these problems, here we present a decentralized capability-based access control architecture designed for IoT consortium networks named IoT-CCAC. A blockchain-based database is utilized in our solution for better performance since it exhibits favorable features of both blockchain and conventional databases. The performance of IoT-CCAC is evaluated to demonstrate the superiority of our proposed architecture. IoT-CCAC is a secure, salable, effective solution that meets the enterprise and business's needs and adaptable for different IoT interoperability scenarios.In this work, a novel fuzzy decision making technique namely trapezoidal fuzzy Best-Worst method (fuzzy BWM) is developed which is based on Best-Worst method (BWM) and Trapezoidal fuzzy number. The real motive behind our work is to take a broad view of the existing fuzzy BWM based on triangular fuzzy number by trapezoidal fuzzy number. Also, we have presented a new hybrid MCDM technique called as Trapezoidal fuzzy Best Worst Analytic Hierarchy based on proposed trapezoidal fuzzy BWM and existing trapezoidal fuzzy Analytic Hierarchy Process (AHP). BWM approach is employed in evaluating the PV of considering criteria and trapezoidal fuzzy AHP is used to assess the local priority vale (PV) of considering alternatives (or indicators) of a decision problem. Moreover it used to identify the most significant alternative which is responsible for performance efficiency of a hydro power plant under climatic scenario. From the result, it is undoubtedly found that hydraulic had is most responsible indicator. Further, the CR (consistency ratio) value which is determined by our proposed trapezoidal fuzzy BWM is less than that of existing BWM and fuzzy BWM techniques. Finally, we have validated our result by comparative study, scenario analysis and sensitivity analysis.In recent years, Deep Learning techniques applied to steganalysis have surpassed the traditional two-stage approach by unifying feature extraction and classification in a single model, the Convolutional Neural Network (CNN). Several CNN architectures have been proposed to solve this task, improving steganographic images' detection accuracy, but it is unclear which computational elements are relevant. Here we present a strategy to improve accuracy, convergence, and stability during training. The strategy involves a preprocessing stage with Spatial Rich Models filters, Spatial Dropout, Absolute Value layer, and Batch Normalization. Using the strategy improves the performance of three steganalysis CNNs and two image classification CNNs by enhancing the accuracy from 2% up to 10% while reducing the training time to less than 6 h and improving the networks' stability.
  Results of scientific experiments and research work, either conducted by individuals or organizations, are published and shared with scientific community in different types of scientific publications such as books, chapters, journals, articles, reference works and reference works entries. One aspect of these documents is their contents and the other is metadata. Metadata of scientific documents could be used to increase mutual cooperation, find people with common interest and research work, and to find scientific documents in the matching domains. The major issue in getting these benefits from metadata of scientific publications is availability of these data in unstructured (or semi-structured) format so that it can not be used to ask smart queries that can help in computing and performing different types of analysis on scientific publications data. Also, acquisition and smart processing of publications data is a complicated as well as time and resource consuming task.
 
  To address this problem we have deved perform different types of analysis on scientific publications data.
 The resulting datasets can be used to make smart queries by making use of SPARQL protocol.  https://www.selleckchem.com/products/ginkgolic-acid-s9432.html  We also present the quantitative as well as qualitative analysis of our resulting datasets which ultimately can be used to compute the research behavior of organizations in rapidly growing knowledge society. Finally, we present the potential usage of producing and processing such open data of scientific publications and how results of performing smart queries on resulting open datasets can be used to compute the impact and perform different types of analysis on scientific publications data.