Abstract
The smart grid is a fully automatic delivery grid for electricity power with a two-way reliable flow of electricity and information among different equipment on the grid. Smart meters and sensors monitoring the system provide a huge amount of data in various part of smart grid. To logically manage this trouble, a new lossy data compression approach for big data compression is proposed. The optimal singular value decomposition (SVD) is applied to a matrix that achieves the optimal number of singular values to the sending process, and the other ones will be neglected. This goal is done due to the quality of retrieved data and the compression ratio. In the presented scheme, to implement the optimization framework, various intelligent optimization methods are used to determine the number of optimal values in the elimination stage. The efficiency and capabilities of the proposed method are examined using a wide range of data types, from electricity market data to image processing benchmarks. The comparisons show that the compression level obtained by the proposed method can dominate the points given by the existing SVD rank reduction methods. Also, as the other finding of this article, the performance of the rank reduction methods depends on the application and data types. It means that a rank reduction method can reveal a good performance in one application and performs unacceptably for another purpose. So, the optimized rank reduction can pave the way toward a robust and reliable performance.

Abstract
This study aims to address an emerging area of application of agile methodologies outside the traditional technological environment, particularly outside the context of software engineering. In this sense, it intends to understand and contextualise the problems and challenges that are posed by traditional methodologies and explore how agile methodologies can complement or substitute to traditional methodologies in non-technological environments. For this purpose, a qualitative study was performed through three case studies operating in different sectors of activity like digital marketing, management consulting, and tourism. The findings indicate that both traditional and agile methodologies can coexist, and it is common to find hybrid methodologies that are adopted and customised according to the needs of each project and client. Furthermore, it was possible to conclude that following agile practices that make sense to the cultural values of the organisation is more important than adopting a specific agile methodology. Copyright © 2021 Inderscience Enterprises Ltd.

Abstract
We carry out the first provable security analysis of the new FIDO2 protocols, the promising FIDO Alliance's proposal for a standard for passwordless user authentication. Our analysis covers the core components of FIDO2: the W3C's Web Authentication (WebAuthn) specification and the new Client-to-Authenticator Protocol (CTAP2). Our analysis is modular. For WebAuthn and CTAP2, in turn, we propose appropriate security models that aim to capture their intended security goals and use the models to analyze their security. First, our proof confirms the authentication security of WebAuthn. Then, we show CTAP2 can only be proved secure in a weak sense; meanwhile, we identify a series of its design flaws and provide suggestions for improvement. To withstand stronger yet realistic adversaries, we propose a generic protocol called sPACA and prove its strong security; with proper instantiations, sPACA is also more efficient than CTAP2. Finally, we analyze the overall security guarantees provided by FIDO2 and WebAuthn+sPACA based on the security of their components. We expect that our models and provable security results will help clarify the security guarantees of the FIDO2 protocols. In addition, we advocate the adoption of our sPACA protocol as a substitute for CTAP2 for both stronger security and better performance.

Abstract
A B S T R A C T Power Distribution Systems usually rely on closed and fixed communication networks due to the strict requirements they must comply with. With the appearance of new communication technologies that can contribute to the assurance of those requirements (for example, 5G), open networks can be used for such systems, decreasing the overall cost of maintaining and upgrading the communication network. Although, shifting from closed communication environments to networks integrated with the Internet using 5G communication environments can expose these systems to severe threats, since they were developed to operate under closed networks not addressing security by default. This paper analyses the security requirements for Power Distribution Systems operating on open networks, identifying the gap between such systems and the existing security mechanisms. From this analysis, we present a solution based on low cost off-the-shelf hardware, composed by a security library and a bridging device, intended to act as a security gateway for Intelligent Electronic Devices (IEDs) in Power Distribution Systems. We also evaluate the functionality of our security gateway, and analyse its impact on the stringent performance requirements of such systems.

Abstract
Cognitive health status is a determining factor for quality of life. Since early identification of mild cognitive impairment is critical to better manage further decline, and to guide therapeutic and rehabilitation interventions, there is the need for efficient clinical instruments able to monitor the individuals, particularly older adults, in their residential environments. This paper presents a scoping review to identify of innovative digital solutions to detect cognitive impairments and that might be used as a screening tool for age related cognitive impairment of community-dwelling older adults. Nineteen articles were included in this scoping review. As a main conclusion, most of the included articles report digital solutions to support the application of existing paper-based clinical instruments to assess specific or multiple cognitive functions. However, six studies explore new approaches to assess cognitive functions including virtual reality and serious games.

Abstract
With the significant increase in wind speed usage as a clean source of energy, an accurate wind speed forecasting system is a must for more effective utilization of this energy. Failure to consider the inherent spatio-temporal features of wind speed time series leads to the lack of generalization capacity for current wind speed forecasting approaches. This paper proposes an end-to-end deep neural network framework, i.e., convolutional rough long short-term memory (ConvRLSTM), to extract spatio-temporal wind correlations and mitigate the inherent uncertainties in wind time series by incorporating the Rough set theory into a combination of convolution neural network (CNN) and LSTM units. Our proposed model receives the historical data of wind speed for a 20x20 array of wind turbines in North Carolina, US. Several ConvRLSTM layers extract the most relevant features for the forecasting task, and finally, fully connected layers predict 400 wind speed values using the spatial features obtained by the CNN and temporal features computed by the LSTM. Through analyzing the numerical forecasting results, it can be inferred that the proposed approach outperforms the mainstream and recently published forecasting strategies in terms of the RMSE metric.