Multisource Spatial Data Integration for Use Cases Applications

Big opportunities are given by the reuse and integration of data, which, nowadays, are more and more available, thanks to advances in acquisition and modelling technologies and the open data paradigm. Seamlessly integrating data from heterogenous data sources has been an interest of the geospatial community for long time. However, the higher semantic and geometrical complexity pose new challenges which have never been tackled in a comprehensive methodology. Building on the previous theories and studies, this paper proposes an overarching methodology for multisource spatial data integration. Starting from the definition of the use case-based requirements for the integrated data, it proposes a framework to analyse the involved datasets with respect to integrability and suggests actions to harmonise them towards the destination model. The overall workflow is explained, including the data merging phase and validation. The methodology is tested and exemplified on a case study. Considering the specific data sets’ features and parameters, this approach will allow the development of consistent, well documented and inclusive data integration workflows, for the sake of use cases processes automation and the production of Interoperable and Reusable data.

Quantifying Conventional Electroencephalogram Recordings and Examining its Output Computation with a Quantitative Electroencephalogram

Quantitative electroencephalogram enables mathematical analysis of neurological recordings while conventional electroencephalogram lacks the mathematical output; hence, its usage is limited to neurological experts. This study was to determine if quantified conventional electroencephalogram recordings were compatible and comparable with quantitative electroencephalogram recordings. A group of post-call doctors was recruited and subjected to an EEG recording using a conventional electroencephalogram followed by a quantitative electroencephalogram device. The patterns and quantified recording results were compared. A comparative analysis of the two recording sets did not find differences in the recording patterns and statistical analysis. The findings promoted the use of a readily available conventional electroencephalogram in quantitative brain wave studies and have cleared potential compatibility bias towards data merging.

A Robust Lightweight Data Security Model for Cloud Data Access and Storage

In this paper, an efficient lightweight cloud-based data security model (LCDS) is proposed for building a secured cloud database with the assistance of intelligent rules, data storage, information collection, and security techniques. The major intention of this study is to introduce a new encryption algorithm to secure intellectual data, proposing a new data aggregation algorithm for effective data storage and improved security, developing an intelligent data merging algorithm for accessing encrypted and original datasets. The major benefit of the proposed model is that it is fast in the encryption process at the time of data storage and reduced decryption time during data retrieval. In this work, the authors proposed an enhanced version of the hybrid crypto algorithm (HCA) for cloud data access and storage. The proposed system provides secured storage for storing data within the cloud.

Going Feminine: Identifying How and When Female Candidates Emphasize Feminine and Masculine Traits on the Campaign Trail

Female candidates face a messaging challenge. There is a strong association between masculinity and political leadership. Stressing masculinity can result in a likability backlash for female candidates often seen as lacking feminine qualities, such as warmth. Preventing a likability backlash by highlighting feminine qualities can also harm female candidates. Current scholarship offers conflicting conclusions about how female candidates balance these gendered challenges. We fill this empirical and theoretical gap with a trait-balancing theory clarifying how and when female candidates use feminine and masculine traits to manage competing expectations. We use original data merging information on candidate advertising strategies across three election cycles. We show that female candidates strategically balance masculine and feminine stereotypes in ways that often differ from their male counterparts but also differ based on female candidate partisanship and incumbency. These results are consequential because they highlight how female candidates manage gendered pressures in campaign strategies, which can affect their ability to win elections and, ultimately, women’s representation in government.