Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation

Large and complex data becomes a valuable resource in biomedical discovery, which is highly facilitated to increase the scientific resources for retrieving the helpful information. However, indexing and retrieving the patient information from the disparate source of big data is challenging in biomedical research. Indexing and retrieving the patient information from big data is performed using the MapReduce framework. In this research, the indexing and retrieval of information are performed using the proposed Jaya-Sine Cosine Algorithm (Jaya–SCA)-based MapReduce framework. Initially, the input big data is forwarded to the mapper randomly. The average of each mapper data is calculated, and these data are forwarded to the reducer, where the representative data are stored. For each user query, the input query is matched with the reducer, and thereby, it switches over to the mapper for retrieving the matched best result. The bilevel matching is performed while retrieving the data from the mapper based on the distance between the query. The similarity measure is computed based on the parametric-enabled similarity measure (PESM), cosine similarity and the proposed Jaya–SCA, which is the integration of the Jaya algorithm and the SCA. Moreover, the proposed Jaya–SCA algorithm attained the maximum value of F-measure, recall and precision of 0.5323, 0.4400 and 0.6867, respectively, using the StatLog Heart Disease dataset.

Maya and Mia At La La Land

Jenny Oyallon-Koloski’s Maya and Mia At La La Land, is a videographic film which draws fascinating parallels between Maya Deren’s At Land and Damien Chazelle’s La La Land. By bringing together two such starkly disparate source materials – experimental film vs popular musical – this practice research presents a compelling exploration of what Chazelle identifies as the inherently “radical nature” of the musical. Oyallon-Koloski uses various editing techniques from continuity editing and Soviet montage, to Deren’s own parametric framework, to interweave the films and make abstract narrative connections between their two central female characters. In so doing, she not only critiques the on-screen representation of women, but also draws attention to the stylistic synergies between the two filmmakers.

Water governance challenges presented by nanotechnologies: tracking, identifying and quantifying nanomaterials (the ultimate disparate source) in our waterways

Nanotechnologies are considered an enabling technology, as they enhance the functioning of a wide range of products and processes. They are increasingly appearing in consumer products, including sun creams, socks and outdoor paints, resulting in the potential for direct access of nanomaterials (NMs) into wastewater and the environment. As such, they could be considered as the ultimate disparate source, with multiple products and multiple routes into the environment, as well as numerous transformation pathways, such that the final form may bear little resemblance to the initially produced form. NMs thus represent a significant governance and regulatory challenge, for a number of reasons, related to their small size, which makes detection challenging, especially against a background of naturally occurring nanoscale entities (clay and sediment particles, etc.), and their large surface area and high surface energy which leads to very dynamic behaviour and a strong tendency to interact with (bind) anything they come into contact with. Some initial considerations of regulatory issues related to Registration, Evaluation and Authorization of Chemicals, the Water Framework Directive, and the potential for benign-by-design approaches exploiting the potential for recovery or recycling of NMs at the design phase are presented, aimed at reducing the risk of unintentional accumulation of NMs in our waterways.