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Holographic Data Storage: From Theory to Practical Systems is a primer on the design and building of a holographic data storage system covering the physics, Servo, Data Channel, Recording Materials, and optics behind holographic storage, the requirements of a functioning system, and its integration into \"real-life\" systems.

Multiferroic materials have grabbed great attention of researchers due to their distinctive feature of magnetoelectric coupling with vast applicability in advanced multifunctional devices. To achieve considerable value of magnetoelectric coupling, the samples of La-doped BiFeO 3 and MgFe 2 O 4 , which were initially synthesized via hydrothermal method, were then embedded into the (1– x )Bi 0.9 La 0.1 FeO 3  +  x MgFe 2 O 4 composites using a ball-milling process. The presence of rhombohedrally distorted cubic perovskite structure of La-doped BiFeO 3 having R3c space group symmetry and spinel cubic structure of MgFe 2 O 4 with Fd-3m space group symmetry was confirmed using X-ray diffraction analysis. The microscopic images of the composite samples show a slight variation in grain size with least porosity observed for the composite of x  = 0.5. The elemental mapping assured the presence of all elements in the prepared composites that were in accordance with the stoichiometric ratios. The ferroelectric analysis exposed that the x  = 0.2 composition had shown the highest efficiency of 52% for energy storage devices. The linear magnetoelectric response of the composite samples along with small values of switching charge density observed at x  = 0.3 inferred this particular composite quite preferable for data storage applications.

This article explores the performance optimizations of an embedded database memory management system to ensure high responsiveness of real-time healthcare data frameworks. SQLite is a popular embedded database engine extensively used in medical and healthcare data storage systems. However, SQLite is essentially built around lightweight applications in mobile devices, and it significantly deteriorates when a large transaction is issued such as high resolution medical images or massive health dataset, which is unlikely to occur in embedded systems but is quite common in other systems. Such transactions do not fit in the in-memory buffer of SQLite, and SQLite enforces memory reclamation as they are processed. The problem is that the current SQLite buffer management scheme does not effectively manage these cases, and the naïve reclamation scheme used significantly increases the user-perceived latency. Motivated by this limitation, this paper identifies the causes of high latency during processing of a large transaction, and overcomes the limitation via proactive and coarse-grained memory cleaning in SQLite.The proposed memory reclamation scheme was implemented in SQLite 3.29, and measurement studies with a prototype implementation demonstrated that the SQLite operation latency decreases by 13% on an average and up to 17.3% with our memory reclamation scheme as compared to that of the original version.