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Polylactide (PLA) is among the most common biodegradable polymers, with applications in various fields, such as renewable and biomedical industries. PLA features poly(D-lactic acid) (PDLA) and poly(L-lactic acid) (PLLA) enantiomers, which form stereocomplex crystals through racemic blending. PLA emerged as a promising material owing to its sustainable, eco-friendly, and fully biodegradable properties. Nevertheless, PLA still has a low applicability for drug delivery as a carrier and scaffold. Stereocomplex PLA (sc-PLA) exhibits substantially improved mechanical and physical strength compared to the homopolymer, overcoming these limitations. Recently, numerous studies have reported the use of sc-PLA as a drug carrier through encapsulation of various drugs, proteins, and secondary molecules by various processes including micelle formation, self-assembly, emulsion, and inkjet printing. However, concerns such as low loading capacity, weak stability of hydrophilic contents, and non-sustainable release behavior remain. This review focuses on various strategies to overcome the current challenges of sc-PLA in drug delivery systems and biomedical applications in three critical fields, namely anti-cancer therapy, tissue engineering, and anti-microbial activity. Furthermore, the excellent potential of sc-PLA as a next-generation polymeric material is discussed.

This paper presents a novel topology of dual airgap radial flux permanent magnet vernier machine (PMVM) in order to obtain a higher torque per magnet volume and similar average torque compared to a conventional PMVM machine. The proposed machine contains two stators and a sandwiched yokeless rotor. The yokeless rotor helps to reduce the magnet volume by providing an effective flux linkage in the stator windings. This effective flux linkage improved the average torque of the proposed machine. The competitiveness of the proposed vernier machine was validated using 2D finite element analysis under the same machine volume as that of conventional vernier machine. Moreover, cogging torque, torque ripples, torque density, losses, and efficiency performances also favored the proposed topology.

As renewable energy source integration increases, P2G technology that can store surplus renewable power as methane is expected to expand. The development of a CO2 methanation catalyst, one of the core processes of the P2G concept, is being actively conducted. In this work, low-rank coal (LRC) was used as a catalyst support for CO2 methanation, as it can potentially enhance the diffusion and adsorption behavior by easily controlling the pore structure and composition. It can also improve the process efficiency owing to its simplicity (no pre-reduction step) and high thermal conductivity, compared to conventional metal oxide-supported catalysts. A screening of single metals (Ni, Co, Ru, Rh, and Pd) on LRC was performed, which showed that Ni was the most active. When Ni on the LRC catalyst was doped with a promoter (Ce and Mg), the CO2 conversion percentage increased by >10% compared to that of the single Ni catalyst. When the CO2 methanation activity was compared at 250–500 °C, the Ce-doped Ni/Eco and Mg-doped Ni/Eco catalysts showed similar or better activity than the commercial metal oxide-supported catalyst. In addition, the catalytic performance remained stable even after the test for an extended time (~200 h). The results of XRD, TEM, and TPR showed that highly efficient LRC-based CO2 methanation catalysts can be made when the metal dispersion and composition are modified.

Advanced smart grid technologies enable energy prosumers to trade surplus energy from their distributed renewable energy sources with other peer prosumers through peer-to-peer (P2P) energy trading. In many previous works, P2P energy trading was facilitated by blockchain technology through blockchain’s distributive nature and capacity to run smart contracts. However, the feature that all the data and transactions on a blockchain are visible to all blockchain nodes may significantly threaten the privacy of the parties participating in P2P energy trading. There are many previous works that have attempted to mitigate this problem. However, all these works focused on the anonymity of participants but did not protect the data and transactions. To address this issue, we propose a P2P energy trading system on a blockchain where all bids are encrypted and peer matching is performed on the encrypted bids by a functional encryption-based smart contract. The system guarantees that the information encoded in the encrypted bids is protected, but the peer matching transactions are performed by the nodes in a publicly verifiable manner through smart contracts. We verify the feasibility of the proposed system by implementing a prototype composed of smart meters, a distribution system operator (DSO) server, and private Ethereum blockchain.

The development of smart meters that can frequently measure and report power consumption has enabledelectricity providers to offer various time-varying rates, including time-of-use and real-time pricing plans. High-resolution power consumption data, however, raise serious privacy concerns because sensitive information regarding an individual’s lifestyle can be revealed by analyzing these data. Although extensive research has been conducted to address these privacy concerns, previous approaches have reduced the quality of measured data. In this paper, we propose a new privacy-preserving electricity billing method that does not sacrifice data quality for privacy. The proposed method is based on the novel use of functional encryption. Experimental results on a prototype system using a real-world smart meter device and data prove the feasibility of the proposed method.

Over the last several years, in parallel with the general global advancement in mobile technology and a rise in social media network content consumption, multimedia content production and reproduction has increased exponentially. Therefore, enabled by the rapid recent advancements in deep learning technology, research on scene graph generation is being actively conducted to more efficiently search for and classify images desired by users within a large amount of content. This approach lets users accurately find images they are searching for by expressing meaningful information on image content as nodes and edges of a graph. In this study, we propose a scene graph generation method based on using the Resource Description Framework (RDF) model to clarify semantic relations. Furthermore, we also use convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to generate a scene graph expressed in a controlled vocabulary of the RDF model to understand the relations between image object tags. Finally, we experimentally demonstrate through testing that our proposed technique can express semantic content more effectively than existing approaches.

Direct mapping is an automatic transformation method used to generate resource description framework (RDF) data from relational data. In the field of direct mapping, semantics preservation is critical to ensure that the mapping method outputs RDF data without information loss or incorrect semantic data generation. However, existing direct-mapping methods have problems that prevent semantics preservation in specific cases. For this reason, a mapping method is developed to perform a semantics-preserving transformation of relational databases (RDB) into RDF data without semantic information loss and to reduce the volume of incorrect RDF data. This research reviews cases that do not generate semantics-preserving results, and the corresponding problems into categories are arranged. This paper defines lemmas that represent the features of RDF data transformation to resolve those problems. Based on the lemmas, this work develops a hierarchical direct-mapping method to strictly abide by the definition of semantics preservation and to prevent semantic information loss, reducing the volume of incorrect RDF data generated. Experiments demonstrate the capability of the proposed method to perform semantics-preserving RDB2RDF data transformation, generating semantically accurate results. This work impacts future studies, which should involve the development of synchronization methods to achieve RDF data consistency when original RDB data are modified.

Calculation of the shortest path between two nodes in a graph is a popular operation used in graph queries in applications such as map information systems, social networking services, and biotechnology. Recent shortest-path search techniques based on graphs stored in relational databases are able to calculate the shortest path efficiently, even in large data using frontier-expand-merge operations. However, previous approaches used a sequential bidirectional search method that causes a bottleneck, thus degrading performance. The repeated use of an aggregate SQL function also degrades performance. This paper proposes a parallel bi-directional search method using multithreading. In addition, an efficient optimization method is proposed that uses B-tree indexing instead of an aggregate SQL function. Various experiments using synthetic and real data reveal that the proposed optimization technique performs more efficiently than conventional methods. As the size of data in practical applications continues to grow, these optimizations will enable the shortest path in a graph to be found quickly and accurately.

The electrical switching behavior of the GeTe phase-changing material grown by atomic layer deposition is characterized for the phase change random access memory (PCRAM) application. Planar-type PCRAM devices are fabricated with a TiN or W bottom electrode (BE). The crystallization behavior is characterized by applying an electrical pulse train and analyzed by applying the Johnson&ndash, Mehl&ndash, Avrami kinetics model. The device with TiN BE shows a high Avrami coefficient (&gt, 4), meaning that continuous and multiple nucleations occur during crystallization (set switching). Meanwhile, the device with W BE shows a smaller Avrami coefficient (~3), representing retarded nucleation during the crystallization. In addition, larger voltage and power are necessary for crystallization in case of the device with W BE. It is believed that the thermal conductivity of the BE material affects the temperature distribution in the device, resulting in different crystallization kinetics and set switching behavior.