Deep Model-Based Security-Aware Entity Alignment Method for Edge-Specific Knowledge Graphs

This paper proposes a deep model-based entity alignment method for the edge-specific knowledge graphs (KGs) to resolve the semantic heterogeneity between the edge systems’ data. To do so, this paper first analyzes the edge-specific knowledge graphs (KGs) to find unique characteristics. The deep model-based entity alignment method is developed based on their unique characteristics. The proposed method performs the entity alignment using a graph which is not topological but data-centric, to reflect the characteristics of the edge-specific KGs, which are mainly composed of the instance entities rather than the conceptual entities. In addition, two deep models, namely BERT (bidirectional encoder representations from transformers) for the concept entities and GAN (generative adversarial networks) for the instance entities, are applied to model learning. By utilizing the deep models, neural network models that humans cannot interpret, it is possible to secure data on the edge systems. The two learning models trained separately are integrated using a graph-based deep learning model GCN (graph convolution network). Finally, the integrated deep model is utilized to align the entities in the edge-specific KGs. To demonstrate the superiority of the proposed method, we perform the experiment and evaluation compared to the state-of-the-art entity alignment methods with the two experimental datasets from DBpedia, YAGO, and wikidata. In the evaluation metrics of Hits@k, mean rank (MR), and mean reciprocal rank (MRR), the proposed method shows the best predictive and generalization performance for the KG entity alignment

Deep Model-Based Security-Aware Entity Alignment Method for Edge-Specific Knowledge Graphs

This paper proposes a deep model-based entity alignment method for the edge-specific knowledge graphs (KGs) to resolve the semantic heterogeneity between the edge systems’ data. To do so, this paper first analyzes the edge-specific knowledge graphs (KGs) to find unique characteristics. The deep model-based entity alignment method is developed based on their unique characteristics. The proposed method performs the entity alignment using a graph which is not topological but data-centric, to reflect the characteristics of the edge-specific KGs, which are mainly composed of the instance entities rather than the conceptual entities. In addition, two deep models, namely BERT (bidirectional encoder representations from transformers) for the concept entities and GAN (generative adversarial networks) for the instance entities, are applied to model learning. By utilizing the deep models, neural network models that humans cannot interpret, it is possible to secure data on the edge systems. The two learning models trained separately are integrated using a graph-based deep learning model GCN (graph convolution network). Finally, the integrated deep model is utilized to align the entities in the edge-specific KGs. To demonstrate the superiority of the proposed method, we perform the experiment and evaluation compared to the state-of-the-art entity alignment methods with the two experimental datasets from DBpedia, YAGO, and wikidata. In the evaluation metrics of Hits@k, mean rank (MR), and mean reciprocal rank (MRR), the proposed method shows the best predictive and generalization performance for the KG entity alignment

Single-step-fabricated perovskite quantum dot photovoltaic absorbers enabled by surface ligand manipulation

Lead halide perovskite colloidal quantum dots (PQDs) are receiving great interest in emerging photovoltaics because of their excellent photovoltaic properties and the room-temperature processability without a thermal annealing step. Conductive thick PQD absorbers reported to date have been fabricated via multiple-step layer-by-layer deposition based on solid-state ligand exchange; however, this approach requiring a lot of processing time and cost is not suitable for the mass production. Thus, a single-step fabrication approach of conductive thick PQD absorbers should be devised. Herein, we demonstrate that conductive thick CsPbI3-PQD absorbers can be fabricated via a single-step process based on the surface ligand manipulation and employed in efficient PQD solar cells. We find that the conventional ethyl acetate-based post-treatment significantly removes long-chain ligands of the unexchanged PQDs (UN-PQDs) and cause film delamination of thick UN-PQD solids because of drastic volume shrinkage. Thus, we employ the methyl acetate-based post-treatment using phenethylammonium acetate (PEAOAc) to replace both long-chain oleate and oleylammonium within thick UN-PQD solids with short-chain PEA and OAc ligands without film delamination. To further reduce long-chain ligands within the resultant PQD solids, we also employ the PQDs prepared via a solution-phase ligand exchange (SPLE-PQDs) using the phenethylammonium iodide. Furthermore, we perform various spectroscopic measurements, including Fourier-transform infrared, nuclear magnetic resonance, and X-ray photoelectron spectroscopy, to quantitatively analyze the surface chemistry and ligands of PQDs. Consequently, CsPbI3-PQD solar cells, fabricated via a single-step process using SPLE-PQDs and PEAOAc post-treatment, show improved power conversion efficiency (13.7%) compared to that of the UN-PQD device (12.1%). © 2022 Elsevier B.V.FALS

Surface Modification of Magnetic Nanoparticle Clusters for Biomedical Applications

The diagnosis and therapy of disease require information of multiple targets. In order to identify multiple biomolecule, an effective strategy for separation and multiplexing is important.1) A bio-barcoding system that incorporates quantum dots (QDs) and magnetic nanoparticles can be useful for such applications.2) Silica-shell-coated magnetic nanoparticle cluster (SMNC) were synthesized and the surface of SMNC was decorated by zwitterionic ligands, which made the SMNC colloidally stable at physiological condition and possess minimal non-specific adsorptions.3) Zwitterion tethered SMNC showed enhanced colloidal stability in high salt concentration and under broad pH range. The minimal non-specific adsorption was confirmed by non-specific adsorption level assays using fluorescein conjugated serum albumin. Also, SMNCs were loaded with QDs using amphiphilic polyethyleneimine derivatives (amPEIs). The resultant a few hundred nanometer sized SMNC-QDs-amPEIs were composed of SMNC core surrounded by hundreds of QDs. SMNC-QDs complexs showed excellent photoluminescene(PL) property and can be facilely collected in presence of an external magnetic field. These SMNC-QDs complexs and SMNC with reduced non-specific adsorption can be useful as bio- barcoding agent.1

Structure-Activity Relationships of Acyclic Selenopurine Nucleosides as Antiviral Agents

A series of acyclic selenopurine nucleosides 3a–f and 4a–g were synthesized based on the bioisosteric rationale between oxygen and selenium, and then evaluated for antiviral activity. Among the compounds tested, seleno-acyclovir (4a) exhibited the most potent anti-herpes simplex virus (HSV)-1 (EC50 = 1.47 µM) and HSV-2 (EC50 = 6.34 µM) activities without cytotoxicity up to 100 µM, while 2,6-diaminopurine derivatives 4e–g exhibited significant anti-human cytomegalovirus (HCMV) activity, which is slightly more potent than the guanine derivative 4d, indicating that they might act as prodrugs of seleno-ganciclovir (4d)

Construction of 6,10-<i>syn</i>- and -<i>anti</i>-2,5-Dioxabicyclo[2.2.1]heptane Skeletons via Oxonium Ion Formation/Fragmentation: Prediction of Structure of (<i>E</i>)‑Ocellenyne by NMR Calculation

A highly efficient and stereoselective route to potential synthetic intermediates for ocellenyne and related C₁₅ acetogenin natural products with 6,10-<i>syn</i>- and 6,10-<i>anti</i>-2,5-dioxabicyclo­[2.2.1]­heptane core structures has been developed by means of an iterative biogenesis-inspired oxonium ion formation/fragmentation sequence. In accordance with chemical transformations, the most likely stereostructure for (<i>E</i>)-ocellenyne on the basis of GIAO ¹³C NMR calculations possesses a 6,10-<i>anti</i>-2,5-dioxabicyclo­[2.2.1]­heptane core, as predicted from a plausible biosynthetic pathway, instead of the spectroscopically proposed 6,10-<i>syn</i>-2,5-dioxabicyclo­[2.2.1]­heptane skeleton