Word Embedding based Correlation Model for Question/Answer Matching

With the development of community based question answering (Q&A) services, a large scale of Q&A archives have been accumulated and are an important information and knowledge resource on the web. Question and answer matching has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, we try to improve the matching accuracy by overcoming the lexical gap between question and answer pairs. A Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding, given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs and it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method's promising potential.Comment: 8 pages, 2 figure

Digital Twin-based 3D Map Management for Edge-assisted Device Pose Tracking in Mobile AR

Edge-device collaboration has the potential to facilitate compute-intensive device pose tracking for resource-constrained mobile augmented reality (MAR) devices. In this paper, we devise a 3D map management scheme for edge-assisted MAR, wherein an edge server constructs and updates a 3D map of the physical environment by using the camera frames uploaded from an MAR device, to support local device pose tracking. Our objective is to minimize the uncertainty of device pose tracking by periodically selecting a proper set of uploaded camera frames and updating the 3D map. To cope with the dynamics of the uplink data rate and the user's pose, we formulate a Bayes-adaptive Markov decision process problem and propose a digital twin (DT)-based approach to solve the problem. First, a DT is designed as a data model to capture the time-varying uplink data rate, thereby supporting 3D map management. Second, utilizing extensive generated data provided by the DT, a model-based reinforcement learning algorithm is developed to manage the 3D map while adapting to these dynamics. Numerical results demonstrate that the designed DT outperforms Markov models in accurately capturing the time-varying uplink data rate, and our devised DT-based 3D map management scheme surpasses benchmark schemes in reducing device pose tracking uncertainty.Comment: Accepted by IEEE Internet of Things Journa

Current ripple reduction for finite control set model predictive control strategy of grid-tied inverter with reference current compensation

In the finite control set model predictive control (FCS-MPC) strategy of the grid-tied inverter, the current ripple (CR) affects the selection of optimal voltage vectors, which leads to the increase of output current ripples. In order to solve this problem, this paper proposes a CR reduction method based on reference current compensation (RCC) for the FCS-MPC strategy of grid-tied inverters. Firstly, the influence of the CR on optimal voltage vector selection is analyzed. The conventional CR prediction method is improved, which uses inverter output voltage and grid voltage to calculate current ripples based on the space state equation. It makes up for the shortcomings that the conventional CR prediction method cannot predict in some switching states. The improved CR method is more suitable for the FCS-MPC strategy. In addition, the differences between the two cost functions are compared through visual analysis. It is found that the sensitivity of the square cost function to small errors is better than that of the absolute value function. Finally, the predicted CR is used to compensate the reference current. The compensated reference current is substituted into the square cost function to reduce the CR. The experimental results show that the proposed method reduces the CR by 47.3%. The total harmonic distortion (THD) of output current is reduced from 3.86% to 2.96%

Rapid Preparation of Spherical Granules via the Melt Centrifugal Atomization Technique

Granules with superior fluidity and low moisture absorption are ideal for tableting and capsule filling. Melt granulation as a solvent-free technology has attracted increasing interest for the granulation of moisture-sensitive drugs. The objective of the present study was to develop a solvent-less and high throughput melt granulation method via the melt centrifugal atomization (MCA) technique. The granule formability of various drugs and excipients via MCA and their dissolution properties were studied. It was found that the yield, fluidity, and moisture resistance of the granules were affected by the drug and excipient types, operation temperature, and collector diameter. The drugs were in an amorphous state in pure drug granules, or were highly dispersed in excipients as solid dispersions. The granules produced via MCA showed an improved drug dissolution. The present study demonstrated that the solvent-free, one-step, and high-throughput MCA approach can be used to produce spherical granules with superior fluidity and immediate drug release characteristics for poorly water-soluble and moisture-sensitive therapeutics

Research on the optimal capacity configuration of green storage microgrid based on the improved sparrow search algorithm

Green storage plays a key role in modern logistics and is committed to minimizing the environmental impact. To promote the transformation of traditional storage to green storage, research on the capacity allocation of wind-solar-storage microgrids for green storage is proposed. Firstly, this paper proposes a microgrid capacity configuration model, and secondly takes the shortest payback period as the objective function, and uses the improved sparrow search algorithm (ISSA) for optimization. Firstly, the Logistic-Tent compound chaotic mapping method is added to the population initialization of the sparrow search algorithm (SSA). Secondly, the adaptive t-distribution mutation is used to improve the discoverer, and the overall optimization ability of the algorithm is improved. Finally, the hybrid decreasing strategy is adopted in the process of vigilance position update. The ISSA can improve the search efficiency of the algorithm, avoid premature convergence and enhance the robustness of the algorithm, which is helpful to better apply to the optimal configuration of wind-solar-storage microgrid capacity in green storage. By analyzing the optimal capacity allocation results of two typical days, the system can better adapt to the dynamic storage requirements and improve the flexibility and sustainability of the supply chain

Clinical and Prognostic Value of PET/CT Imaging with Combination of 68

Background. To evaluate the clinical and prognostic value of PET/CT with combination of 68Ga-DOTATATE and 18F-FDG in gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). Method. 83 patients of GEP-NENs who underwent 68Ga-DOTATATE and 18F-FDG PET/CT were enrolled between June 2013 and December 2016. Well-differentiated (WD) NETs are divided into group A (Ki-67 < 10%) and group B (Ki-67 ≥ 10%), and poorly differentiated (PD) NECs are defined as group C. The relationship between PET/CT results and clinicopathological characteristics was retrospectively investigated. Result. For groups A/B/C, the sensitivities of 68Ga-DOTATATE and 18F-FDG were 78.8%/83.3%/37.5% and 52.0%/72.2%/100.0%. A negative correlation between Ki-67 and SUVmax of 68Ga-DOTATATE (R = −0.415; P ≤ 0.001) was observed, while a positive correlation was noted between Ki-67 and SUVmax of 18F-FDG (R = 0.683; P ≤ 0.001). 62.5% (5/8) of patients showed significantly more lesions in the bone if 68Ga-DOTATATE was used, and 22.7% (5/22) of patients showed more lymph node metastases if 18F-FDG was used. Conclusions. The sensitivity of dual tracers was correlated with cell differentiation, and a correlation between Ki-67 and both SUVmax of PET-CTs could be observed. 68Ga-DOTATATE is suggested for WD-NET and 18F-FDG is probably suitable for patients with Ki-67 ≥ 10%

Identification of Renal Long Non-coding RNA RP11-2B6.2 as a Positive Regulator of Type I Interferon Signaling Pathway in Lupus Nephritis

Objective: Lupus nephritis (LN) is one of the most serious complications of systemic lupus erythematosus (SLE). Type I interferon (IFN-I) is associated with the pathogenesis of LN. Long non-coding RNAs (lncRNAs) have been implicated in the pathogenesis of SLE, however, the roles of lncRNAs in LN are still poorly understood. Here, we identified and investigated the function of LN-associated lncRNA RP11-2B6.2 in regulating IFN-I signaling pathway.Methods: RNA sequencing was used to analyze the expression of lncRNAs in kidney biopsies from LN patients and controls. Antisense oligonucleotides and CRISPRi system or overexpression plasmids and CRISPRa system were used to perform loss or gain of function experiments. In situ hybridization, imaging flow cytometry, dual-luciferase reporter assay, and ATAC sequencing were used to study the functions of lncRNA RP11-2B6.2. RT-qPCR, ELISA, and western blotting were done to detect RNA and protein levels of specific genes.Results: Elevated lncRNA RP11-2B6.2 was observed in kidney biopsies from LN patients and positively correlated with disease activity and IFN scores. Knockdown of lncRNA RP11-2B6.2 in renal cells inhibited the expression of IFN stimulated genes (ISGs), while overexpression of lncRNA RP11-2B6.2 enhanced ISG expression. Knockdown of LncRNA RP11-2B6.2 inhibited the phosphorylation of JAK1, TYK2, and STAT1 in IFN-I pathway, while promoted the chromatin accessibility and the transcription of SOCS1.Conclusion: The expression of lncRNAs is abnormal in the kidney of LN. LncRNA RP11-2B6.2 is a novel positive regulator of IFN-I pathway through epigenetic inhibition of SOCS1, which provides a new therapeutic target to alleviate over-activated IFN-I signaling in LN

Selective modes affect gene feature and function differentiation of tetraploid Brassica species in their evolution and domestication

The genus Brassica contains a diverse group of important vegetables and oilseed crops. Genome sequencing has been completed for the six species (B. rapa, B. oleracea, B. nigra, B. carinata, B. napus, and B. juncea) in U’s triangle model. The purpose of the study is to investigate whether positively and negatively selected genes (PSGs and NSGs) affect gene feature and function differentiation of Brassica tetraploids in their evolution and domestication. A total of 9,701 PSGs were found in the A, B and C subgenomes of the three tetraploids, of which, a higher number of PSGs were identified in the C subgenome as comparing to the A and B subgenomes. The PSGs of the three tetraploids had more tandem duplicated genes, higher single copy, lower multi-copy, shorter exon length and fewer exon number than the NSGs, suggesting that the selective modes affected the gene feature of Brassica tetraploids. The PSGs of all the three tetraploids enriched in a few common KEGG pathways relating to environmental adaption (such as Phenylpropanoid biosynthesis, Riboflavin metabolism, Isoflavonoid biosynthesis, Plant-pathogen interaction and Tropane, piperidine and pyridine alkaloid biosynthesis) and reproduction (Homologous recombination). Whereas, the NSGs of the three tetraploids significantly enriched in dozens of biologic processes and pathways without clear relationships with evolution. Moreover, the PSGs of B. carinata were found specifically enriched in lipid biosynthesis and metabolism which possibly contributed to the domestication of B. carinata as an oil crop. Our data suggest that selective modes affected the gene feature of Brassica tetraploids, and PSGs contributed in not only the evolution but also the domestication of Brassica tetraploids