Comparison of Graph Databases and Relational Databases When Handling Large-Scale Social Data

Over the past few years, with the rapid development of mobile technology, more people use mobile social applications, such as Facebook, Twitter and Weibo, in their daily lives, and there is an increasing amount of social data. Thus, finding a suitable storage approach to store and process the social data, especially for the large-scale social data, should be important for the social network companies. Traditionally, a relational database, which represents data in terms of tables, is widely used in the legacy applications. However, a graph database, which is a kind of NoSQL databases, is in a rapid development to handle the growing amount of unstructured or semi-structured data. The two kinds of storage approaches have their own advantages. For example, a relational database should be a more mature storage approach, and a graph database can handle graph-like data in an easier way. In this research, a comparison of capabilities for storing and processing large-scale social data between relational databases and graph databases is applied. Two kinds of analysis, the quantitative research analysis of storage cost and executing time and the qualitative analysis of five criteria, including maturity, ease of programming, flexibility, security and data visualization, are taken into the comparison to evaluate the performance of relational databases and graph databases when handling large-scale social data. Also, a simple mobile social application is developed for experiments. The comparison is used to figure out which kind of database is more suitable for handling large-scale social data, and it can compare more graph database models with real-world social data sets in the future research

Assessing the Location of Ionic and Molecular Solutes in a Molecularly Heterogeneous and Nonionic Deep Eutectic Solvent

Copyright © 2020 American Chemical Society. Deep eutectic solvents (DES) are emerging sustainable designer solvents viewed as greener and better alternatives to ionic liquids. Nonionic DESs possess unique properties such as viscosity and hydrophobicity that make them desirable in microextraction applications such as oil-spill remediation. This work builds upon a nonionic DES, NMA-LA DES, previously designed by our group. The NMA-LA DES presents a rich nanoscopic morphology that could be used to allocate solutes of different polarities. In this work, the possibility of solvating different solutes within the nanoscopically heterogeneous molecular structure of the NMA-LA DES is investigated using ionic and molecular solutes. In particular, the localized vibrational transitions in these solutes are used as reporters of the DES molecular structure via vibrational spectroscopy. The FTIR and 2DIR data suggest that the ionic solute is confined in a polar and continuous domain formed by NMA, clearly sensing the direct effect of the change in NMA concentration. In the case of the molecular nonionic and polar solute, the data indicates that the solute resides in the interface between the polar and nonpolar domains. Finally, the results for the nonpolar and nonionic solute (W(CO)6) are unexpected and less conclusive. Contrary to its polarity, the data suggest that the W(CO)6 resides within the NMA polar domain of the DES, probably by inducing a domain restructuring in the solvent. However, the data are not conclusive enough to discard the possibility that the restructuring comprises not only the polar domain but also the interface. Overall, our results demonstrate that the NMA-LA DES has nanoscopic domains with affinity to particular molecular properties, such as polarity. Thus, the presented results have a direct implication to separation science

High-efficiency robust perovskite solar cells on ultrathin flexible substrates.

Wide applications of personal consumer electronics have triggered tremendous need for portable power sources featuring light-weight and mechanical flexibility. Perovskite solar cells offer a compelling combination of low-cost and high device performance. Here we demonstrate high-performance planar heterojunction perovskite solar cells constructed on highly flexible and ultrathin silver-mesh/conducting polymer substrates. The device performance is comparable to that of their counterparts on rigid glass/indium tin oxide substrates, reaching a power conversion efficiency of 14.0%, while the specific power (the ratio of power to device weight) reaches 1.96 kW kg(-1), given the fact that the device is constructed on a 57-μm-thick polyethylene terephthalate based substrate. The flexible device also demonstrates excellent robustness against mechanical deformation, retaining >95% of its original efficiency after 5,000 times fully bending. Our results confirmed that perovskite thin films are fully compatible with our flexible substrates, and are thus promising for future applications in flexible and bendable solar cells

Configuring Intelligent Reflecting Surface with Performance Guarantees: Blind Beamforming

This work gives a blind beamforming strategy for intelligent reflecting surface (IRS), aiming to boost the received signal-to-noise ratio (SNR) by coordinating phase shifts across reflective elements in the absence of channel information. While the existing methods of IRS beamforming typically first estimate channels and then optimize phase shifts, we propose a conditional sample mean based statistical approach that explores the wireless environment via random sampling without performing any channel estimation. Remarkably, the new method just requires a polynomial number of random samples to yield an SNR boost that is quadratic in the number of reflective elements, whereas the standard random-max sampling algorithm can only achieve a linear boost under the same condition. Moreover, we gain additional insight into blind beamforming by interpreting it as a least squares problem. Field tests demonstrate the significant advantages of the proposed blind beamforming algorithm over the benchmark algorithms in enhancing wireless transmission.Comment: 16 pages, 15 figure

Network meta-analysis of balloon angioplasty, nondrug metal stent, drug-eluting balloon, and drug-eluting stent for treatment of infrapopliteal artery occlusive disease

PURPOSE:We aimed to conduct a network meta-analysis of mixed treatments for the infrapopliteal artery occlusive disease.METHODS:We searched randomized controlled trials (RCTs) regarding balloon angioplasty (BA), nondrug metal stent (NDMS), drug-eluting balloon (DEB), or drug-eluting stent (DES) in PubMed, Embase, CENTRAL, Ovid, Sinomed, and other relevant websites. We selected and assessed the trials that met the inclusion criteria and conducted a network meta-analysis using the ADDIS software.RESULTS:We included 11 relevant trials. We analyzed data of 1322 patients with infrapopliteal artery occlusive disease, of which 351 were in the NDMS vs. DES trials, 231 in the NDMS vs. BA trials, 490 in the BA vs. DEB trials, 50 in the DEB vs. DES trials, and 200 in the BA vs. DES trials. The network meta-analysis indicated that with NDMS as the reference, DES had a better result with respect to restenosis (odds ratio [OR], 5.16; 95% credible interval [CI], 1.58–18.41; probability of the best treatment, 84%) and amputation (OR, 2.50; 95% CI, 0.81–7.11; probability of the best treatment, 61%) and DEB had a better result with respect to target lesion revascularization (TLR; OR, 3.74; 95% CI, 0.78–17.05; probability of the best treatment, 57%). Moreover, with BA as the reference, NDMS had a better result with respect to technical success (OR, 0.10; 95% CI, 0.00–1.15; probability of the best treatment, 86%).CONCLUSION:Our meta-analysis revealed that DES is a better treatment with respect to short-term patency and limb salvage rate, NMDS may provide a better technical success, and DEB and DES are good choices for reducing revascularization

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

Deep learning for dense Z-spectra reconstruction from CEST images at sparse frequency offsets

A direct way to reduce scan time for chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is to reduce the number of CEST images acquired in experiments. In some scenarios, a sufficient number of CEST images acquired in experiments was needed to estimate parameters for quantitative analysis, and this prolonged the scan time. For that, we aim to develop a general deep-learning framework to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets so as to reduce the number of experimentally acquired CEST images and achieve scan time reduction. The main innovation works are outlined as follows: (1) a general sequence-to-sequence (seq2seq) framework is proposed to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets; (2) we create a training set from wide-ranging simulated Z-spectra instead of experimentally acquired CEST data, overcoming the limitation of the time and labor consumption in manual annotation; (3) a new seq2seq network that is capable of utilizing information from both short-range and long-range is developed to improve reconstruction ability. One of our intentions is to establish a simple and efficient framework, i.e., traditional seq2seq can solve the reconstruction task and obtain satisfactory results. In addition, we propose a new seq2seq network that includes the short- and long-range ability to boost dense CEST Z-spectra reconstruction. The experimental results demonstrate that the considered seq2seq models can accurately reconstruct dense CEST images from experimentally acquired images at 11 frequency offsets so as to reduce the scan time by at least 2/3, and our new seq2seq network contributes to competitive advantage

Spectral control of nonclassical light using an integrated thin-film lithium niobate modulator

Manipulating the frequency and bandwidth of nonclassical light is essential for implementing frequency-encoded/multiplexed quantum computation, communication, and networking protocols, and for bridging spectral mismatch among various quantum systems. However, quantum spectral control requires a strong nonlinearity mediated by light, microwave, or acoustics, which is challenging to realize with high efficiency, low noise, and on an integrated chip. Here, we demonstrate both frequency shifting and bandwidth compression of nonclassical light using an integrated thin-film lithium niobate (TFLN) phase modulator. We achieve record-high electro-optic frequency shearing of telecom single photons over terahertz range ($\pm$ 641 GHz or $\pm$ 5.2 nm), enabling high visibility quantum interference between frequency-nondegenerate photon pairs. We further operate the modulator as a time lens and demonstrate over eighteen-fold (6.55 nm to 0.35 nm) bandwidth compression of single photons. Our results showcase the viability and promise of on-chip quantum spectral control for scalable photonic quantum information processing

Retroform Cervical Dystonia: Target Muscle Selection and Efficacy of Botulinum Toxin Injection

IntroductionRetroform cervical dystonia (RCD), which includes retrocaput and retrocollis, is a rare form of cervical dystonia. Few reports have been published on RCD. The present study aimed to characterize the target muscles involved in RCD and the efficacy of botulinum toxin type A (BTX-A) injection.MethodsPatients with consecutive cervical dystonia with RCD as the most problematic feature were retrospectively analyzed over a 10-year period. Target muscles were screened and confirmed based on clinical evaluation, single-photon emission computed tomography, and electromyography. In addition, efficacy and adverse events following BTX-A injection in patients with RCD were evaluated.ResultsA total of 34 patients with RCD were included, 18 of whom presented with retrocaput and 16 with retrocollis. The most frequently injected muscles in RCD were splenius capitis (SPCa, 97.1%) and semispinalis capitis (SSCa, 97.1%), followed by levator scapulae (LS, 50.0%), rectus capitis posterior major (RCPM, 47.1%), trapezius (TPZ, 41.2%), and sternocleidomastoid muscle (SCM, 41.2%). Besides cervical muscles, the erector spinae was also injected in 17.6% of patients. Most muscles were predominantly bilaterally injected. The injection schemes of retrocaput and retrocollis were similar, possibly because in patients with retrocollis, retrocaput was often combined. BTX-A injection achieved a satisfactory therapeutic effect in RCD, with an average symptom relief rate of 69.0 ± 16.7%. Mild dysphagia (17.6%) and posterior cervical muscle weakness (17.6%) were the most common adverse events.ConclusionSPCa, SSCa, LS, RCPM, LS, and SCM were commonly and often bilaterally injected in RCD. Patients with RCD could achieve satisfactory symptom relief after BTX-A injection