Unveiling hidden stellar aggregates in the Milky Way: 1656 new star clusters found in Gaia EDR3

We report 1,656 new star clusters found in the Galactic disk (|b|<20 degrees) beyond 1.2 kpc, using Gaia EDR3 data. Based on an unsupervised machine learning algorithm, DBSCAN, and followed our previous studies, we utilized a unique method to do the data preparation and obtained the clustering coefficients, which proved to be an effective way to search blindly for star clusters. We tabulated the physical parameters and member stars of the new clusters, and presented some interesting examples, including a globular cluster candidate. The cluster parameters and member stars are available at CDS via anonymous ftp to https://cdsarc.cds.unistra.fr/ftp/vizier.submit//he22c. We examined the new discoveries and discussed their statistical properties. The proper motion dispersions and radii of the new clusters were the same as the previously reported ones. The new star clusters beyond 1.2 kpc were older than those in the solar neighborhood, and the new objects found in the third Galactic quadrant presented the lowest line-of-sight extinctions. Combined with our previous results, the total population of new clusters detected through our method was 2,541, corresponding to 55% of all newly published clusters in the Gaia era. The number of cataloged Gaia star clusters was also increased to nearly six thousand. In the near future, it is necessary to make a unified confirmation and member star determination for all reported clusters.Comment: 16 pages, 11 figures, 3 tables with full clusters/members data link in CDS, accepted for publication in ApJ

Enhanced cycling stability of Li–O2 batteries by using a polyurethane/SiO2/glass fiber nanocomposite separator

A considerable improvement in the cycle performance of aprotic Li–O2 batteries was achieved by using a polyurethane/SiO2 gel nanoparticles/glass fiber (PU/SiO2/GF) nanocomposite separator, where a persistent capability of 1000 mA h g−1 was maintained for at least 300 charge/discharge cycles in a DMSO electrolyte with 1 M LiClO4 and 0.05 M LiI. In comparison, the cell with a conventional GF separator in the same experimental setup only run for 60 cycles. SEM, XRD and FT-IR analyses indicate that the corrosion and dendritic growth of the Li anode were significantly inhibited during the charge/discharge cycling, and the eventual failure of the Li–O2 batteries was attributed to the cathode passivation caused by the accumulation of the discharge product, which blocked the transfer of oxygen and electrolyte to the MWNTs cathode

Detection of MMP activity in living cells by a genetically encoded surface-displayed FRET sensor

AbstractMatrix metalloproteinases (MMPs) are secretory endopeptidases. They have been associated with invasion by cancer-cell and metastasis. Previous studies have demonstrated that proteolytic activity could be detected using fluorescence resonance energy transfer (FRET) with mutants of GFP. To monitor MMP activity, we constructed vectors that encoded a MMP Substrate Site (MSS) between YFP and CFP. In vitro, YFP–MSS–CFP can be used to detect MMP activity and 1,10-phenathroline inhibition of MMP activity. In living cells, MMPs are secreted proteins and act outside of the cell, and therefore YFP–MSS–CFPdisplay was anchored on the cellular surface to detect extracellular MMP. A pDisplay-YC vector expressing the YFP–MSS–CFPdisplay on the cellular surface was transfected into MCF-7 cells that expressed low levels of MMP. Efficient transfer of energy from excited CFP to YFP within the YFP–MSS–CFPdisplay molecule was observed, and real-time FRET was declined when MCF-7 was incubated with MMP2. However, no such transfer of energy was detected in the YFP–MSS–CFPdisplay expressing MDA-MB 435s cells, in which high secretory MMP2 were expressed. The FRET sensor YFP–MSS–CFPdisplay can sensitively and reliably monitor MMP activation in living cells and can be used for high-throughput screening of MMP inhibitors for anti-cancer treatments

A facile surface preservation strategy on the lithium anode for high performance Li-O2 batteries

Protecting an anode from deterioration during charging/discharging has been seen as one of the key strategies in achieving high-performance lithium (Li)–O2 batteries and other Li–metal batteries with a high energy density. Here, we describe a facile approach to prevent the Li anode from dendritic growth and chemical corrosion by constructing a SiO2/GO hybrid thin layer on the surface. The uniform pore-preserving layer can conduct Li ions in the stripping/plating process, leading to an effective alleviation of the dendritic growth of Li by guiding the ion flux through the microstructure. Such a preservation technique significantly enhances the cell performance by enabling the Li–O2 cell to cycle up to 348 times at 1 A·g–1 with a capacity of 1000 mA·h·g–1, which is several times the cycles of cells with pristine Li (58 cycles), Li–GO (166 cycles), and Li–SiO2 (187 cycles). Moreover, the rate performance is improved, and the ultimate capacity of the cell is dramatically increased from 5400 to 25,200 mA·h·g–1. This facile technology is robust and conforms to the Li surface, which demonstrates its potential applications in developing future high-performance and long lifespan Li batteries in a cost-effective fashion

A Novel Web Attack Detection System for Internet of Things via Ensemble Classification

Internet of things (IoT) has become one of the fastestgrowing technologies and has been broadly applied in various fields. IoT networks contain millions of devices with the capability of interacting with each other and providing functionalities that were never available to us before. These IoT networks are designed to provide friendly and intelligent operations through big data analysis of information generated or collected from an abundance of devices in real time. However, the diversity of IoT devices makes the IoT networks environments more complex and more vulnerable to various web attacks compared to traditional computer networks. In this paper, we propose a novel Ensemble Deep Learning based Web Attack Detection System (EDL-WADS) to alleviate the serious issues that IoT networks faces. Specifically, we have designed three deep learning models to first detect web attacks separately. We then use an ensemble classifier to make the final decision according to the results obtained from the three deep learning models. In order to evaluate the proposed WADS, we have performed experiments on a public dataset as well as a realword dataset running in a distributed environment. Experimental results show that the proposed system can detect web attacks accurately with low false positive and negative rates

A Ga-Sn Liquid Metal Mediated Structural Cathode for Li-O2 Batteries

One of the recent challenges in Li–O2 battery technology is the cycle life, which can be severely shortened by cathode passivation induced by discharge product accumulation; this can be eliminated by reducing the amount of discharge products. Herein, we report a feasibility study on the development of a Ga–Sn liquid metal (LM)-functionalized multiwalled carbon nanotubes (MWNTs) cathode. In a comparison of MWNT, LM, m-LM/MWNT (pre-mixed LM and MWNTs), and LM/MWNT (LM-modified MWNTs) cathodes, morphology analysis showed that small Li2O2 flakes rather than large crystals grow on the conductive Ga–Sn LM and MWNTs of the LM/MWNT cathode only. The decomposition of the flaky Li2O2 on the LM/MWNT cathode occurred at lower charge overpotentials, resulting in low polarization; thus, the cathode passivation and the consumption of the Li anode were both alleviated during the cyclic process. The LM/MWNT cathode significantly improved the cycle life, rate performance, and ultimate capacity of Li–O2 batteries

Comparison of Apparent Diffusion Coefficient and T2 Relaxation Time Variation Patterns in Assessment of Age and Disc Level Related Intervertebral Disc Changes

Purpose To compare the variation patterns of ADC and T2 values in different age and intervertebral disc (IVD) levels, thus to identify their sensitivities in assessing age and disc level related IVDs changes. Materials and Methods The T2 and ADC values were recorded from 345 IVDs of 69 volunteers. Kendall's correlation analysis was used to identify the relationship between age and T2/ADC mean values respectively. The one-way analysis of variance (ANOVA) with post hoc analysis was then applied to test the differences of T2 and ADC values among different IVD levels and age groups, followed by linear regression analysis between age (45 years) and T2/ADC mean values. This study was approved by the Ethics Committee of the Chinese Academy of Medical Sciences and the Peking Union Medical College Hospital. Results: Significant negative correlation was observed between age and T2/ADC mean values. The T2 and ADC values showed significant differences among IVD levels and among age groups except for T2 values in age group 1 (25–34 years) and group 2 (35–44 years), and for ADC values at L1–2 level. Both T2 and ADC values showed significant differences between young (age45 years) at each IVD level. A linear relationship was observed between age and T2/ADC mean values in the elderly group as well as in the young group for the ADC mean values, while no such tendency was identified in the young group for the T2 mean values. Conclusions: ADC values may be a more sensitive parameter than T2 in assessing age and disc level related intervertebral disc changes

Application of a new Structural Joint Inversion Approach to Teleseismic and Gravity Data from Mt.Vesuvius, Italy

A 3-D joint inversion of seismic and gravimetric data is performed to re-investigate the subsurface structure of Mt. Vesuvius (Italy) utilizing an improved joint inversion method. The aim is to derive models of the 3D distribution of velocity and density perturbations that are consistent with both data sets and with local velocity models. Mt. Vesuvius is a strato volcano located within a graben (Campania Plain) formed in Plio-Pleistocene. Campania Plain is bordered by mostly Mesozoic carbonaceous rocks. Mt. Vesuvius is the southernmost and the youngest of a group of Pleistocene volcanoes, three of which (Ischia, Campi Flegrei and Mt. Vesuvius) have erupted in historical times. The most recent eruption of Mt. Vesuvius occurred in 1944 and since then the volcanic activity has been characterized by moderate low magnitude seismicity and low temperature fumaroles at the summit crater. We modified the coupling mechanism between velocity and density models in the JI-3D optimized joint inversion method (Jordan and Achauer, 1999). This method was designed to provide stable and high resolution results and involves iterative optimized parameterization, 3D ray tracing, and the incorporation of a priori information. The coupling of the velocity and density models, vital to the joint inversion, is based on a cross-gradient approach (e.g. Gallardo and Meju, 2004), which has been proven to work very well in a variety of cases involving seismic, magnetic, CSEM, MT and gravity data sets. We implemented the cross-gradient coupling for our 3-D irregular adaptive grid parameterization. In contrast to conventional joint inversion methods this approach encourages structural similarities in the models and does not rely on predefined relationships between velocity and density parameters. As a consequence, the resulting velocity-density relations are not contaminated by a priori assumptions and can be utilized to derive rock physical parameters. We apply this method to data from the TomoVes project (Gasparini et al. 1998), combining seismics and Bouguer gravity and local high resolution velocity models as a priori information. The starting models for the joint inversion are derived by separate inversions of the individual data sets. We show 3D distributions of velocity perturbations and density variations from the joint inversion of teleseismic relative traveltimes and Bouguer anomaly data with the aim of extracting further information about the physical status of the volcano- tectonic system

AuPt Nanoparticles/ Multi-Walled Carbon Nanotubes Catalyst as High Active and Stable Oxygen Reduction Catalyst for Al-Air Batteries

A series of AuPt nanoparticles supported on multi-walled carbon nanotubes (AuxPt/MWNTs) catalysts with ultrafine distribution (d ≈ 3.0 nm) were synthesized for Al-air battery cathode to enhance the oxygen reduction reaction. Among them, Au0.67Pt/MWNTs catalyst with metal loading of 10.2wt.% (Au:4.1wt.%, Pt:6.1wt.%) exhibited a superior ORR catalytic activity and competitive durability to 20wt.% Pt/C catalyst. When applied as Al-air battery, appropriate increasing Au loading encourage better battery performance. Au1.68Pt/MWNTs with 8.95wt.% of Au and as little as 5.3 wt.% Pt content exhibit larger specific capacity (921 mAh g-1) and power density (146.8 mW cm-2) as well as better durability than 20 wt.% Pt/C catalyst when it is assembled as cathode in Al-air battery