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In this study, the inner-core structures of Hurricane Andrew (1992) are explicitly simulated using an improved version of the Penn State-NCAR nonhydrostatic, two-way interactive, movable, triply nested grid mesoscale model (MM5). A modified Betts-Miller cumulus parameterization scheme and an explicit microphysics scheme were used simultaneously to simulate the evolution of the larger-scale flows over the coarser-mesh domains. The intense storm itself is explicitly resolved over the finest-mesh domain using a grid size of 6 km and an explicit microphysics package containing prognostic equations for cloud water, ice, rainwater, snow, and graupel. The model is initialized with the National Centers for Environmental Prediction analysis enhanced by a modified moisture field. A model-generated tropical-storm-like vortex was also incorporated. A 72-h integration was made, which covers the stages from the storm's initial deepening to a near-category 5 hurricane intensity and the landfall over Florida. As verified against various observations and the best analysis, the model captures reasonably well the evolution and inner-core structures of the storm. In particular, the model reproduces the track, the explosive deepening rate (Ͼ1.5 hPa h Ϫ1), the minimum surface pressure of 919 hPa preceding landfall, the strong surface wind (Ͼ65 m s Ϫ1) near the shoreline, as well as the ring of maximum winds, the eye, the eyewall, the spiral rainbands, and other cloud features. Of particular significance is that many simulated kinematics, thermodynamics, and precipitation structures in the core regions compare favorably to previous observations of hurricanes. The results suggest that it may be possible to predict reasonably the track, intensity, and inner-core structures of hurricanes from the tropical synoptic conditions if high grid resolution, realistic model physics, and proper initial vortices (depth, size, and intensity) in relation to their larger-scale conditions (e.g., SST, moisture content, and vertical shear in the lower troposphere) are incorporated.

The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions.

Although most of the planetary boundary layer (PBL) parameterizations have demonstrated the capability to reproduce many meteorological phenomena in the lowest few kilometers, little attention has been paid to the prediction of the diurnal cycles of surface wind speed (V SFC ) in relation to surface temperature (T SFC ). In this study, the performance of five widely used PBL parameterizations in reproducing the diurnal cycles of V SFC and T SFC is evaluated using the 3-day mesoscale simulations of summertime weak-gradient flows over the central United States where little organized convection and topographical forcing were present. The time series of areaaveraged V SFC and T SFC , as well as the vertical wind and thermal profiles from the five sensitivity simulations, are compared with hourly surface observations and other available data. The hourly surface observations reveal that the diurnal cycles of V SFC are in phase (but surface wind directions are 5-6 h out of phase) with those of T SFC . It is shown that both V SFC and T SFC are very sensitive to the PBL parameterizations, given the identical conditions for all of the other model parameters. It is found that all five of the PBL schemes can reproduce the diurnal phases of T SFC (and wind directions), albeit with different amplitudes. However, all of the schemes underestimate the strength of V SFC during the daytime, and most of them overestimate it at night. Moreover, some PBL schemes produce pronounced phase errors in V SFC or substantially weak V SFC all of the time, despite their well-simulated diurnal cycle of T SFC . The results indicate that a perfect simulation of the diurnal T SFC cycle (and the thermal structures above) does not guarantee the reproduction of the diurnal cycles of V SFC . The final outcome would depend on how various physical processes, such as the vertical turbulent exchanges of the mass and momentum under different stability conditions, are parameterized. Because the upper portion of the PBL flow is often nearly opposite in phase to V SFC under weak-gradient conditions, the results have significant implications for the predictability of diurnal precipitation and the studies of air quality, wind energy, and other environmental problems. 1 The diurnal cycle of the PBL used to be viewed in terms of surface radiative forcing and low-level temperature variations. In this study, it also includes the surface winds and the winds above in the PBL.

Despite considerable progress in understanding the hurricane vortex using balanced models, the validity of gradient wind balance in the eyewall remains controversial in observational studies. In this paper, the structure and development of unbalanced forces and flows in hurricanes are examined, through the analyses of the radial momentum and absolute angular momentum (AAM) budgets, using a high-resolution (i.e., ⌬x ϭ 6 km), fully explicit simulation of Hurricane Andrew (1992). It is found from the radial momentum budgets that supergradient flows and accelerations, even after temporal and azimuthal averaging, are well organized from the bottom of the eye center to the upper outflow layer in the eyewall. The agradient accelerations are on average twice greater than the local Coriolis force, and caused mainly by the excess of the centrifugal force over the pressure gradient force. It is shown by the AAM budgets that supergradient flows could occur not only in the inflow region as a result of the inward AAM transport, but also in the outflow region through the upward transport of AAM. The eyewall is dominated by radial outflow in which the upward transport of AAM overcompensates the spindown effect of the outflow during the deepening stage. The intense upper outflow layer is generated as a consequence of the continuous outward acceleration of airflows in the eyewall updrafts. In spite of the pronounced agradient tendencies, results presented here suggest that the azimuthally averaged tangential winds above the boundary layer satisfy the gradient wind balance within an error of 10%. The analyses of instantaneous fields show pronounced asymmetries and well-organized wavenumber-2 structures of the agradient flows and forces in the form of azimuthally propagating vortex-Rossby waves in the eyewall. These waves propagate cyclonically downstream with a speed half the tangential winds near the top of the boundary layer and vertically upward. Agradient flows/forces and AAM transport in the eye are also discussed.

Human epidermal growth factor receptor 2 (HER2) is overexpressed in over 20% of breast cancers. The dimerization of HER2 receptors leads to the activation of downstream signals enabling proliferation and survival of malignant phenotypes. Owing to the high expression levels of HER2, combination therapies are currently required for the treatment of HER2-positive breast cancer. Here, we designed non-toxic transformable peptides that self-assemble into micelles in aqueous conditions, but, upon binding to HER2 on cancer cells, transform into nanofibers, which Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

During the presummer rainy season (April–June), southern China often experiences frequent occurrences of extreme rainfall, leading to severe flooding and inundations. To expedite the efforts in improving the quantitative precipitation forecast (QPF) of the presummer rainy season rainfall, the China Meteorological Administration (CMA) initiated a nationally coordinated research project, namely, the Southern China Monsoon Rainfall Experiment (SCMREX) that was endorsed by the World Meteorological Organization (WMO) as a research and development project (RDP) of the World Weather Research Programme (WWRP). The SCMREX RDP (2013–18) consists of four major components: field campaign, database management, studies on physical mechanisms of heavy rainfall events, and convection-permitting numerical experiments including impact of data assimilation, evaluation/improvement of model physics, and ensemble prediction. The pilot field campaigns were carried out from early May to mid-June of 2013–15. This paper: i) describes the scientific objectives, pilot field campaigns, and data sharing of SCMREX; ii) provides an overview of heavy rainfall events during the SCMREX-2014 intensive observing period; and iii) presents examples of preliminary research results and explains future research opportunities.

Due to the lack of meteorological observations over the tropical oceans, almost all the current hurricane models require bogusing of a vortex into the large-scale analysis of the model initial state. In this study, an algorithm to construct hurricane vortices is developed using the Advanced Microwave Sounding Unit (AMSU-A) data. Under rain-free atmospheric conditions, the temperature profile could be retrieved with a root-meansquare error of 1.5ЊC. Under heavy rainfall conditions, measurements from channels 3-5 are removed in retrieving temperatures. An application of this algorithm to Hurricane Bonnie (1998) shows well the warm-core eye and strong thermal gradients across the eyewall. The rotational and divergent winds are obtained by solving the nonlinear balance and omega equations using the large-scale analysis as the lateral boundary conditions. In doing so, the sea level pressure distribution is empirically specified, and the geopotential heights are calculated from the retrieved temperatures using the hydrostatic equation. The so-derived temperature and wind fields associated with Bonnie compare favorably to the dropsonde observations taken in the vicinity of the storm. The initial moisture field is specified based on the AMSU-derived total precipitable water. The effectiveness of using the retrieved hurricane vortex as the model initial conditions is tested using three 48-h simulations of Bonnie with the finest grid size of the 4-km, triply nested version of the fifthgeneration Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). It is found that the control run captures reasonably well the track and rapid deepening stage of the storm. The simulated radar reflectivity exhibits highly asymmetric structures of the eyewall and cloud bands, similar to the observed. A sensitivity simulation is conducted, in which an axisymmetric vortex is used in the model initial conditions. The simulated features are less favorable compared to the observations. Without the incorporation of the AMSU data, the simulated intensity and cloud structures differ markedly from the observed. The results suggest that this algorithm could provide an objective, observation-based way to incorporate a dynamically consistent vortex with reasonable asymmetries into the initial conditions of hurricane models. This algorithm could also be utilized to estimate three-dimensional hurricane flows after the hurricane warm core and eyewall are developed.