The penalty method is a simple and popular approach to resolving contact in computer graphics and robotics. Penalty-based contact, however, suffers from stability problems due to the highly variable ...and unpredictable net stiffness, and this is particularly pronounced in simulations with time-varying distributed geometrically complex contact. We employ semi-implicit integration, exact analytical contact gradients, symbolic Gaussian elimination and a SVD solver to simulate stable penalty-based frictional contact with large, time-varying contact areas, involving many rigid objects and articulated rigid objects in complex conforming contact and self-contact. We also derive implicit proportional-derivative control forces for real-time control of articulated structures with loops. We present challenging contact scenarios such as screwing a hexbolt into a hole, bowls stacked in perfectly conforming configurations, and manipulating many objects using actively controlled articulated mechanisms in real time.
Evaluating sea surface temperature (SST) products is essential before their application in marine environmental monitoring and related studies. SSTs from the in situ SST Quality Monitor (iQuam) ...system, Advanced Microwave Scanning Radiometer 2 (AMSR2) aboard the Global Change Observation Mission 1st-Water, and the Microwave Radiation Imager (MWRI) aboard the Chinese Fengyun-3D satellite are intercompared utilizing extended triple collocation (ETC) and direct comparison methods. Additionally, error characteristic variations with respect to time, latitude, SST, sea surface wind speed, columnar water vapor, and columnar cloud liquid water are analyzed comprehensively. In contrast to the prevailing focus on SST validation accuracy, the random errors and the capability to detect SST variations are also evaluated in this study. The result of ETC analysis indicates that iQuam SST from ships exhibits the highest random error, above 0.83 °C, whereas tropical mooring SST displays the lowest random error, below 0.28 °C. SST measurements from drifters, tropical moorings, Argo floats, and high-resolution drifters, which possess random errors of less than 0.35 °C, are recommended for validating remotely sensed SST. The ability of iQuam, AMSR2, and MWRI to detect SST variations diminishes significantly in ocean areas between 0°N and 20°N latitude and latitudes greater than 50°N and 50°S. AMSR2 and iQuam demonstrate similar random errors and capabilities for detecting SST variations, whereas MWRI shows a high random error and weak capability. In comparison to iQuam SST, AMSR2 exhibits a root-mean-square error (RMSE) of about 0.51 °C with a bias of −0.05 °C, while MWRI shows an RMSE of about 1.26 °C with a bias of −0.14 °C.
C‐band high‐resolution radar (synthetic aperture radar SAR) is the only spaceborne instrument able to probe at very high resolution and over all ocean basins the sea surface under extreme weather ...conditions. When coanalyzed with Stepped Frequency Microwave Radiometer wind estimates, the radar backscatter signals acquired in major hurricanes from Sentinel‐1 and Radarsat‐2 SAR reveal high sensitivity in the cross‐polarized channel for wind speeds up to 75 m/s. The combination of the two copolarized and cross‐polarized channels can then be used to derive high‐resolution surface wind estimates. The retrieval methods and impacts of intense rainfall are discussed in the context of a Hurricane Irma (2017) case study. On 7 September 2017, Sentinel‐1 measurements intercepted Hurricane Irma when it was at category 5 intensity. When compared to Stepped Frequency Microwave Radiometer, SAR‐derived wind speeds yield bias and root‐mean‐square of about 1.5 and 5.0 m/s, respectively. The retrieved wind structure parameters for the outer core are found to be in agreement with the Best‐Track and combined satellite‐ and aircraft‐based analyses. SAR measurements uniquely describe the inner core and provide independent measurements of the maximum wind speed and the radius of maximum wind. Near the radius of maximum wind a 65‐m/s increase in wind speed in less than 10 km is detected, corresponding to an instantaneous absolute vorticity of order 210 times the Coriolis parameter. Using a parametric Holland model and the environmental surface pressure (1,011 hPa), SAR‐derived wind speeds correspond to a central surface pressure of 918 hPa (921 hPa from the Best‐Track) in Irma's eye.
Key Points
No saturation of the cross‐polarized C‐band ocean backscattered signal for surface wind speeds up to 75 m/s is observed
Combined copolarization and cross‐polarization are used for ocean surface wind retrieval from Sentinel‐1 and Radarsat‐2 SAR over major hurricanes
Irma category 5 hurricane wind structure is described at high resolution O(1 km) and compared with other existing and independent analysis
Sharing scientific data is an effective means to rationally exploit scientific data and is vital to promote the development of the industrial chain and improve the level of science and technology. In ...recent years, the popularity of the open data platform has increased, but problems remain, including imperfect system architecture, unsound privacy and security, and non-standardized interaction data. To address these problems, the blockchain’s decentralization, smart contracts, distributed storage, and other features can be used as the core technology for open data systems. This paper addresses the problems of opening, allocation-right confirmation, sharing, and rational use of wild-bird data from Yunnan Province, China. A data storage model is proposed based on the blockchain and interstellar file system and is applied to wild-bird data to overcome the mutual distrust between ornithology institutions in the collaborative processing and data storage of bird data. The model provides secure storage and secure access control of bird data in the cloud, thereby ensuring the decentralized and secure storage of wild-bird data for multiple research institutions.
Empathy is significantly influenced by the identification of others’ emotions. In a recent study, we have found increased activation in the anterior insular cortex (aIns) that could be attributed to ...affect sharing rather than perceptual saliency, when seeing another person genuinely experiencing pain as opposed to merely acting to be in pain. In that prior study, effective connectivity between aIns and the right supramarginal gyrus (rSMG) was revealed to represent what another person really feels. In the present study, we used a similar paradigm to investigate the corresponding neural signatures in the domain of empathy for disgust - with participants seeing others genuinely sniffing unpleasant odors as compared to pretending to smell something disgusting (in fact the disgust expressions in both conditions were acted for reasons of experimental control). Consistent with the previous findings on pain, we found stronger activations in aIns associated with affect sharing for genuine disgust (inferred) compared with pretended disgust. However, instead of rSMG we found engagement of the olfactory cortex. Using dynamic causal modeling (DCM), we estimated the neural dynamics of aIns and the olfactory cortex between the genuine and pretended conditions. This revealed an increased excitatory modulatory effect for genuine disgust compared to pretended disgust. For genuine disgust only, brain-to-behavior regression analyses highlighted a link between the observed modulatory effect and a few empathic traits. Altogether, the current findings complement and expand our previous work, by showing that perceptual saliency alone does not explain responses in the insular cortex. Moreover, it reveals that different brain networks are implicated in a modality-specific way when sharing the affective experiences associated with pain vs. disgust.
Empathy for pain engages both shared affective responses and self-other distinction. In this study, we addressed the highly debated question of whether neural responses previously linked to affect ...sharing could result from the perception of salient affective displays. Moreover, we investigated how the brain network involved in affect sharing and self-other distinction underpinned our response to a pain that is either perceived as genuine or pretended (while in fact both were acted for reasons of experimental control). We found stronger activations in regions associated with affect sharing (anterior insula aIns and anterior mid-cingulate cortex) as well as with affective self-other distinction (right supramarginal gyrus rSMG), in participants watching video clips of genuine vs. pretended facial expressions of pain. Using dynamic causal modeling, we then assessed the neural dynamics between the right aIns and rSMG in these two conditions. This revealed a reduced inhibitory effect on the aIns to rSMG connection for genuine pain compared to pretended pain. For genuine pain only, brain-to-behavior regression analyses highlighted a linkage between this inhibitory effect on the one hand, and pain ratings as well as empathic traits on the other. These findings imply that if the pain of others is genuine and thus calls for an appropriate empathic response, neural responses in the aIns indeed seem related to affect sharing and self-other distinction is engaged to avoid empathic over-arousal. In contrast, if others merely pretend to be in pain, the perceptual salience of their painful expression results in neural responses that are down-regulated to avoid inappropriate affect sharing and social support.
Schematic diagram showing direct exchange reactions between the monovalent capping ligand ODA and the mPEG-G2.5-DOX ligand and the conjugates injected to the tumor-bearing mice.
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► ...The novel strategies to prepare PAMAM-Dox-conjugated Iron oxide NPs. ► Conjugates were dual functional nanoparticles for tumor imaging and tumor therapy via the EPR effect. ► It was a pH-controlled release system, which can only release at low pH environment. ► Conjugates contained IONPs which could be used as magnetic resonance imaging contrast agent.
PH-responsive drug release system based on the conjugates of PAMAM dendrimers–doxorubicin (PAMAM–DOX) and superparamagnetic iron oxide (Fe
3O
4) nanoparticles (IONPs) has been constructed and characterized. The IONPs were stabilized by mPEG-G2.5 PAMAM dendrimers. The anticancer drug DOX was conjugated to the dendrimer segments of amino-stabilized IONPs using hydrazine as the linker via hydrazone bonds, which is acid cleavable and can be used as an ideal pH-responsive drug release system. The drug release profiles of DOX–PAMAM dendrimer conjugates were studied at pH 5.0 and 7.4. The results showed that the hydrolytic release profile can be obtained only at the condition of lysosomal pH (pH
=
5.0), and IONPs participated in carrying DOX to the tumor by the Enhanced Permeability and Retention (EPR) effect. These novel DOX-conjugated IONPs have the potential to enhance the effect of MRI contrast and cancer therapy in the course of delivering anticancer drugs to their target sites. Although the dendrimer–DOX-coated IONPs do not have any targeting ligands attached on their surface, they are potentially useful for cancer diagnosis in vivo.
Deep convolutional neural networks have greatly enhanced the semantic segmentation of remote sensing images. However, most networks are primarily designed to process imagery with red, green, and blue ...bands. Although it is feasible to directly utilize established networks and pre-trained models for remotely sensed images, they suffer from imprecise land object contour localization and unsatisfactory segmentation results. These networks still need to explore the domain knowledge embedded in images. Therefore, we boost the segmentation performance of remote sensing images by augmenting the network input with multiple nonlinear spectral indices, such as vegetation and water indices, and introducing a novel holistic attention edge detection network (HAE-RNet). Experiments were conducted on the GID and Vaihingen datasets. The results showed that the NIR-NDWI/DSM-GNDVI-R-G-B (6C-2) band combination produced the best segmentation results for both datasets. The edge extraction block benefits better contour localization. The proposed network achieved a state-of-the-art performance in both the quantitative evaluation and visual inspection.
Deep learning has achieved remarkable performance in semantically segmenting remotely sensed images. However, the high-frequency detail loss caused by continuous convolution and pooling operations ...and the uncertainty introduced when annotating low-contrast objects with weak boundaries induce blurred object boundaries. Therefore, a dual-stream network MAE-BG, consisting of an edge detection (ED) branch and a smooth branch with boundary guidance (BG), is proposed. The ED branch is designed to enhance the weak edges that need to be preserved, simultaneously suppressing false responses caused by local texture. This mechanism is achieved by introducing improved multiple-attention edge detection blocks (MAE). Furthermore, two specific ED branches with MAE are designed to combine with typical deep convolutional (DC) and Codec infrastructures and result in two configurations of MAE-A and MAE-B. Meanwhile, multiscale edge information extracted by MAE networks is fed into the backbone networks to complement the detail loss caused by convolution and pooling operations. This results in smooth networks with BG. After that, the segmentation results with improved boundaries are obtained by stacking the output of the ED and smooth branches. The proposed algorithms were evaluated on the ISPRS Potsdam and Inria Aerial Image Labelling datasets. Comprehensive experiments show that the proposed method can precisely locate object boundaries and improve segmentation performance. The MAE-A branch leads to an overall accuracy (OA) of 89.16%, a mean intersection over union (MIOU) of 80.25% for Potsdam, and an OA of 96.61% and MIOU of 86.63% for Inria. Compared with the results without the proposed edge optimization blocks, the OAs from the Potsdam and Inria datasets increase by 5.49% and 7.64%, respectively.
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