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BackgroundInflammation plays an essential role in the pathogenesis of hypertension. A novel inflammatory biomarker systemic inflammatory response index (SIRI) is related with all-cause and cardiovascular (CVD) mortality, while the role of SIRI in hypertension patients is unclear.MethodsA total of 21,506 participants with hypertension were recruited in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. SIRI was calculated as the neutrophil count * monocyte count/lymphocyte count. Hypertension was defined according to the examination of blood pressure, prescription, and self-reported physician diagnosis. Survival status was followed through 31 December 2019. The non-linear relationship was assessed using restricted cubic spline analysis. The association of all-cause mortality with SIRI was evaluated using the Kaplan–Meier curve and the weighted Cox regression analysis. The predictive abilities were assessed with Receiver operating curve.ResultsDuring 189,063 person-years of follow-up, 5,680 (26.41%) death events were documented, including 1,967 (9.15%) CVD related deaths. A J-shaped association was observed between SIRI and all-cause and CVD mortality. The Kaplan–Meier curve indicated the all-cause and CVD mortality risks were higher in high SIRI quartiles compared with lower SIRI quartiles. After adjusting for all covariates, the SIRI was positively associated with the all-mortality risk with HR = 1.19 (1.15, 1.22), and CVD mortality with HR = 1.19 (1.15, 1.24). The result was robust in subgroup analysis and sensitivity analysis.ConclusionElevated SIRI level is associated with increased all-cause and CVD mortality among patients with hypertension. SIRI is considered as a potential inflammatory biomarker in the clinical practice. Further large-scale cohort studies are required to confirm our findings.

Smart local energy system (SLES) can support tailored regional solutions through the orchestration of cyber physical architectures, coordinating distributed technologies, with operational and forecasting models across all energy actors. Unprecedented access to new information, data streams and remotely accessible control can substantially benefit the multi-objective optimisation of multiple performance metrics. Given the expansion of this internet of things (IoT) and cyber-physical system (CPS), it is important to not only design effective detection and management of potential cybersecurity issues, but also to address the challenges in having affective and adaptive governance—built on standards to ensure the security of the IoT to minimise risks and harms to all users. This study conducts an extensive and critical investigation into the existing standards and identifies areas to focus on as to support the expansive adoption of cyber physical networks. Although existing standards and protocols are highly fragmented, our findings suggest that many of them can meet the requirements of the applications and infrastructures of SLES. Additionally, many standards have been introduced to protect information security and personal privacy due to their increasing importance. The research also suggests that the industry needs to produce more affordable and cyber-secured devices and services. For the government and regulators, relevant guidelines on the minimum function and security requirements for applications should be provided. Additionally, compliance testing and certifications should be in place and carried out by an independent third party to ensure the components of SLES ecosystem with a satisfied security level by design.

High-quality MRI reconstruction plays a critical role in clinical applications. Deep learning-based methods have achieved promising results on MRI reconstruction. However, most state-of-the-art methods were designed to optimize the evaluation metrics commonly used for natural images, such as PSNR and SSIM, whereas the visual quality is not primarily pursued. Compared to the fully-sampled images, the reconstructed images are often blurry, where high-frequency features might not be sharp enough for confident clinical diagnosis. To this end, we propose an invertible sharpening network (InvSharpNet) to improve the visual quality of MRI reconstructions. During training, unlike the traditional methods that learn to map the input data to the ground truth, InvSharpNet adapts a backward training strategy that learns a blurring transform from the ground truth (fully-sampled image) to the input data (blurry reconstruction). During inference, the learned blurring transform can be inverted to a sharpening transform leveraging the network's invertibility. The experiments on various MRI datasets demonstrate that InvSharpNet can improve reconstruction sharpness with few artifacts. The results were also evaluated by radiologists, indicating better visual quality and diagnostic confidence of our proposed method., Comment: Accepted by MICCAI 2022

We present Neural Contact Fields, a method that brings together neural fields and tactile sensing to address the problem of tracking extrinsic contact between object and environment. Knowing where the external contact occurs is a first step towards methods that can actively control it in facilitating downstream manipulation tasks. Prior work for localizing environmental contacts typically assume a contact type (e.g. point or line), does not capture contact/no-contact transitions, and only works with basic geometric-shaped objects. Neural Contact Fields are the first method that can track arbitrary multi-modal extrinsic contacts without making any assumptions about the contact type. Our key insight is to estimate the probability of contact for any 3D point in the latent space of object shapes, given vision-based tactile inputs that sense the local motion resulting from the external contact. In experiments, we find that Neural Contact Fields are able to localize multiple contact patches without making any assumptions about the geometry of the contact, and capture contact/no-contact transitions for known categories of objects with unseen shapes in unseen environment configurations. In addition to Neural Contact Fields, we also release our YCB-Extrinsic-Contact dataset of simulated extrinsic contact interactions to enable further research in this area. Project page: https://github.com/carolinahiguera/NCF, Comment: 2023 International Conference on Robotics and Automation (ICRA)

Smart local energy system (SLES) is considered as a promising pathway facilitating a more effective and localised operation, benefited from the complex information and communication technology (ICT) infrastructures and Internet of things (IoT) technologies. As a part of the critical infrastructure, it is important to not only put effective detection and management to tackle potential cybersecurity issues, but also require considerable numbers of standards to ensure the security of the internet of things system to minimise the risks. This study aims to review the existing standards, investigate how the compatibility with SLES development, and identify the area to focus on in the future. Although existing standards and protocols are highly fragmented, our findings suggest that many of them can meet the requirements of the applications and infrastructures of SLES. Additionally, many standards have been introduced to protect information security and personal privacy due to their increasing importance. The research also suggests that the industry needs to produce more affordable and cyber-secured devices and services. For the government and regulators, relevant guidelines on the minimum function and security requirements for applications should be provided. Additionally, compliance testing and certifications should be in place and carried out by an independent third party to ensure the components of SLES ecosystem with a satisfied security level by design.

This work presents a new version of the tactile-sensing finger GelSlim 3.0, which integrates the ability to sense high-resolution shape, force, and slip in a compact form factor for use with small parallel jaw grippers in cluttered bin-picking scenarios. The novel design incorporates the capability to use real-time analytic methods to measure shape, estimate the contact 3D force distribution, and detect incipient slip. To achieve a compact integration, we optimize the optical path from illumination source to camera and other geometric variables in a optical simulation environment. In particular, we optimize the illumination sources and a light shaping lens around the constraints imposed by the photometric stereo algorithm used for depth reconstruction. The optimized optical configuration is integrated into a finger design composed of robust and easily replaceable snap-to-fit fingetip module that allow for ease of manufacture, assembly, use, and repair. To stimulate future research in tactile-sensing and provide the robotics community access to reliable and easily-reproducible tactile finger with a diversity of sensing modalities, we open-source the design and software at https://github.com/mcubelab/gelslim.

Discharge in liquid has been studied in many forms and water is the mostly used liquid in those researches. In this paper, alcohol aqueous solution is used to form stable discharging. A kilo-Hz alternating current high voltage power source is used to generate plasma under the surface of liquid and the power frequency set is the range of 1~20 kilo-Hz. The voltage-current curve, optical emission spectrum and many other evidences show that the discharging in liquid is intermittent with high optic effect.

In this paper, we propose an approach to detect incipient slip, i.e. predict slip, by using a high-resolution vision-based tactile sensor, GelSlim. The sensor dynamically captures the tactile imprints of the contact object and their changes with a soft gel pad. The method assumes the object is mostly rigid and treats the motion of object's imprint on sensor surface as a 2D rigid-body motion. We use the deviation of the true motion field from that of a 2D planar rigid transformation as a measure of slip. The output is a dense slip field which we use to detect when small areas of the contact patch start to slip (incipient slip). The method can detect both translational and rotational incipient slip without any prior knowledge of the object at 24 Hz. We test the method on 10 objects 240 times and achieve 86.25% detection accuracy. We further show how the slip feedback can be used to monitor the gripping force to avoid slip with a closed-loop bottle-cap screwing and unscrewing experiment with incipient slip detection feedback. The method was demonstrated to be useful for the robot to apply proper gripping force and stop screwing at the right point before breaking objects. The method can be applied to many manipulation tasks in both structured and unstructured environments.

Taking Lake Taihu which was the third largest shallow lake in China as an example,we researched the demarcation of buffer zone on the basis of information collection,topographic survey and field investigation.On domestic and international literatures about buffer zones of lakes,we preliminarily defined approximately 2 km wide as the width of Lake Taihu buffer zone.Considering the principles of land markers(villages,roads,mountains)and regional differences of regulations and planning,we defined the upper and lower boundaries of the buffer zone.The lower boundary was at the lake shoreline or levees around the lake.The upper boundary was at approximately 2km from the lower boundary.According to land use types,there were three typical types of villages,roads and mountains in Lake Taihu buffer zone.The upper boundary at village area was divided by the administrative villages about 2.0 km from the lakeshore,and was divided by roads when the roads are paralleled with the lake shores.At mountain area,the upper boundary was divided by mountain ridges.On the 1∶5000 topographic maps,we calculated 382.75 km in total length and 452.31 km2 in the total area of Lake Taihu buffer zone by means of Auto CAD software.