Charging a Stylus using Mobile Device Near Field Communication (NFC) Coil

This disclosure describes techniques to charge a stylus using the existing near field communication (NFC) integrated circuit and coil on the mobile device. The storage slot for a stylus in a case that holds a mobile device is designed such that upon insertion, the stylus NFC coil automatically aligns with the phone NFC coil, thereby enabling charging during storage. Effectively, the storage slot functions as a charging dock for the stylus. Since the storage slot of the phone case is the normal home for the stylus, charging takes place in the background and becomes a seamless experience for the user. A Hall sensor on the main logic board of the mobile device is used to detect the presence of the stylus; no other hardware changes are required. The device NFC mode is automatically configured to allow other NFC functionality while selectively charging the stylus when the battery level of the stylus falls below a threshold

Association of serum lipids with inflammatory bowel disease: a systematic review and meta-analysis

BackgroundSerum lipid levels seem to be abnormal in Inflammatory bowel disease (IBD). However, the specific manifestation of abnormal serum lipid levels in IBD are heterogeneous among studies and have not been sufficiently determined yet.MethodsPubMed, EMBASE, and Cochrane Library databases were searched. Serum lipid levels were compared between IBD patients and Health individuals, Crohn’s (CD) and ulcerative colitis (UC), active and inactive, mild and non-mild patients, respectively. Meta-analyses were performed by using a random-effect model. Weight mean difference (WMD) with 95% confidence intervals (CIs) were calculated.ResultsOverall, 53 studies were included. Compared with healthy controls, IBD patients had significantly lower TC (WMD = −0.506, 95%CI = −0.674 to −0.338, p < 0.001), HDL-c (WMD = −0.122, 95%CI = −0.205 to −0.039, p = 0.004), and LDL-c (WMD = −0.371, 95%CI = −0.547 to −0.194, p < 0.001) levels. CD groups had a significantly lower TC (WMD = −0.349, 95%CI = −0.528 to −0.170, p < 0.0001) level as compared to UC groups. Active IBD and non-mild UC groups had significantly lower TC (WMD = −0.454, 95%CI = −0.722 to −0.187, p = 0.001) (WMD =0.462, 95%CI = 0.176 to 0.748, p = 0.002) and LDL-c (WMD = −0.225, 95%CI = −0.445 to −0.005, p = 0.045) (WMD =0.346, 95%CI = 0.084–0.609, p = 0.010) levels as compared to inactive IBD and mild UC groups, respectively.ConclusionThe overall level of serum lipids in IBD patients is lower than that of healthy individuals and is negatively associated with disease severity.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42022383885

Self-doping effect in confined copper selenide semiconducting quantum dots for efficient photoelectrocatalytic oxygen evolution

Self-doping can not only suppress the photogenerated charge recombination of semiconducting quantum dots by self-introducing trapping states within the bandgap, but also provide high-density catalytic active sites as the consequence of abundant non-saturated bonds associated with the defects. Here, we successfully prepared semiconducting copper selenide (CuSe) confined quantum dots with abundant vacancies and systematically investigated their photoelectrochemical characteristics. Photoluminescence characterizations reveal that the presence of vacancies reduces the emission intensity dramatically, indicating a low recombination rate of photogenerated charge carriers due to the self-introduced trapping states within the bandgap. In addition, the ultra-low charge transfer resistance measured by electrochemical impedance spectroscopy implies the efficient charge transfer of CuSe semiconducting quantum dots-based photoelectrocatalysts, which is guaranteed by the high conductivity of their confined structure as revealed by room-temperature electrical transport measurements. Such high conductivity and low photogenerated charge carriers recombination rate, combined with high-density active sites and confined structure, guaranteeing the remarkable photoelectrocatalytic performance and stability as manifested by photoelectrocatalysis characterizations. This work promotes the development of semiconducting quantum dots-based photoelectrocatalysis and demonstrates CuSe semiconducting quantum confined catalysts as an advanced photoelectrocatalysts for oxygen evolution reaction

Comparison Study of Gold Nanohexapods, Nanorods, and Nanocages for Photothermal Cancer Treatment

Gold nanohexapods represent a novel class of optically tunable nanostructures consisting of an octahedral core and six arms grown on its vertices. By controlling the length of the arms, their localized surface plasmon resonance peaks could be tuned from the visible to the near-infrared region for deep penetration of light into soft tissues. Herein we compare the in vitro and in vivo capabilities of Au nanohexapods as photothermal transducers for theranostic applications by benchmarking against those of Au nanorods and nanocages. While all these Au nanostructures could absorb and convert near-infrared light into heat, Au nanohexapods exhibited the highest cellular uptake and the lowest cytotoxicity in vitro for both the as-prepared and PEGylated nanostructures. In vivo pharmacokinetic studies showed that the PEGylated Au nanohexapods had significant blood circulation and tumor accumulation in a mouse breast cancer model. Following photothermal treatment, substantial heat was produced in situ and the tumor metabolism was greatly reduced for all these Au nanostructures, as determined with ^(18)F-flourodeoxyglucose positron emission tomography/computed tomography (^(18)F-FDG PET/CT). Combined together, we can conclude that Au nanohexapods are promising candidates for cancer theranostics in terms of both photothermal destruction and contrast-enhanced diagnosis

Contribution of Regional PM2.5 Transport to Air Pollution Enhanced by Sub-Basin Topography: A Modeling Case over Central China

The Twain-Hu basin (THB), covering the lower plain of Hubei and Hunan provinces in Central China, has experienced severe air pollution in recent years. However, the terrain effects of such sub-basin on air quality over the THB have been incomprehensibly understood. A heavy PM2.5 pollution event occurred over the THB during 4–10 January 2019. By using the observations and WRF-Chem simulations, we investigated the underlying mechanisms of sub-basin effects on the air pollution with several sensitivity experiments. Observationally, air pollution in the western THB urban area with an average PM2.5 concentration of 189.8 μg m−3, which was more serious than the eastern urban area with the average PM2.5 concentration of 106.3 μg m−3, reflecting a different influence of topography on air pollution over the THB. Simulation results revealed that the terrain effect can contribute 12.0% to increasing the PM2.5 concentrations in the western THB, but slightly mitigate the pollution extent in the eastern THB with the contribution of −4.6% to PM2.5 during the heavy pollution episode. In particular, the sub-basin terrain was conducive to the accumulation of PM2.5 by regional transport with the contribution of 39.1 %, and contrarily lowered its local pollution by −57.0% via the enhanced atmospheric boundary layer height and ventilation coefficients. Given a heavy air pollution episode occurring over the THB, such inverse contribution of terrain effects reflected a unique importance of sub-basin topography in regional transport of air pollutants for air pollution in central China

Adaptive Mean Shift-Based Identification of Individual Trees Using Airborne LiDAR Data

Identifying individual trees and delineating their canopy structures from the forest point clouddataacquiredbyanairborneLiDAR(LightDetectionAndRanging)hassignificantimplications in forestry inventory. Once accurately identified, tree structural attributes such as tree height, crown diameter, canopy based height and diameter at breast height can be derived. This paper focuses on a novel computationally efficient method to adaptively calibrate the kernel bandwidth of a computational scheme based on mean shift—a non-parametric probability density-based clustering technique—to segment the 3D (three-dimensional) forest point clouds and identify individual tree crowns. The basic concept of this method is to partition the 3D space over each test plot into small vertical units (irregular columns containing 3D spatial features from one or more trees) first, by using a fixed bandwidth mean shift procedure and a small square grouping technique, and then rough estimation of crown sizes for distinct trees within a unit, based on an original 2D (two-dimensional) incremental grid projection technique, is applied to provide a basis for dynamical calibration of the kernel bandwidth for an adaptive mean shift procedure performed in each partition. The adaptive mean shift-based scheme, which incorporates our proposed bandwidth calibration method, is validated on 10 test plots of a dense, multi-layered evergreen broad-leaved forest located in South China. Experimental results reveal that this approach can work effectively and when compared to the conventional point-based approaches (e.g., region growing, k-means clustering, fixed bandwidth or multi-scale mean shift), its accuracies are relatively high: it detects 86 percent of the trees (“recall”) and 92 percent of the identified trees are correct (“precision”), showing good potential for use in the area of forest inventory

A practical man-in-the-middle attack on deep learning edge device by sparse light strip injection into camera data lane

The vulnerability of deep neural networks (DNNs) has been exposed by adversarial examples. Although the adversarial perturbations can be made visually imperceptible or photorealistic on any image, they have to be added offline on pre-captured static input in order to accomplish the malicious goal. As opposed to subtle distortion, real-time misclassification on streaming images can be realized by manipulating the objects in physical world. Recently, object-contactless physical attacks, as exemplified by a translucent sticker affixed to the lens of a camera, show that a sensor-enabled edge computing platform can be an alluring target of adversarial attack. Nevertheless, success rates of reported camera-based patch attacks are not high enough to overshadow other forms of evasion attacks even when they are performed under the white-box scenario. In this paper, we present a practical and robust fault injection approach cooperated with a hardware-friendly sparse strip pattern to deceive the deployed DNN device on real-time streaming images. The strip perturbation is generated in a line-offset form by an optimization algorithm. It can be injected into camera data lane between the image sensor and the endpoint node stealthily without disturbing the data traffic through an interface bridge implemented by a tiny off-the-shelf FPGA device. We demonstrate our attack on the Raspberry Pi 4 platform with the Pi camera v2 and the Intel NCS2 inference stick. By evaluating 280 physically captured images from ten objects in 28 viewing angles, we show that the proposed attack on four ImageNet models including ResNet50, MobileNet-v2, Inception-v3 and EfficientNet-B0 can achieve 89.2% ∼ 96.1% success rates.National Research Foundation (NRF)Submitted/Accepted versionThis research is supported by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme/Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: CHFA-GC1-AW01)

Impact of Inter-Regional Transport in a Low-Emission Scenario on PM_2.5 in Hubei Province, Central China

In 2020, when the novel coronavirus disease 2019 (COVID-19) broke out as a global pandemic, cities in Hubei Province first went into lockdown on 23 January and resumed work and production on 20 March. From February to March 2020, human activities in Hubei decreased significantly, with the average particulate matter smaller than 2.5 μm (PM2.5) concentration standing at 40 μg/m3, which is 21% lower than the expected based on a linear fitting trend in thePM2.5 concentration in Hubei. By using the empirical orthogonal function (EOF) method, this paper comparatively analyzes the spatial-temporal variations of Hubei’s PM2.5 concentration anomaly in February and March 2020 and the same periods of 2016–2019. The results show that the daytime peak of the PM2.5 daily variation in Hubei in a low-emission scenario during COVID-19 declined significantly, to which human activities contributed the most. However, during nighttime, the PM2.5 peak became more prominent, and the meteorological conditions had a more noticeable effect on the PM2.5 concentration. In addition, during COVID-19, there was a great drop in PM2.5 pollution accumulated from local sources within the urban circle of Wuhan City, while an increase was seen in central-western Hubei due to the inter-regional pollutant transport. Thus, the high PM2.5 concentration center in the urban circle of Wuhan disappeared, but the pollution transport channel cities in central-western Hubei remained as high-PM2.5-concentration centers

The affective facial recognition task: The influence of cognitive styles and exposure times

The main task of emotional facial recognition is to understand human emotion expression through the recognition of facial expressions, so as to achieve more effective communication and interpersonal communication. Therefore, facial recognition plays an important role in people's daily lives. In addition, the research of facial recognition is also helpful to understand the human perception processing mode, and promote the development of pattern recognition, cognitive science, neural network and other fields. With the development of cognitive science, facial recognition technology has been continuously improved, and emotional facial recognition tasks have received attention in the fields of pattern recognition and artificial intelligence, and have become a research hotspot. Among them, pattern recognition is a cognitive system applied to many fields. For the first time, we confirmed the effects of facial memory time, personal cognitive style, and emotions associated with the target face on facial recognition patterns. This study measured the impact of time, cognitive style, and emotional type of 62 qualified college students. The research results show that cognitive style and facial emotional content are of great significance for face pattern recognition. Specifically, students classified as “dependent” have achieved good results in face pattern recognition, and positive and negative strong emotional faces have left behind those who show neutral emotions. A deeper impression. Finally, an unusual phenomenon was discovered, which indicates that the shorter the time spent on the face of the memory, the higher the recognition score