Various wearable aerogel sensors are emerging for their light weight, fairly wide sensing range, and sensitive sensing ability. Aramid nanofibers (ANFs) as a kind of burgeoning building blocks ...realize multifunctional applications in diversified fields for their innate extinguished mechanical property and thermal stability. Limited by their high insulating property, in this work ANFs were designed to integrate with a 2D emerging MXene sheet with a distinct conductive property. Herein, we report an MXene/ANFs composite aerogel through a feasible controllable vacuum filtration followed by a freeze-drying process. Benefiting from the inerratic 3D hierarchical and “mortar–brick” porous structure with an ultralow density of 25 mg/cm3, MXene/ANFs aerogels are proved to possess high compressible resilience and appealing sensing performance up to 1000 times. Importantly, verified by a series of simulation experiments, the MXene/ANFs aerogel sensor shows a wide detection range (2.0–80.0% compression strain), sensitive sensing property (128 kPa–1), and ultralow detection limit (100 Pa), which still play a flexible role in detecting human light movement and even vigorous sports after undergoing ultrahigh devastating pressures (∼623 kPa). In addition, the MXene/ANFs aerogel sensor can withstand a harsh high temperature of 200 °C and shows excellent flame resistance. The MXene/ANFs aerogel with excellent integrated property, especially the highly sensitive sensing property and excellent thermal stability, presents great potential for a human behavior monitoring sensor and sensing under certain extreme conditions.
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12.
Context-Aware Path Ranking in Road Networks Yang, Sean Bin; Guo, Chenjuan; Yang, Bin
IEEE transactions on knowledge and data engineering,
07/2022, Volume:
34, Issue:
7
Journal Article
Peer reviewed
Open access
Ranking paths becomes an increasingly important functionality in many transportation services, where multiple paths connecting a source-destination pair are offered to drivers. We study ranking such ...paths under specific contexts, e.g., at a departure time and for a specific driver. More specifically, we model ranking as a regression problem where we assign a ranking score to each path with the help of historical trajectories. The intuition is that if a driver's trajectory used path <inline-formula><tex-math notation="LaTeX">P</tex-math> <mml:math><mml:mi>P</mml:mi></mml:math><inline-graphic xlink:href="yang-ieq1-3025024.gif"/> </inline-formula> at time <inline-formula><tex-math notation="LaTeX">t</tex-math> <mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="yang-ieq2-3025024.gif"/> </inline-formula>, we consider this as an evidence that path <inline-formula><tex-math notation="LaTeX">P</tex-math> <mml:math><mml:mi>P</mml:mi></mml:math><inline-graphic xlink:href="yang-ieq3-3025024.gif"/> </inline-formula> is preferred by the driver at time <inline-formula><tex-math notation="LaTeX">t</tex-math> <mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="yang-ieq4-3025024.gif"/> </inline-formula>, thus should have a higher ranking score than other paths connecting the same source and destination. To solve the regression problem, we first propose an effective training data enriching method to obtain a compact and diversified set of training paths using historical trajectories, which provides a data foundation for efficient and effective learning. Next, we propose a multi-task learning framework that considers features representing both candidate paths and contexts. Specifically, a road network embedding is proposed to embed paths into feature vectors by considering both road network topology and spatial properties, such as distances and travel times. By modeling different departure times as a temporal graph, graph embedding is used to embed departure times into feature vectors. The objective function not only considers the discrepancies on ranking scores but also the reconstruction errors of the spatial properties of the paths, which in turn improves the final ranking estimation. Empirical studies on a substantial trajectory data set offer insight into the designed properties of the proposed framework, indicating that it is effective and practical in real world settings.
Petroleum-based plastics are useful but they pose a great threat to the environment and human health. It is highly desirable yet challenging to develop sustainable structural materials with excellent ...mechanical and thermal properties for plastic replacement. Here, inspired by nacre's multiscale architecture, we report a simple and efficient so called "directional deforming assembly" method to manufacture high-performance structural materials with a unique combination of high strength (281 MPa), high toughness (11.5 MPa m
), high stiffness (20 GPa), low coefficient of thermal expansion (7 × 10
K
) and good thermal stability. Based on all-natural raw materials (cellulose nanofiber and mica microplatelet), the bioinspired structural material possesses better mechanical and thermal properties than petroleum-based plastics, making it a high-performance and eco-friendly alternative structural material to substitute plastics.
Direct substitution of readily available alcohols is recognized as a key research area in green chemical synthesis. Starting from simple racemic secondary alcohols, the achievement of catalytic ...enantioconvergent transformations of the substrates will be highly desirable for efficient access to valuable enantiopure compounds. To accomplish such attractive yet challenging transformations, the strategy of the enantioconvergent borrowing hydrogen methodology has proven to be uniquely effective and versatile. This review aims to provide an overview of the impressive progress made on this topic of research that has only thrived in the past decade. In particular, the conversion of racemic secondary alcohols to enantioenriched chiral amines, N-heterocycles, higher-order alcohols and ketones will be discussed in detail.
Enantioconvergent transformations of racemic secondary alcohols to enantioenriched chiral amines, N-heterocycles, higher-order alcohols and ketones through borrowing hydrogen catalysis is covered in this review.
This paper addresses the remote sensing image pan-sharpening problem from the perspective of compressed sensing (CS) theory which ensures that with the sparsity regularization, a compressible signal ...can be correctly recovered from the global linear sampled data. First, the degradation model from a high- to low-resolution multispectral (MS) image and high-resolution panchromatic (PAN) image is constructed as a linear sampling process which is formulated as a matrix. Then, the model matrix is considered as the measurement matrix in CS, so pan-sharpening is converted into signal restoration problem with sparsity regularization. Finally, the basis pursuit (BP) algorithm is used to resolve the restoration problem, which can recover the high-resolution MS image effectively. The QuickBird and IKONOS satellite images are used to test the proposed method. The experimental results show that the proposed method can well preserve spectral and spatial details of the source images. The pan-sharpened high-resolution MS image by the proposed method is competitive or even superior to those images fused by other well-known methods.
Histogram shifting (HS) is a useful technique of reversible data hiding (RDH). With HS-based RDH, high capacity and low distortion can be achieved efficiently. In this paper, we revisit the HS ...technique and present a general framework to construct HS-based RDH. By the proposed framework, one can get a RDH algorithm by simply designing the so-called shifting and embedding functions. Moreover, by taking specific shifting and embedding functions, we show that several RDH algorithms reported in the literature are special cases of this general construction. In addition, two novel and efficient RDH algorithms are also introduced to further demonstrate the universality and applicability of our framework. It is expected that more efficient RDH algorithms can be devised according to the proposed framework by carefully designing the shifting and embedding functions.
In this paper, we propose a scheme to detect the copy-move forgery in an image, mainly by extracting the keypoints for comparison. The main difference to the traditional methods is that the proposed ...scheme first segments the test image into semantically independent patches prior to keypoint extraction. As a result, the copy-move regions can be detected by matching between these patches. The matching process consists of two stages. In the first stage, we find the suspicious pairs of patches that may contain copy-move forgery regions, and we roughly estimate an affine transform matrix. In the second stage, an Expectation-Maximization-based algorithm is designed to refine the estimated matrix and to confirm the existence of copy-move forgery. Experimental results prove the good performance of the proposed scheme via comparing it with the state-of-the-art schemes on the public databases.
Background
Coronavirus disease 2019 (COVID‐19) caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection has been widely spread. We aim to investigate the clinical ...characteristic and allergy status of patients infected with SARS‐CoV‐2.
Methods
Electronic medical records including demographics, clinical manifestation, comorbidities, laboratory data, and radiological materials of 140 hospitalized COVID‐19 patients, with confirmed result of SARS‐CoV‐2 viral infection, were extracted and analyzed.
Results
An approximately 1:1 ratio of male (50.7%) and female COVID‐19 patients was found, with an overall median age of 57.0 years. All patients were community‐acquired cases. Fever (91.7%), cough (75.0%), fatigue (75.0%), and gastrointestinal symptoms (39.6%) were the most common clinical manifestations, whereas hypertension (30.0%) and diabetes mellitus (12.1%) were the most common comorbidities. Drug hypersensitivity (11.4%) and urticaria (1.4%) were self‐reported by several patients. Asthma or other allergic diseases were not reported by any of the patients. Chronic obstructive pulmonary disease (COPD, 1.4%) patients and current smokers (1.4%) were rare. Bilateral ground‐glass or patchy opacity (89.6%) was the most common sign of radiological finding. Lymphopenia (75.4%) and eosinopenia (52.9%) were observed in most patients. Blood eosinophil counts correlate positively with lymphocyte counts in severe (r = .486, P < .001) and nonsevere (r = .469, P < .001) patients after hospital admission. Significantly higher levels of D‐dimer, C‐reactive protein, and procalcitonin were associated with severe patients compared to nonsevere patients (all P < .001).
Conclusion
Detailed clinical investigation of 140 hospitalized COVID‐19 cases suggests eosinopenia together with lymphopenia may be a potential indicator for diagnosis. Allergic diseases, asthma, and COPD are not risk factors for SARS‐CoV‐2 infection. Older age, high number of comorbidities, and more prominent laboratory abnormalities were associated with severe patients.
Decreased eosinophil count, which was positively correlated with lymphocyte counts, may be a potential biological indicator for diagnosing
COVID‐19 patients. Low prevalence of allergic diseases, COPD and patients with smoking history indicated they may not be the predisposing
factors of COVID‐19. Elder age, high number of comorbidities and more prominent laboratory abnormalities were associated with severe
patientsz.
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Designing effective electrocatalysts for the carbon dioxide reduction reaction (CO2RR) is an appealing approach to tackling the challenges posed by rising CO2 levels and realizing a closed carbon ...cycle. However, fundamental understanding of the complicated CO2RR mechanism in CO2 electrocatalysis is still lacking because model systems are limited. We have designed a model nickel single‐atom catalyst (Ni SAC) with a uniform structure and well‐defined Ni‐N4 moiety on a conductive carbon support with which to explore the electrochemical CO2RR. Operando X‐ray absorption near‐edge structure spectroscopy, Raman spectroscopy, and near‐ambient X‐ray photoelectron spectroscopy, revealed that Ni+ in the Ni SAC was highly active for CO2 activation, and functioned as an authentic catalytically active site for the CO2RR. Furthermore, through combination with a kinetics study, the rate‐determining step of the CO2RR was determined to be *CO2−+H+→*COOH. This study tackles the four challenges faced by the CO2RR; namely, activity, selectivity, stability, and dynamics.
Ni‐che reaction: In situ reduction of nickel(II) 2,9,16,23‐tetra(amino)phthalocyanine, anchored on the surface of carbon nanotubes, yields nickel single atoms. Advanced spectroscopy of the single‐atom catalyst reveals that Ni+ is a highly active catalytic site for CO2 activation and reduction.
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This study aimed to investigate the effect of the cell wall component lipoteichoic acid (LTA) of Staphylococcus aureus on mammary alveolar tight junctions (TJs) proteins including claudin-1, occludin ...and zonula occluden (ZO)-1. Primary bovine mammary epithelial cells were grown on Transwell inserts for 108 hours. The integrity and tightness of the growing epithelial cell layer were evaluated by measuring transepithelial electrical resistance. The permeability of bovine mammary epithelial cells (BMECs) was assessed by measuring horseradish peroxidase transmission. The mRNA levels of BMECs TJ components were measured with quantitative real-time polymerase chain reaction. In LTA-induced mastitis of mouse model, the protein expression of TJs (claudin-1, occludin and ZO-1) were determined by western blot analysis. Our results showed that LTA increased barrier permeability of BMECs. Treatment with LTA decreased mRNA and protein levels of occludin and ZO-1 in vivo and in vitro, however, the expression of claudin-1 did not change. The results suggested that disruption of mammary epithelial barrier integrity is caused by the alteration of occludin and ZO-1.
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