PNC Enabled IIoT: A General Framework for Channel-Coded Asymmetric Physical-Layer Network Coding

This paper investigates the application of physical-layer network coding (PNC) to Industrial Internet-of-Things (IIoT) where a controller and a robot are out of each other's transmission range, and they exchange messages with the assistance of a relay. We particularly focus on a scenario where the controller has more transmitted information, and the channel of the controller is stronger than that of the robot. To reduce the communication latency, we propose an asymmetric transmission scheme where the controller and robot transmit different amount of information in the uplink of PNC simultaneously. To achieve this, the controller chooses a higher order modulation. In addition, the both users apply channel codes to guarantee the reliability. A problem is a superimposed symbol at the relay contains different amount of source information from the two end users. It is thus hard for the relay to deduce meaningful network-coded messages by applying the current PNC decoding techniques which require the end users to transmit the same amount of information. To solve this problem, we propose a lattice-based scheme where the two users encode-and-modulate their information in lattices with different lattice construction levels. Our design is versatile on that the two end users can freely choose their modulation orders based on their channel power, and the design is applicable for arbitrary channel codes.Comment: Submitted to IEEE for possible publicatio

Combating Multi-path Interference to Improve Chirp-based Underwater Acoustic Communication

Linear chirp-based underwater acoustic communication has been widely used due to its reliability and long-range transmission capability. However, unlike the counterpart chirp technology in wireless -- LoRa, its throughput is severely limited by the number of modulated chirps in a symbol. The fundamental challenge lies in the underwater multi-path channel, where the delayed copied of one symbol may cause inter-symbol and intra-symbol interfere. In this paper, we present UWLoRa+, a system that realizes the same chirp modulation as LoRa with higher data rate, and enhances LoRa's design to address the multi-path challenge via the following designs: a) we replace the linear chirp used by LoRa with the non-linear chirp to reduce the signal interference range and the collision probability; b) we design an algorithm that first demodulates each path and then combines the demodulation results of detected paths; and c) we replace the Hamming codes used by LoRa with the non-binary LDPC codes to mitigate the impact of the inevitable collision.Experiment results show that the new designs improve the bit error rate (BER) by 3x, and the packet error rate (PER) significantly, compared with the LoRa's naive design. Compared with an state-of-the-art system for decoding underwater LoRa chirp signal, UWLoRa+ improves the throughput by up to 50 times

Electrocatalytic Oxygen Reduction Using Metastable Zirconium Suboxide

Strategies for discovery of high-performance electrocatalysts are important to advance clean energy technologies. Metastable phases such as low temperature or interfacial structures that are difficult to access in bulk may offer such catalytically active surfaces. We report here that the suboxide Zr3O, which is formed at Zr−ZrO2 interfaces but does not appear in the experimental Zr−O phase diagram exhibits outstanding oxygen reduction reaction (ORR) performance surpassing that of benchmark Pt/C and most transition metal-based catalysts. Addition of Fe3C nanoparticles to give a Zr−Zr3O−Fe3C/NC catalyst (NC=nitrogen-doped carbon) gives a half-wave potential (E1/2) of 0.914 V, outperforming Pt/C and showing only a 3 mV decrease after 20,000 electrochemical cycles. A zinc-air battery (ZAB) using this cathode material has a high power density of 241.1 mW cm−2 and remains stable for over 50 days of continuous cycling, demonstrating potential for practical applications. Zr3O demonstrates that interfacial or other phases that are difficult to stabilize may offer new directions for the discovery of high-performance electrocatalysts.</p

Anomalous bond softening mediated by strain-induced Friedel-like oscillations in a BC2N superlattice

The crystal structure of BC2N and the origin of its superhardness remain under constant debate, hindering its development. Herein, by evaluating the x-ray diffraction pattern, the thermodynamic stability at normal and high pressures of a series of BC2N candidates, the (111) BC2N2x2 superlattice (labeled R2u-BC2N) is identified as the realistic crystal structure of the experimentally synthesized BC2N. We further reveal that the strain-induced Friedel-like oscillations dominates the preferable slip systems of R2u-BC2N by drastically weakening the heterogenous bonds across the slip plane and thus leads to its ultralow dislocation slip resistance, which originates from the metallization triggered by the reduction in energy separation between bonding and antibonding interactions of the softened bonds. Our results rule out R2u-BC2N as the intrinsic superhard material surpassing c-BN, whereas the experimentally determined extreme hardness can be attributed to the nanocrystalline grains glued by interfacial amorphous carbon which provides a strong barrier for plastic deformation. These findings provide a view of the longstanding issue of the possible structure of experimentally observed BC2N, and establish a mechanism underlying the strain-driven electronic instability of superlattice structures, providing guidance towards rational design of superhard materials.Web of Science1066art. no. L06010

EyelashNet: A Dataset and A Baseline Method for Eyelash Matting

Eyelashes play a crucial part in the human facial structure and largely affect the facial attractiveness in modern cosmetic design. However, the appearance and structure of eyelashes can easily induce severe artifacts in high-fidelity multi-view 3D face reconstruction. Unfortunately it is highly challenging to remove eyelashes from portrait images using both traditional and learning-based matting methods due to the delicate nature of eyelashes and the lack of eyelash matting dataset. To this end, we present EyelashNet, the first eyelash matting dataset which contains 5,400 high-quality eyelash matting data captured from real world and 5,272 virtual eyelash matting data created by rendering avatars. Our work consists of a capture stage and an inference stage to automatically capture and annotate eyelashes instead of tedious manual efforts. The capture is based on a specifically-designed fluorescent labeling system. By coloring the eyelashes with a safe and invisible fluorescent substance, our system takes paired photos with colored and normal eyelashes by turning the equipped ultraviolet (UVA) flash on and off. We further correct the alignment between each pair of photos and use a novel alpha matte inference network to extract the eyelash alpha matte. As there is no prior eyelash dataset, we propose a progressive training strategy that progressively fuses captured eyelash data with virtual eyelash data to learn the latent semantics of real eyelashes. As a result, our method can accurately extract eyelash alpha mattes from fuzzy and self-shadow regions such as pupils, which is almost impossible by manual annotations. To validate the advantage of EyelashNet, we present a baseline method based on deep learning that achieves state-of-the-art eyelash matting performance with RGB portrait images as input. We also demonstrate that our work can largely benefit important real applications including high-fidelity personalized avatar and cosmetic design

Nitrogen-Doped Indium Oxide Electrochemical Sensor for Stable and Selective NO₂ Detection

Efficient gas sensors are critical for environmental monitoring and industrial safety. While metal oxide semiconductor (MOS) sensors are cost-effective, they struggle with poor selectivity, high operating temperatures, and limited stability. Electrochemical sensors, though selective and energy-efficient, face high costs, and stability issues due to precious metal catalysts like platinum on carbon (Pt/C). Herein, a novel, cost-effective electrochemical sensor using nitrogen-doped indium oxide In2O3−xN2x/3Vx/3 (0.01≤x≤0.14), synthesized with varying nitriding times is presented. The optimized In2O3 N-40 min sensor demonstrates a remarkable response current of 771 nA to 10 ppm nitrogen dioxide (NO2) at ambient temperature, with outstanding long-term stability (over 30 days) and rapid response/recovery times (5/16 s). Compared to Pt/C sensors, it shows 84% and 67% reductions in response and recovery times, respectively, and maintains 98% performance after a month, versus 68% for Pt/C. This cost-effective sensor presents a promising alternative for electrochemical gas sensing, eliminating the need for precious metal catalysts.</p

Recent Advances of SnO2-Based Sensors for Detecting Fault Characteristic Gases Extracted From Power Transformer Oil

Tin oxide SnO2-based gas sensors have been widely used for detecting typical fault characteristic gases extracted from power transformer oil, namely, H2, CO, CO2, CH4, C2H2, C2H4, and C2H6, due to the remarkable advantages of high sensitivity, fast response, long-term stability, and so on. Herein, we present an overview of the recent significant improvement in fabrication and application of high performance SnO2-based sensors for detecting these fault characteristic gases. Promising materials for the sensitive and selective detection of each kind of fault characteristic gas have been identified. Meanwhile, the corresponding sensing mechanisms of SnO2-based gas sensors of these fault characteristic gases are comprehensively discussed. In the final section of this review, the major challenges and promising developments in this domain are also given

Basic Reproduction Number of Enterovirus 71 and Coxsackievirus A16 and A6: Evidence From Outbreaks of Hand, Foot, and Mouth Disease in China Between 2011 and 2018.

BACKGROUND: Enterovirus 71 (EV-A71), coxsackievirus A16 (CV-A16), and coxsackievirus A6 (CV-A6) are common serotypes causing hand, foot, and mouth disease (HFMD). Analyses on the basic reproduction number (R0) of common pathogens causing HFMD are limited and there are no related studies using field data from outbreaks in mainland China. METHODS: We estimated the pathogen-specific basic reproduction number based on laboratory-confirmed HFMD outbreaks (clusters of ≥10 HFMD cases) reported to the national surveillance system between 2011 and 2018. The reproduction numbers were calculated using a mathematical model and the cumulative cases during the initial growth periods. RESULTS: This study included 539 outbreaks, of which 198 were caused by EV-A71, 316 by CV-A16, and 25 by CV-A6. All 10 417 cases involved were children. Assuming the outbreaks occurred in closed systems and the incubation period is 5 days, the median (interquartile range [IQR]) R0 estimates of EV-A71, CV-A16, and CV-A6 were 5.06 (2.81, 10.20), 4.84 (3.00, 9.00), and 5.94 (3.27, 10.00). After adjusting for seroprevalences, the R0 (IQR) estimates for EV-A71, CV-A16 (optimistic and conservative scenarios), and CV-A6 were 12.60 (7.35, 25.40), 9.29 (6.01, 19.20), 15.50 (9.77, 30.40), and 25.80 (14.20, 43.50), respectively. We did not observe changes in the R0 of EV-A71 after vaccine licensure (P = .67). CONCLUSIONS: HFMD is highly transmissible when caused by the 3 most common serotypes. In mainland China, it primarily affects young children. Although a vaccine became available in 2016, we have not yet observed any related changes in the disease dynamics

Accuracy of epidemiological inferences based on publicly available information: retrospective comparative analysis of line lists of human cases infected with influenza A(H7N9) in China

Background: Appropriate public health responses to infectious disease threats should be based on best-available evidence, which requires timely reliable data for appropriate analysis. During the early stages of epidemics, analysis of ‘line lists’ with detailed information on laboratory-confirmed cases can provide important insights into the epidemiology of a specific disease. The objective of the present study was to investigate the extent to which reliable epidemiologic inferences could be made from publicly-available epidemiologic data of human infection with influenza A(H7N9) virus. Methods: We collated and compared six different line lists of laboratory-confirmed human cases of influenza A(H7N9) virus infection in the 2013 outbreak in China, including the official line list constructed by the Chinese Center for Disease Control and Prevention plus five other line lists by HealthMap, Virginia Tech, Bloomberg News, the University of Hong Kong and FluTrackers, based on publicly-available information. We characterized clinical severity and transmissibility of the outbreak, using line lists available at specific dates to estimate epidemiologic parameters, to replicate real-time inferences on the hospitalization fatality risk, and the impact of live poultry market closure. Results: Demographic information was mostly complete (less than 10% missing for all variables) in different line lists, but there were more missing data on dates of hospitalization, discharge and health status (more than 10% missing for each variable). The estimated onset to hospitalization distributions were similar (median ranged from 4.6 to 5.6 days) for all line lists. Hospital fatality risk was consistently around 20% in the early phase of the epidemic for all line lists and approached the final estimate of 35% afterwards for the official line list only. Most of the line lists estimated >90% reduction in incidence rates after live poultry market closures in Shanghai, Nanjing and Hangzhou. Conclusions: We demonstrated that analysis of publicly-available data on H7N9 permitted reliable assessment of transmissibility and geographical dispersion, while assessment of clinical severity was less straightforward. Our results highlight the potential value in constructing a minimum dataset with standardized format and definition, and regular updates of patient status. Such an approach could be particularly useful for diseases that spread across multiple countries