Membranes which allow fast and selective transport of protons and cations are required for a wide range of electrochemical energy conversion and storage devices, such as proton-exchange membrane (PEM) fuel cells (PEMFCs) and redox flow batteries (RFBs). Herein we report a new approach to designing solution-processable ion-selective polymer membranes with both intrinsic microporosity and ion-conductive functionality. Polymers are synthesized with rigid and contorted backbones, which incorporate hydrophobic fluorinated and hydrophilic sulfonic acid functional groups, to produce membranes with negatively charged subnanometer-sized confined ionic channels. The ready transport of protons and cations through these membranes, and the high selectivity towards nanometer-sized redox-active molecules, enable efficient and stable operation of an aqueous alkaline quinone redox flow battery and a hydrogen PEM fuel cell.
Due to the continuous improvement of traffic analysis technology, traditional covert channels have become insecure and vulnerable to human sabotage. Blockchain technology has the characteristics of immutability and anonymity, making covert communication more unmonitored and robust. However, it also brings about low communication efficiency. In this article, we adopt the idea of transaction rounds and propose for the first time the construction of HMAC values order (HVO) scheme. Furthermore, we further propose a HMAC values and transaction matrices (HV-TM) scheme to improve communication efficiency. This article is the first to use the gas field to embed data to improve the embedding rate. Use the random numbers generated by the Mersenne Twister algorithm to disrupt the order of addresses to improve the concealment of reused addresses. Experiments have shown that the two schemes have higher communication efficiency and better embedding rate than existing schemes.
Background: Novel coronavirus pneumonia (NCP) is often changing rapidly and fatal. Early detection and early triage of coronavirus disease 2019 (Covid-19) is the key to success management of the disease. An easily obtainable yet accurate variable for both diagnosis and prognosis is urgently needed. We aim to report predictive Value of the Neutrophil-to-Lymphocyte Ratio(NLR) for diagnosis and worse clinical course of the COVID-19, which have not been well demonstrated. Methods: Our study consisted of two stages, at the first stage, a retrospective, single-center, cohort study including was conducted in Heilongjiang, on admission, demographic, clinical, and laboratory data were collected and compared between patients with COVID-19 and patients with non COVID-19; we used multivariable logistic regression methods to explore the risk factors associated with COVID-19;A receiver operating characteristic(ROC) analysis was conducted to calculate the area under the curve(AUC) to assess predictive value of NLR for diagnosis of COVID 19. At the second stage, we conducted retrospective, multi-center and large sample study in 43 hospitals from ten provinces of China, COVID-19 patients with laboratory-confirmed divided into three groups including mild cases, ordinary cases and severe cases. Multivariate logistic regression methods were used to identify the risk factors for the deterioration of COVID-19, along with, a receiver operating characteristic (ROC) curve was also drawn to assess impact on the clinical course of the COVID-19. Findings: We recruited a total of 635 patients with COVID-19 and 27 cases with non COVID-19(Viral pneumonia) from 28 January to 25 February. A total of 88 cases were enrolled with a retrospective, single-center, cohort study from Heilongjiang province, of these, COVID-19 cases were 61(69%) and non COVID-19 cases were 27(31%). On admission, fever (69%) was the most common symptoms, cough (56%) and fatigue(53%). An average(SD) of NLR of COVID-19 patients and non- COVID-19 patients were3.48±2.04 and 2.21±1.14, respectively. multivariable regression showed increasing odds of COVID-19 patients associated with NLR(odds ratio 1.752, 95% CI 1.111-2.763, per 1 unit increase; p=0.016). In addition, the area under the curve (AUC) of NLR was 0.707 and cutoff value was 2.22. At the second stage, 635 patients with COVID were enrolled with a retrospective, multi-center, large sample study in the 43 settings from 10 provinces, of these, mild case were 86(14%), ordinary cases [486(76%)],severe cases[63(10%)], common symptoms was at onset of disease were cough[356(56%)], an average of NLR of 635 patients was 4.04±4.68, and elevated NLR with the deterioration of clinical course[mild case(2.73±2.28), ordinary cases(3.58±3.07), severe cases(9.38±10.52), P<0.0001], in multivariable logistic regression model, compared to mild group, fever(OR 5.739, 95% CI 2.849-11.564) and cough(OR 3.265, 95% CI 1.675-6.331) were associated with ordinary cases, increasing odds of NLR was associated with ordinary cases (OR 1.199, 95% CI 1.010-1.422, per 1 unit increase; p=0.038). Fever(OR 7.587, 95% CI 2.601- 22.132),cough(OR 6.493, 95% CI 2.257-18.682) and shortness of breath(OR 4.133, 95% CI 1.125-15.179) were associated with severe cases, increasing odds of NLR (OR 1.342, 95% CI 1.122-1.605, per 1 unit increase; p=0.001), and increasing odds of age (OR 1.036, 95% CI 1.001- 1.073, per 1 unit increase; p=0.041) were associated with severe cases. The area under the curve (AUC) of NLR was 0.727 and cutoff value was 4.06, additionally, AUC of lymphocytes was 0.719 and cutoff value was 0.765. Interpretation: NLR as inflammatory markers with rapid, convenient characteristics, NLR≥2.22 could be utilized as a predicting indicator for the early recognition COVID-19 and facilitate detection timely; meanwhile, NLR≥4.06 and lymphocytes≤0.765 were as predicting indicator for severe COVID-19 and can facilitate to further prevent worse progress of clinical course. In addition, cough, shortness of breath, abnormal chest radiological findings were also associated with worse outcome.Funding Statement: The study was supported by" National Science and Technology Major Project" (2018ZX10101001-005-003, 2018ZX10101001-005-004).Declaration of Interests: All authors declare that they have no competing interests.Ethics Approval Statement: The study was approved by National Administration of Traditional Chinese Medicine, Administration of Traditional Chinese Medicine of 10 provinces and the institutional board of 43 participating setting.
Abstract Magnetic soft materials have been extensively utilized in the development of soft robots. However, they usually fail to achieve programmable soft‐hard magnetic transformations, limiting multimodal magnetoactive deformation and locomotion. Herein, a coaxial magnetic fiber (CMF) consisting of a porous polydimethylsiloxane (p‐PDMS) sheath and a core of sodium alginate sol (SAS) mixed with neodymium‐iron‐boron (NdFeB) particles (NdFeB@SAS) is reported, fabricated using intermittent coaxial 3D printing. The CMF can reversibly transition between a hard magnetic state (coercivity H cm 6900 Oe and remanence M r 61 emu g −1 ) and a soft magnetic state ( H cm 450 Oe and M r 16 emu g −1 ) through the solvent exchange strategy, achieving a ≈15‐fold change in coercivity and a ≈4‐fold increase in remanence. Three CMF‐based samples are constructed and programmed with varying soft‐hard magnetic profiles, exhibiting multimodal magnetoactive deformation. Moreover, under a fluctuating magnetic field, a bionic butterfly robot can flap its wings over a vertical branch without falling. A magnetic brush is also constructed to paint origami objects using magnetic actuation. These demonstrations underscore that the responsiveness of the CMF to global magnetic fields can be locally tailored, allowing for the coexistence of attraction and repulsion in various regions of the CMF, providing a novel approach for developing soft robots.
Automatic lip-reading is a technique of understanding the uttered speech by visually interpreting the lip movement of the speaker. with the development of the lip-reading, more and more related technologies are proposed. However, the current research of lip-reading is mainly conducted under the ideal lighting conditions and there is few researchers focus on the lip-reading technique under variant lighting conditions. For this problem, this paper proposes a new method of lip feature extraction under variant lighting conditions. The method consists of a preprocessing chain of illumination normalization and improved LBP features, which can improve the recognition rate of lip-reading under variant lighting conditions from two aspects. Experiments show that the lip feature extraction method proposed by this paper is effective.
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