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Despite a great deal of effort spanning for decades, it remains yet puzzling concerning how alcohol molecules functionalize the hydrogen bond (H-bond) networks of water. We employed an isotopic substitution method (using alcohol-heavy water system) to avoid spectral overlap between the alcohol hydroxyl groups and water hydrogen bonds. We showed spectrometrically that under the strong pulse laser, the low mixing ratio (VA < 20%) of alcohol can strengthen the H-bond network structure of D2O through :ÖC2H6↔ D2Ö: compression. But when VA > 20%, H-bond network of D2O will deform via the self-association between alcohol molecules. Our experiments not only reveal the H-bond kinetics of heavy water-alcohol interactions but also provide important reference for understanding the distinctive properties of H-bond in water-organic system.

The utilization of combined heat and power (CHP) units is a key way to enhance renewable energy accommodation. However, carbon emissions of CHP challenge the pollution and economic optimization of integrated energy system (IES). To tackle this challenge, we propose a novel model and optimal dispatch for CHP with power-to-gas (P2G) and carbon capture system (CCS), which solves the problems of the carbon source required for P2G and the CHP's carbon emissions by the optimal dispatch in IES. The model takes the CHP, P2G, and CCS as a whole system. The operation rule of the model is developed, and its coupling characteristics of electric power, heat power, gas power and carbon are analyzed. Correspondingly, the optimization strategy of IES considers the proposed model and carbon trading mechanism. The optimal dispatch model of IES is established and solved by YALMIP, and GUROBI. Our method is verified by simulation. Compared with other conventional models, the accommodation capacity of renewable energy with the proposed method is enhanced, and the carbon emissions and operating costs of IES are also reduced.

The stability of solidified shell thickness plays an important role in continuous casting. In this paper, 220mm × 260mm section continuous casting blank of an electric furnace company is taken as the research object. Under the condition of solidification process, the solidification shell thickness is simulated by the physical model established and ANSYS software. At the same time, according to the calculation formula of solidified shell thickness, combined with the specific data location of the designed and measured solidified shell thickness test, the good match between the experimental calculation and the model prediction is achieved. It provides a reliable basis for following F-EMS optimization and essential data sustain for the transformation and promotion of solidified structure of the blank under light pressure soft reduction.

Cloud computing can improve the calculation and data storage ability for the control center in the power system. A new framework of the cloud-based control center is proposed in this paper. This cloud computing system can collect the load data from smart meters of the grid and classify demand-side management (DSM) loads that meet the specific requirements. The selected loads belong to the off-peak period (from 21:00 to 07:00 next day) and can contribute to shifting the night peak load. A feature extraction combined with Variational Mode Decomposition (FE-VMD) of the loads which can be trained in recurrent neural network (RNN) is proposed in this paper. Using the feature value to replace the actual load data, input data can be significantly reduced which is suitable for a vast amount of load in the power system. A case study of real load data from 200,000 customers has been classified with this method, and the accuracy is compared with the other methods. From simulation with MATLAB, it can be seen that the FE-VMD combined with the RNN method provides the best result of 89.8% recognition accuracy among these methods.

The MoN/VN multilayer films with three modulation periods (Λ) were deposited on 718 alloy steel substrates by magnetron sputtering. The effects of Λ on microstructure, mechanical properties, and friction performance of the MoN/VN multilayer films at variously typical temperatures were systematically investigated. The results indicated that the cross-section of as-prepared films were all typically columnar structure, and the hardness and elastic modulus of the MoN/VN multilayer films increase with the reduction of the modulation period. The MoN/VN multilayer films with Λ = 22.66 nm showed the maximum H/E value of 0.052 among all the as-prepared films. In addition, the MoN/VN multilayer films exhibited better tribological properties under room temperature (25 ℃) and 700 °C due to the formation of slight wear and self-lubricating oxide films, respectively. Especially, the friction coefficient and wear rate of the MoN/VN multilayer films with Λ = 22.66 nm were as low as 0.29 and 1.37 × 10–6 mm3/Nm at 700 °C, respectively. Diagrams of worn surface of the MoN/VN multilayer films with Λ = 22.66 nm and Λ = 4.77 nm at different tests temperature: (a, e) RT, (b, f) 300 °C, (c, g) 500 °C and (d, h) 700 °C.

We introduced a locked-energy coherent Stokes radiation scheme based on multi-mode stimulated Raman scattering (SRS) of aqueous sulfuric acid solutions. SRS of pure sulfuric acid has only one vibrational characteristic peak. Mixing sulfuric acid with water leads to the appearance of bisulfate, and the energy transfer between adjacent vibrational energy levels in a 17.0 mol/l aqueous sulfuric acid solution leads to generation of five different vibrational modes in a solution. The competition between these five SRS modes prevents the vibrational energy from being transferred to high-order Stokes waves, thereby locking most of the energy in the first-order Stokes SRS and outputting a very strong, broadband, and coherent Raman radiation ranging from 555 to 570 nm. This study demonstrates the possibility of producing an intense single longitudinal mode laser.

The SARS-CoV-2 virus has had a major impact on global human health. During the spread of SARS-CoV-2, weakened host immunity and the use of vaccines with low efficacy may result in the development of more virulent strains or strains with resistance to existing vaccines and antibodies. The prevalence of SARS-CoV-2 mutant strains differs among regions, and this variation may affect the effectiveness of vaccines. In this study, an epidemiological investigation of SARS-CoV-2 in Portugal was performed, and the VSV-ΔG-G* pseudovirus system was used to construct 12 S protein epidemic mutants, D614G, A222V+D614G, B.1.1.7, S477N+D614G, P1162R+D614G+A222V, D839Y+D614G, L176F+D614G, B.1.1.7+L216F, B.1.1.7+M740V, B.1.258, B.1.258+L1063F, and B.1.258+N751Y.The mutant pseudoviruses were used to infect four susceptible cell lines (i.e., Huh7, hACE2-293T, Vero, and LLC-MK2) and 14 cell lines overexpressing ACE2 from different species. Mutant strains did not show increased infectivity or cross-species transmission. Neutralization activity was evaluated using the newly constructed pseudoviruses, mouse serum, and 11 monoclonal antibodies. The neutralizing activity in immunized mouse serum was not significantly reduced for the mutant strains. Additionally, mutant strains in Portugal showed escape from 9 of 11 monoclonal antibodies. Neutralization resistance was mainly caused by the S477N, N439K, and N501Y mutations in the Spike receptor binding domain. These findings emphasize the importance of SARS-CoV-2 mutation tracking in different regions for epidemic prevention and control.

The eruption of a pandemic, such as COVID-19, can cause an unprecedented global crisis. Contact tracing, as a pillar of communicable disease control in public health for decades, has shown its effectiveness on pandemic control. Despite intensive research on contact tracing, existing schemes are vulnerable to attacks and can hardly simultaneously meet the requirements of data integrity and user privacy. The design of a privacy-preserving contact tracing framework to ensure the integrity of the tracing procedure has not been sufficiently studied and remains a challenge. In this paper, we propose P2B-Trace, a privacy-preserving contact tracing initiative based on blockchain. First, we design a decentralized architecture with blockchain to record an authenticated data structure of the user's contact records, which prevents the user from intentionally modifying his local records afterward. Second, we develop a zero-knowledge proximity verification scheme to further verify the user's proximity claim while protecting user privacy. We implement P2B-Trace and conduct experiments to evaluate the cost of privacy-preserving tracing integrity verification. The evaluation results demonstrate the effectiveness of our proposed system.

To deal with the pollution of lead ions (Pb2+) in water, sodium alginate (SA) thick films prepared with a sol-gel preparation method were modified with sulfhydryl, addressing the issue (lead-ion pollution) with the optimized adsorbing capacity (416.1 mg·g−1), which can far surpass the pure SA films (222.0 mg·g−1). Through adsorption kinetics analysis and thermodynamic analysis, we find that the adsorption process is an exothermic process that follows both physical adsorption (pseudo-first-order) and chemisorption (pseudo-second-order). More surprisingly, the films can adsorb lead ions in weak acid environments and at room temperature, giving rise to the possibility of addressing related issues under universal conditions.

The molybdenum nitride (MoN) film with multiphase structure (Mo 2 N, MoN and Mo) was successfully prepared using magnetron sputtering. The mechanical and tribological performances of the MoN film were both further improved by C-doping and heat treatment. The results show that compared to the MoN film, the MoCN film has smoother and denser microstructure, lower friction coefficient and wear rate, and an increased hardness of 28 GPa. The improved performance exhibited by the MoCN film is mainly attributed to the lubricating effect of forming amorphous carbon after doping with C element and high hardness of the sp 3 C C hybrid bond. However, different results exhibited on the MoN and MoCN films after heat treatment at 400 °C. Both the mechanical and tribological properties were improved in the MoN film but reduced in the MoCN film. This may be related to different crystallization of Mo and the change of amorphous carbon in the different film. This suggests that heat treatment is of great importance to the properties of MoN and MoCN films.

In order to improve the service performance of key mechanical parts in high humidity and high vacuum environments, the MoS2/Sn composite films with different Sn contents were prepared by magnetron sputtering technique. The morphology, composition and structure of the as-deposited films were investigated by SPM, SEM, XRD and XPS. The mechanical and tribological properties of MoS2/Sn composite films in different humidity and vacuum environments were investigated. The results show that Sn-doping changes the crystal structure of MoS2, increases the hardness and elastic modulus, and reduces the oxidation activity of the MoS2 films. Especially, the MoS2/Sn composite film with optimized Sn content of 7.2 at.% has better tribological properties compared with pure MoS2 films in a high humidity of 75% RH and high vacuum environments.

MoS2 is easily oxidized into MoO3 at elevated temperature, which leads to a sharp deterioration of lubricity and greatly limits the application. To improve the atmospheric tribology properties of MoS2 at elevated temperature, the co-deposited MoS2–W composite films are prepared by magnetron sputtering. Compared with pure MoS2, the design of three kinds of MoS2–W composite films not only maintains lower temperature-sensitivity, but also greatly improves the mechanical properties. In particular, the three MoS2–W composite films embrace good tribology performance at all test temperature, especially 100 °C, at which physical adsorption of H2O is prominently less than that of 25 °C and the oxidation is relatively slight, compared with that of the higher temperature. Individually, the MoS2-8.2%W composite film maintains the optimal tribology performance at any test temperature and becomes self-adaptive to temperature variation, which originates from the optimized structure and good mechanical properties.

Purpose: To investigate the disease-causing gene in a Chinese family with Leber congenital amaurosis 4 (LCA4). Materials and methods: Four members of an LCA family underwent ophthalmological examination and systemic assessment. DNA samples were obtained from their peripheral blood. Whole exome sequencing (WES) was performed in the two patients. After data filtering, Sanger sequencing was performed to verify the mutation within this family. Results: The two patients were diagnosed as having LCA4 and with keratoconus (KCN). The older brother also has intellectual disability, epilepsy, Tourette syndrome and an abnormal gait, while the younger one has an abnormal bulge at the end of his sternum. A novel p.Gln81* mutation in the AIPL1 gene was determined as causing LCA4 in this family. Protein structure change prediction showed AIPL1 p.Gln81* mutation coded a very short AIPL1 peptide and could not form a normal structure as an normal AIPL1 protein. Conclusion: Although KCN has been associated with LCA4, this type of LCA is typically moderate in severity and variable between patients. The present cases also have some systemic abnormalities.

Organocatalyzed ring-opening polymerization (ROP) of cyclic esters was developed into a stage that demanding translation of the academic success into commercially viable production. An industrial relevant development of the organocatalysis in ROPs required bulk polymerizations at high temperatures up to 180 °C. Organic phosphoric acid was one of the few organocatalysts workable under these conditions that devoid of otherwise commonly appeared transesterification, epimerization, and decarboxylation in the ROPs. A commercial dibutyl phosphate (DBuP), which was inexpensive and available by hundreds of tons as antistatic agent, was firstly evaluated as a catalyst. Well-defined polyesters catalyzed by DBuP were obtained with narrow dispersities (Đm 1.08–1.31) and predicted molecular weights from 2.1 to 55 kg mol−1. Series of homopolymers, diblock copolymers and end-functionalized polymers of e-caprolactone (CL), δ-valerolactone (VL), trimethylene carbonate (TMC), and l -lactide (LA) were synthesized. 1H NMR, 13C NMR, SEC, and MALDI–ToF MS analyses indicated that the obtained polyesters were exactly the designated ones. NMR titration experiments confirmed that the ROP proceeded a bifunctional activation mechanism. The cytotoxicity test using MTT assay showed biosafety of the polyesters. The experimental results suggested that dibutyl phosphate is a commercial organocatalyst widely useful in all major cyclic ester monomer toward commercial relevant ring-opening polymerizations.

H-bond donor (HBD) and base co-catalysis achieved great success in ring-opening polymerization (ROP). Parallel H-bond donor and Bronsted acid (HBD–BA) cocatalysis has been scarcely explored in ROPs. In developing the HBD–BA catalysis in to versatile toolbox for ring-opening polymerizations of wider scope and better control, tunable HBDs including boronate pinacol ester urea (BU), urea (U), and thiourea (TU) were evaluated in cooperation with trifluoroacetic acid (TFA) in HBD–BA catalytic mode. Each of the HBD–TFA co-catalyst promoted ROPs of δ-valerolactone (VL) and e-caprolactone (CL) successfully. The catalytic performances of the HBD–TFA on the HBD part was in an order of BU > U > TU to their corresponding ROPs of VL and CL. The turnover frequencies of the catalyzed ROPs of VL and CL by BU–TFA was twice as that of TFA. The ROPs afforded homopolymers with predicted molecular weights (PVL, Mn,NMR = 2.51–9.92 kg mol−1; PCL, Mn,NMR = 2.51–11.1 kg mol−1) and narrow dispersities (PVL, Đ ≤ 1.16; PCL, Đ ≤ 1.18) by ratios of monomer to initiator from 20 to 100. The obtained polymers were analyzed by 1H NMR, 13C NMR, and MALDI-ToF MS, the results indicated the high chain-end fidelity. Chain extension experiments were conducted by a first ROP of VL/CL and second ROP of CL/VL with precise molecular weights (from 2.98 kg mol−1 to 6.15 kg mol−1) and narrow dispersities below 1.18. The kinetics plots showed a distinct first order relationship between ln([VL]0/[VL]) versus the reaction time, it indicated the controlled nature of the catalytic ROP. NMR titrations supported the proposed mechanism that HBD associated with TFA and promoted the cationic polymerization.

CrSiN coatings with various Si contents were prepared on 304L substrate through medium frequency magnetron sputtering. Toughness, corrosion resistance and tribology property of as-prepared coatings in 0.1 M HCl solution were studied. The result showed that the toughness of CrN coating was effectively enhanced by the incorporation of proper silicon. Compared with other coatings, the CrSiN coating with high silicon proportion presented lower icorr and higher |Z|0.01 Hz. When the silicon proportion exceeded 16.0 at.%, the inhibition efficiency was up to 99.10%. During tribology test, the COF and wear rate descended at the early stage and then ascended as the silicon proportion increased as a whole, which was ascribed to the combined action between toughness, anti-corrosion and lubrication behaviors.

The H-bridge cascaded active power filter (APF) has broad application prospect in the harmonic procession of high voltage field. Due to differences between H bridges, the imbalance of DC side voltages affects not only compensation effect, but also the safe and stable operation of APF. By derivation of the power exchange model between APF and the power grid, the voltage balance among phases is achieved by the five-order zero-sequence voltage, and the global voltage balance is achieved by the fundamental positive-sequence active current. By appending an active voltage vector in the AC side of each H-bridge, the voltage balance is achieved within the single phase. This three-level equilibrium strategy controls each DC side voltage at the given value eventually. At the same time, the cascaded APF compensates harmonic and reactive current and reduces THD of the grid current. The feasibility of the method is verified by simulation and experimental results under low voltage conditions and provides the basis for application of the APF to a higher voltage level.

In a wind-diesel system with weak network architecture, many techniques are utilized to enhance its stability. However, the frequency oscillation between the grid-side converter and the diesel generator may be caused by their interactions, and there are only a few studies considering this issue, which is a major challenge for the stability. To tackle this challenge, the cause of frequency oscillation is studied, and an adaptive fuzzy PI controller based on the variable universe is proposed to suppress oscillation in this paper. The control parameter of the wind turbine generator (WTG) converter's dc-link voltage loop and wind speed has an important influence on the oscillation. Our main idea is to suppress frequency oscillation by the optimizing control parameter. In order to analyze the interaction between WTG and diesel generator, we develop the small-signal models of the wind-diesel generator. Subsequently, the adaptive fuzzy PI controller based on the variable universe is proposed in the dc-link voltage loop to optimize control parameter instead of gain-scheduled PI controller, and its key feature is the innovative use of an extension factor with d-axis current in the output universe. The function of the extension factor is to adapt WTG to different wind speeds. The experimental results demonstrate that the proposed controller can significantly suppress frequency oscillation. The oscillation amplitude of isolated grid frequency is also significantly reduced by up to 0.175 Hz.

The spike protein of SARS-CoV-2 has been undergoing mutations and is highly glycosylated. It is critically important to investigate the biological significance of these mutations. Here, we investigated 80 variants and 26 glycosylation site modifications for the infectivity and reactivity to a panel of neutralizing antibodies and sera from convalescent patients. D614G, along with several variants containing both D614G and another amino acid change, were significantly more infectious. Most variants with amino acid change at receptor binding domain were less infectious, but variants including A475V, L452R, V483A, and F490L became resistant to some neutralizing antibodies. Moreover, the majority of glycosylation deletions were less infectious, whereas deletion of both N331 and N343 glycosylation drastically reduced infectivity, revealing the importance of glycosylation for viral infectivity. Interestingly, N234Q was markedly resistant to neutralizing antibodies, whereas N165Q became more sensitive. These findings could be of value in the development of vaccine and therapeutic antibodies.

Ten emerging SARS-CoV-2 variants—B.1.1.298, B.1.1.7, B.1.351, P.1, P.2, B.1.429, B.1.525, B.1.526-1, B.1.526-2, B.1.1.318—and seven corresponding single amino acid mutations in the receptor-binding domain were examined using SARS-CoV-2 pseudovirus. The results indicate that the current SARS-CoV-2 variants do not increase infectivity among humans. The K417N/T, N501Y, or E484K-carrying variants exhibited increased abilities to infect to mouse ACE2-overexpressing cells. The activities of Furin, TMPRSS2, and cathepsin L were increased against most of the variants. RBD amino acid mutations comprising K417T/N, L452R, Y453F, S477N, E484K, and N501Y caused significant immune escape from 11 of 13 monoclonal antibodies. However, the resistance to neutralization by convalescent serum or vaccines was mainly caused by the E484K mutation, while the neutralization of E484K-carrying variants was decreased by 1.1–6.2-fold. The convalescent serum from B.1.1.7- and B.1.351-infected patients neutralized the variants themselves better than other SARS-CoV-2 variants.

Blockchain has emerged as a promising solution for secure data storage and retrieval for decentralized applications. To scale blockchain systems, a prevailing approach is to employ a hybrid storage model, where only small meta-data are stored on-chain while the raw data are outsourced to an off-chain storage service provider. The key issue for query processing in such a system is the design of gas-efficient authenticated data structure (ADS) to authenticate the query results. In this paper, we study novel ADS schemes for authenticated keyword search in hybrid-storage blockchains. We first propose the Suppressed Merkle inverted (Merkleinv) index, which maintains only a partial ADS structure on-chain that can be securely updated with a logarithm-sized cryptographic proof. Moreover, we propose a Chameleon inverted (Chameleoninv) index that leverages the chameleon vector commitment to achieve a constant maintenance cost. It is further optimized with Bloom filters to enhance the query and verification performance. We prove the security of the proposed ADS schemes and evaluate their performance using real datasets on the Ethereum platform. Experimental results show that, compared to a baseline solution, the proposed Merkleinv and Chameleoninv indexes reduce the average on-chain maintenance cost from US$10.39 down to US$2.50 and US$0.24, respectively, without sacrificing much the query performance.

Background Grassroots CDC laboratory efficiency can reflect and influence a country's capacity for disease control and prevention, improving the efficiency of the grassroots CDC laboratory is very important to national CDC system, this article is based on DEA method, on the basis of clearly defining the concept of efficiency, more methods to build grassroots CDC laboratory efficiency evaluation index, taking China as an example, the grassroots CDC laboratory efficiency study, discusses the current problems in the development of grassroots CDC laboratory operation efficiency. Methods Using data from China National Health Development Research Center, combined with some public data from National Bureau of Statistics and China Health Statistics Yearbook data, use data envelopment analysis (DEA) to analyze the operation of China's grassroots CDC disease control laboratories from 2017 to 2019 Efficiency, input-output improvement value and total factor productivity changes. Results From 2017 to 2019, the operational efficiency of grass-roots CDC laboratories in China showed a downward trend, and there was a serious problem of insufficient output, especially the grass-roots CDC laboratories in western China, which had the lowest operational efficiency. By analyzing the change of total factor productivity, it is found that the total factor productivity of grass-roots CDC laboratories in China generally shows a downward trend, and the technical level and management efficiency have declined to a certain extent, and the working ability of CDC laboratories has not been effectively improved. Conclusions The operational efficiency of grass-roots CDC laboratories is declining, and the technical level and management efficiency are declining. The unreasonable utilization of laboratory resources and unbalanced development are still outstanding, and the operational efficiency of laboratories needs to be further developed. Policymakers should pay attention to the imbalance of resource utilization and development of grass-roots CDC laboratories, give certain policy support to inferior CDC laboratories, and urge grass-roots CDC laboratories to strengthen management, improve technical level and management efficiency, increase the actual output of laboratories, and further enhance the working ability of grass-roots CDC laboratories.

The SARS-CoV-2 variant VUI/202012/01 has been reported to spread unexpectedly fast in the United Kingdom. It is estimated that its transmissibility may increase by 70%. In this study, the top five variants circulating in the UK including D614G+L18F+A222V, D614G+A222V, D614G+S477N, VUI/202012/01 and D614G+69-70del+439K were analyzed for their infective and neutralizing characteristics. The pseudotyped viruses were constructed for the five variants and 12 single mutants composed those variants. We found that the VUI/202012/01 variant does enhance its infectivity due to the cumulative effect of multiple mutations including 69-70del and 144/145del mutations in NTD, A570D in RBD, and S982A in S2. Meanwhile, mutations N501Y, N439K and S477N in RBD can cause a significant decrease in the neutralization activity for some mAbs. Although VUI/202012/01 did not affect the neutralization effect of convalescent sera, it affected the neutralization activity of animal immunized sera by RBD protein or recombinant spike DNA to some extent.

The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented health crisis for the global. Digital contact tracing, as a transmission intervention measure, has shown its effectiveness on pandemic control. Despite intensive research on digital contact tracing, existing solutions can hardly meet users’ requirements on privacy and convenience. In this paper, we propose \(\mathsf {BU}\)-\(\mathsf {Trace}\), a novel permissionless mobile system for privacy-preserving intelligent contact tracing based on QR code and NFC technologies. First, a user study is conducted to investigate and quantify the user acceptance of a mobile contact tracing system. Second, a decentralized system is proposed to enable contact tracing while protecting user privacy. Third, an intelligent behavior detection algorithm is designed to ease the use of our system. We implement \(\mathsf {BU}\)-\(\mathsf {Trace}\) and conduct extensive experiments in several real-world scenarios. The experimental results show that \(\mathsf {BU}\)-\(\mathsf {Trace}\) achieves a privacy-preserving and intelligent mobile system for contact tracing without requesting location or other privacy-related permissions.

Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test system (IEEE RTS &lsquo, 96 standard). The generation capacity states and the risk model were obtained using the sequential Monte Carlo simulation (MCS) method. The load was gradually increased stepwise and is simulated against the constant generation capacity. In each case, the reliability index was recorded in terms of loss-of-load evaluation (LOLE). The recorded reliability index was thereafter fitted with the load-growth trend by the linear regression approach. A predictive assessment approach is thereafter proffered through the obtained fitting equation. In addition, a reliability threshold is effectively determined at a yield point for a reliability benchmark.

Zero-day Web attacks are arguably the most serious threats to Web security, but are very challenging to detect because they are not seen or known previously and thus cannot be detected by widely-deployed signature-based Web Application Firewalls (WAFs). This paper proposes ZeroWall, an unsupervised approach, which works with an existing WAF in pipeline, to effectively detecting zero-day Web attacks. Using historical Web requests allowed by an existing signature-based WAF, a vast majority of which are assumed to be benign, ZeroWall trains a self-translation machine using an encoder-decoder recurrent neural network to capture the syntax and semantic patterns of benign requests. In real-time detection, a zero-day attack request (which the WAF fails to detect), not understood well by self-translation machine, cannot be translated back to its original request by the machine, thus is declared as an attack. In our evaluation using 8 real-world traces of 1.4 billion Web requests, ZeroWall successfully detects real zero-day attacks missed by existing WAFs and achieves high F1-scores over 0.98, which significantly outperforms all baseline approaches.

The volatility of wind power generations could significantly challenge the economic and secure operation of combined electricity and heat networks. To tackle this challenge, this paper proposes a framework of optimal dispatch with distributed electric heating storage based on a correlation-based long short-term memory prediction model. The prediction model of distributed electric heating storage is developed to model its behavior characteristics which are obtained by the auto-correlation and correlation analysis with external factors including weather and time-of-use price. An optimal dispatch model of combined electricity and heat networks is then formulated and resolved by a constraint reduction technique with clustering and classification. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the mean absolute percentage error with the proposed correlation-based long short-term memory can be reduced by 1.009 and 0.481 respectively. Compared with conventional method, the peak wind power curtailment with dispatching distributed electric heating storage is reduced by nearly 30% and 50% in two cases respectively.

This demonstration presents vChain, a blockchain system that ensures query integrity. With the proliferation of blockchain applications and services, there has been an increasing demand for querying the data stored in a blockchain database. However, existing solutions either are at the risk of losing query integrity, or require users to maintain a full copy of the blockchain database. In comparison, by employing a novel verifiable query processing framework, vChain enables a lightweight user to authenticate the query results returned from a potentially untrusted service provider. We demonstrate its verifiable query operations, usability, and performance with visualization for better insights. We also showcase how users can detect falsified results in the case that the service provider is compromised.

Multi-elements hyperaccumulators are of high scientific interest to be applied in remediation of mix-contaminated soils. Sedum alfredii is a well-known Cd/Zn co-hyperaccumulator with high Pb and Cu tolerance. This study investigated the ability of the hyperaccumulating ecotype (HE) S. alfredii to tolerate and accumulate Ni. Differed from the non-hyperaccumulating ecotype (NHE), HE plants grew healthy after 50 μM Ni exposure for 4 weeks. The HE plants translocated up to 40 % Ni to the shoots under high Ni stress and accumulated >3000 and 200 mg kg-1 Ni in roots and shoots, respectively. Micro-XRF image showed that Ni was highly restricted within the HE stem and leaf vascular bundles, especially the xylem tissues. The HE roots were of high Ni tolerance, showing much less pronounced Ni-induced phytotoxicity as compared with the NHEs. Ni-induced O2- was observed in the apoplastic part of HE root cells, but both Ni and the induced O2- were highly accumulated in the sensitive zone (root cap, meristem, and cylinder) of NHE roots. These results suggest that although low Ni mobility out of vascular tissues limits the metal accumulation in stems and leaves, HE S. alfredii is highly tolerant towards Ni stress by metal homeostasis in root cells.

Recently, stacked networks show powerful performance in Image Restoration, such as challenging motion deblurring problems. However, the number of stacking levels is a hyper-parameter fine-tuned manually, making the stacking levels static during training without theoretical explanations for optimal settings. To address this challenge, we leverage the iterative process of the traditional plug-and-play method to provide a dynamic stacked network for Image Restoration. Specifically, a new degradation model with a novel update scheme is designed to integrate the deep neural network as the prior within the plug-and-play model. Compared with static stacked networks, our models are stacked dynamically during training via iterations, guided by a solid mathematical explanation. Theoretical proof on the convergence of the dynamic stacking process is provided. Experiments on the noise dataset BSD68, Set12, and motion blur dataset GoPro demonstrate that our framework outperforms the state-of-the-art in terms of PSNR and SSIM score without extra training process.

The CrN-Ag composite coatings with various silver contents were prepared on 316L stainless steel by magnetron sputtering. The corrosion performance and antifouling properties (including anti-algae and antibacterial behaviors) of coatings in the artificial seawater were evaluated to give insight to potential marine applications. Results revealed that Ag-doped coatings exhibited worse corrosion resistance with increased silver, which indicated silver accelerated the reaction of the primary battery and promoted the occurrence of corrosion. However, opposite tendency was presented on anti-algae behavior. The coatings containing silver content of 13.18 at.% showed excellent anti-algae properties, and better antibacterial properties against Bacillus subtilis than Escherichia coli. The attachment of Chlorella, N. closterium and P. tricornutum on the surface of coating reduces by about 45%, 72% and 64%, respectively. The agglomerate of silver in the coatings with 18.37 at.% constrains its release and diffuse, which slowed down the corrosion reaction, embodying positive influence on corrosion resistance and negative effect on anti-algae behavior.

Typical organocatalyst promoted ring-opening polymerization (ROP) by activation of monomer or polymer chain end via the acidic or basic mechanism. Dual activating of both the monomer and the propagating polymer chain end suggested an ideal strategy of widely tuneable catalysis of high efficiency. Stoichiometric Bronsted acid–base adduct was rarely employed in ROPs since general Bronsted acid and Bronsted base quench each other. We proposed that an acid–base adduct may work bifunctionally in catalysis. The cationic part of the acid–base adduct as H-bond donor activated the monomer, and the counter anion part as H-bond acceptor activated initiator/chain end. A series of guanidiniums, derived from commercially available Bronsted acids and guanidine, were employed as catalysts in ROPs of carbonate, lactide, and lactone. Guanidinium triazabicyclodecene hydrochloride exhibited outstanding catalytic performances in the respect of the rate and controlled in the ROPs of l -lactide, trimethylene carbonate, and δ-valerolactone with nearly full conversions, predicted molecular weights, as well as narrow dispersity of Đ = 1.07. These experimental results indicated that guanidinium is an efficient organocatalyst in controlled/living ring-opening polymerization.

Catechol moiety offers a versatile platform in the preparation of functionalized polymers, but it is not usually compatible with catalysis in polymerizations. To address these challenges, we suggest employment of one Brønsted base in masking the activity of catechol moiety and to modulate the polymerization. Based on this strategy, the ring-opening polymerization (ROP) of sarcosine N-carboxyanhydrides (Sar-NCA) was carried out using dopamine hydrochloride as an initiator and triethylamine as a Brønsted base. PSar with predicted molecular weights (M n,NMR=3.7 kg mol−1) and narrow dispersities (ĐN-butyl glycine N-carboxyanhydrides (Bu-Gly-NCA) from the PSar in one-pot produced catechol end-functionalized amphiphilic polysarcosine-block-poly(N-butyl glycine) diblock copolypeptoids (cat-PSar-b-PGlyBu). Further, cat-PSar-b-PGlyBu enabled the aqueous dispersion of manganese oxide nanoparticles which was attributable to the anchor of the diblock copolymers onto the surface of the nanoparticles. The strategy for catechol masking and polymerization mediating by one Brønsted base offered a new avenue into the synthesis of catechol-ended block copolymers.

Podocyte injury is an early pathological change characteristic of various glomerular diseases, and apoptosis and F‐actin cytoskeletal disruption are typical features of podocyte injury. In this study, we found that adriamycin (ADR) treatment resulted in typical podocyte injury and repressed plectin expression. Restoring plectin expression protected against ADR‐induced podocyte injury whereas siRNA‐mediated plectin silencing produced similar effects as ADR‐induced podocyte injury, suggesting that plectin plays a key role in preventing podocyte injury. Further analysis showed that plectin repression induced significant integrin α6β4, focal adhesion kinase (FAK) and p38 MAPK phosphorylation. Mutating Y1494, a key tyrosine residue in the integrin β4 subunit, blocked FAK and p38 phosphorylation, thereby alleviating podocyte injury. Inhibitor studies demonstrated that FAK Y397 phosphorylation promoted p38 activation, resulting in podocyte apoptosis and F‐actin cytoskeletal disruption. In vivo studies showed that administration of ADR to rats resulted in significantly increased 24‐hour urine protein levels along with decreased plectin expression and activated integrin α6β4, FAK, and p38. Taken together, these findings indicated that plectin protects podocytes from ADR‐induced apoptosis and F‐actin cytoskeletal disruption by inhibiting integrin α6β4/FAK/p38 pathway activation and that plectin may be a therapeutic target for podocyte injury‐related glomerular diseases.

In view of the low sensitivity, high operating temperature and poor selectivity of acetone measurements, in this paper much effort has been paid to improve the performance of acetone sensors from three aspects: increasing the surface area of the material, improving the surface activity and enhancing gas diffusion. A hierarchical flower-like Pt-doped (1 wt %) 3D porous SnO2 (3DPS) material was synthesized by a one-step hydrothermal method. The micropores of the material were constructed by subsequent annealing. The results of the experiments show that the 3DPS-based sensor's response is strongly dependent on temperature, exhibiting a mountain-like response curve. The maximum sensor sensitivity (Ra/Rg) was found to be as high as 505.7 at a heating temperature of 153 °C and with an exposure to 100 ppm acetone. Additionally, at 153 °C, the sensor still had a response of 2.1 when exposed to 50 ppb acetone gas. The 3DPS-based sensor also has an excellent selectivity for acetone detection. The high sensitivity can be explained by the increase in the specific surface area brought about by the hierarchical flower-like structure, the enhanced surface activity of the noble metal nanoparticles, and the rapid diffusion of free-gas and adsorbed gas molecules caused by the multiple channels of the microporous structure.

This paper proposes a cyber–physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber–physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers’ behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber–physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively.

With the rapid increase of grid-connected capacity of wind turbines in China, wind power consumption is challenged. Especially, the three northern areas' wind curtailment is up to 95% of the whole country. In order to improve the capacity of wind power consumption, our country encourage the technology of clean energy heating. Electrical heating with heat storage is one of the most popular methods of clean energy heating. However, the analysis of the heat storage load is not adequate by the method, because of the huge power grid data, poor data quality and the inaccurate extraction of heat storage load. Aiming the problems, a method based on K-means, K-nearest neighbor algorithm (KNN), and support vector machine algorithm (SVM) is proposed for characteristic extraction of heat storage load in power system. K-means is used to cluster data and extract heat storage load as the training cluster. KNN and SVM are used to train the training cluster and extract the heat storage load. Take the heat storage load in a certain area in Liaoning province as an example. Compared with the traditional methods, the extraction method is more accurate. Then the extracted heat storage data are classified and analyzed. It is of great practical significance to improve the consumption of wind power and improve power quality reasonably.