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For ultra-large-scale offshore wind bases, to achieve cost-effective and efficient transmission from offshore wind farms (OWFs) to distant load centers, this study proposes a transmission system that integrates multiple offshore low-frequency AC (LFAC) links and onshore high-voltage DC (HVDC) link. To integrate between multiple offshore LFAC links and onshore HVDC link, low-frequency flexible line commutation converter (LF-FLCC) is introduced, which comprises of LCC and low-frequency auxiliary MMC (A-MMC) in series connection. The LF-FLCC offers benefits of low cost, compactness, and DC fault clearance capability by utilizing LCC, while attains flexibility through A-MMC such as AC voltage support, DC power regulation, and active compensation. The LF-FLCC also possesses independent black-start capability through an auxiliary diode rectifier (A-DR). Through voltage boost and energy conversion by the LF-FLCC, the offshore wind power can be aggregated onto the DC bus, facilitating a continuous transmission to load centers through onshore HVDC links economically and efficiently. Based on proposed system, this study proposes the normal operation, black-start scheme, and DC fault clearance strategy. Besides, this paper analyzes the capacity requirement of the A-MMC. The effectiveness of the proposed system is verified in Matlab/Simulink using a scenario of a 10 GW large-scale offshore wind power base.

An environmentally friendly electrochemical strategy for the synthesis of SCF3-containing oxindoles was developed. This electrochemical transformation was accomplished through a cascade trifluoromethylthiolation/cyclization of N-acrylamides with AgSCF3, obviating the requirement for external oxidants. A variety of functional groups were well tolerated in this transformation.

In light ice-covered areas, icing of composite insulators with different pollution degrees has a great influence on their insulation performance. In this article, the solid layer pollution method was adopted to realize different degrees of pre-pollution for six kinds of FXBW-220/160 composite insulators. Then, with the reference ice thickness of 10 mm as the standard, icing and ac flashover tests were carried out on insulators under the multifunctional artificial climate chamber. The ice weight and minimum flashover voltage ( ${U}_{\textit {mf}}{)}$ of insulators under different pollution degrees were obtained, and the path of the flashover arc was recorded. In addition, the maximum withstand voltage gradient equation of insulators under different discharge probabilities was established. The results show that when insulators are energized icing, the ice weight decreases with the increase of salt deposit density (SDD), which in turn causes the ${U}_{\textit {mf}}$ to decrease. The relationship between the ${U}_{\textit {mf}}$ and SDD is a negative power function regardless of whether the insulators are energized icing or not. The differences in arc path, residual ice layer resistance, and the ratio of air gap arc to ice surface arc are the main reasons for affecting the flashover voltage. In addition, the maximum withstand voltage gradient ${E}_{\alpha }$ at discharge probability $\alpha $ % of 10%, 5%, and 0.13% when the insulators are energized icing decreases with the increase of SDD, and ${E}_{{10}} > {E}_{{5}} > {E}_{{0}.{13}}$ . Increasing the spacing and diameter of the largest shed of composite insulators and reasonably cooperating with medium/small sheds can help to significantly improve their flashover performance. The results of this article can provide data support for the selection and optimal design of insulators for 220 kV transmission lines in light ice-covered areas.

Correlating dynamic structural transformation of catalysts with the surface intermediate species under operating conditions is critical for updating the understanding of structure–performance relationships. Here, we probe the electrochemical potential-dependent surface structures and adsorbed intermediates on PtRu binary alloy nanocatalysts to revisit its synergistic mechanisms for CO electrooxidation enhanced activity. In-situspectral characteristics by using modified shell-isolated nanoparticle-enhanced Raman spectroscopy, show that in acidic solution, when the potential is positively scanned from 0.1 V to 1.5 V relative to reversible hydrogen electrode (RHE), the surface of the alloy catalyst evolves from metallic PtRu to adsorbed oxygen gradually covering and accumulating on Ru sites (denoted as PtRuOx, x⩽2), forming segregated RuO2, and finally forming a three-dimensional oxide layer (denoted as 3D PtRuO4). Moreover, molecular evidence associated with periodic density functional theory calculations reveals that electronic effects promote ruthenium to become more oxidizable and oxophilic. In particular, we found here that *O and *OH at surface RuOxsites are highly efficient CO oxidation active species in comparison to the same entities adsorbed on metallic Ru sites. This work sheds light on the complex surface dynamic process of practical Pt-based binary nanocatalysts and improves the understanding of synergistic mechanism for the development of fuel cell devices.

Brain functional networks model the brain's ability to exchange information across different regions, aiding in the understanding of the cognitive process of human visual attention during target searching, thereby contributing to the advancement of camouflage evaluation. In this study, images with various camouflage effects were presented to observers to generate electroencephalography (EEG) signals, which were then used to construct a brain functional network. The topological parameters of the network were subsequently extracted and input into a machine learning model for training. The results indicate that most of the classifiers achieved accuracy rates exceeding 70%. Specifically, the Logistic algorithm achieved an accuracy of 81.67%. Therefore, it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability. The proposed method fully considers the aspects of human visual and cognitive processes, overcomes the subjectivity of human interpretation, and achieves stable and reliable accuracy.

Global warming as a result of greenhouse gas emissions generated by anthropogenic activities (mainly the combustion of fossil fuels) has become an increasingly more severe challenge for all mankind. As a promising zero-carbon energy carrier toward the transition to a carbon-neutral society, ammonia has drawn broad attention from academia, industry, and government bodies. However, widespread applications of ammonia as a primary fuel in combustion devices require solutions for multiple technical challenges, among which the possibly very high nitrogen oxide (NOx) emission in the flue gas is the most concerned one. In this regard, extensive research efforts have been devoted over recent years to a deep understanding of the NOxformation mechanisms, which, in turn, result in a number of NOx-inhibiting technologies successfully developed; these include staged combustion, ultra-lean combustion, moderate or intense low oxygen dilution combustion, plasma-assisted combustion, etc. In this review, a brief literature review has been performed on NOxemission from ammonia flames, with the emphasis put on discussions of advances in understanding chemical kinetics related to NOxformation and reduction, effective technologies for NOxabatement, and applications of these technologies in practical ammonia-fired combustion devices. This review can serve as a reference for future studies on NOxemission and control in ammonia flames and the promotion of ammonia as a zero-carbon fuel in practical applications.

This article proposes a localization algorithm based on an L-shaped array for estimating the 2-D directions-of-arrival (DOAs) of incoherently distributed (ID) sources in the underdetermined case. To begin with, the generalized array manifold (GAM) model is established based on the first-order Taylor series approximation. The raw received data are split into frames that can be processed through delay cross-correlation operation to construct the virtual received data. Then, the covariance matrix of the virtual data is calculated, followed by eigenvalue decomposition to obtain the signal and noise subspaces. The 2-D central DOAs estimation is achieved by two 1-D spectral searching, exploiting the generalized shift-invariance property and the reduced-rank principle. Finally, the 2-D central DOAs are matched by finding the minimum value of an optimization function, utilizing the property that the noise and signal subspaces are orthogonal to each other. Simulation results demonstrate that the proposed algorithm achieves high accuracy in the central DOAs estimation without requiring any prior information about the angular distribution.

The rapid growth of Decentralized Finance (DeFi) boosts the blockchain ecosystem. At the same time, attacks on DeFi applications (apps) are increasing. However, to the best of our knowledge, existing smart contract vulnerability detection tools cannot directly detect DeFi attacks. That's because they lack the capability to recover and understand high-level DeFi semantics, e.g., a user trades a token pair X and Y in a Decentralized EXchange (DEX). In this work, we focus on the detection of two new types of price manipulation attacks. To this end, we propose a platform-independent method to identify high-level DeFi semantics. Specifically, we first construct the Cash Flow Tree (CFT) from a raw transaction and then lifting the low-level semantics to high-level ones, including five advanced DeFi actions. Finally, we use patterns expressed with the recovered DeFi semantics to detect price manipulation attacks. We implemented a prototype named DeFiRanger that detected 14 zero-day security incidents. These findings were reported to affected parties or/and the community for the first time. Furthermore, the backtest experiment discovered 15 unknown historical security incidents. We further performed an attack analysis to shed light on the root causes of vulnerabilities incurring price manipulation attacks.

The goal of supervised representation learning is to construct effective data representations for prediction. Among all the characteristics of an ideal nonparametric representation of high-dimensional complex data, sufficiency, low dimensionality and disentanglement are some of the most essential ones. We propose a deep dimension reduction approach to learning representations with these characteristics. The proposed approach is a nonparametric generalization of the sufficient dimension reduction method. We formulate the ideal representation learning task as that of finding a nonparametric representation that minimizes an objective function characterizing conditional independence and promoting disentanglement at the population level. We then estimate the target representation at the sample level nonparametrically using deep neural networks. We show that the estimated deep nonparametric representation is consistent in the sense that its excess risk converges to zero. Our extensive numerical experiments using simulated and real benchmark data demonstrate that the proposed methods have better performance than several existing dimension reduction methods and the standard deep learning models in the context of classification and regression.

Nitrobenzene is currently the most widely used explosive substance, and is known for its high toxicity and mutagenicity. It can cause severe environmental and water pollution, posing a risk to public health. Among various explosives analysis methods, surface-enhanced Raman spectroscopy (SERS) has the advantages of fast analysis speed, low detection cost, and easy operation, and has become one of the most promising analytical detection methods. Here, we present a portable and reliable sol-based SERS method for the detection of trace amounts of 2,4,6-trinitrotoluene (TNT) in different water bodies. The Meisenheimer complex formed by nitrobenzene and hydrazine hydrate can assemble on unmodified Au nanoparticles in a sol viaAu–N bonds, enabling rapid detection of TNT in seawater, lake water, and tap water using a portable Raman spectrometer. Experimental results show that this SERS method can complete the detection within a few minutes and the detection sensitivity can reach 0.01 mg L−1, which is far lower than China's national standard of no more than 0.5 mg L−1. Furthermore, this method was also successfully applied to detect trace 2,4-dinitrotoluene (2,4-DNT) and picric acid (2,4,6-trinitrophenol) in water, demonstrating its strong applicability for on-site detection of nitrobenzene explosives.

Background:: Cutaneous wound healing represents a common fundamental phenomenon requiring the participation of cells of distinct types and a major concern for the public. Evidence has confirmed that photobiomodulation (PBM) using near-infrared (NIR) can promote wound healing, but the  cells involved and the precise molecular mechanisms remain elusive. Methods:: Full-thickness skin defects with a diameter of 1.0 cm were made on the back of rats and randomly divided into the control group, 10 J, 15 J, and 30 J groups. The wound healing rate at days 4, 8, and 12 postoperatively was measured. HE and Masson staining was conducted to reveal the histological characteristics. Immunofluorescence staining was performed to label the epidermal stem cells (ESCs) and hair follicle stem cells (HFSCs). Western blot was performed to detect the expressions of proteins associated with ESCs and HFSCs. Cutaneous wound tissues were collected for RNA sequencing. Gene ontology and the Kyoto Encyclopedia of Genes and Genomes analysis was performed, and the hub genes were identified using CytoHubba and validated by qRT-PCR. Results:: PBM can promote reepithelialization, extracellular matrix deposition, and wound healing, increase the number of KRT14+/PCNA+ ESCs and KRT15+/PCNA+ HFSCs, and upregulate the protein expression of P63, Krt14, and PCNA. Three hundred and sixty-six differentially expressed genes (DEGs) and 7 hub genes including Sox9, Krt5, Epcam, Cdh1, Cdh3, Dsp, and Pkp3 were identified. These DEGs are enriched in skin development, cell junction, and cadherin binding involved in cell–cell adhesion etc., while these hub genes are related to skin derived stem cells and cell adhesion. Conclusion:: PBM accelerates wound healing by enhancing reepithelialization through promoting ESCs and HFSCs proliferation and elevating the expression of genes associated with stem cells and cell adhesion. This may provide a valuable alternative strategy to promote wound healing and reepithelialization by modulating the proliferation of skin derived stem cells and regulating genes related to cell adhesion.

ABSTRACTThis study focuses on the fastest changing component of housing demand in the future—the immigrant and minority groups, age 25–84. Using the 2006 and 2016 Canadian censuses and American Community Surveys, we compare headship and homeownership rates of both immigrants and native-born Whites in Canada and the United States. We model the probability of being a renter head, owner head, or nonhousehold head by fitting a multinomial logistic regression, controlling for several individual and contextual variables for both countries. We find that most immigrant groups have had similar patterns of household formation in the two countries and that, whereas immigrants have shown upward mobility in both housing markets, those in Canada have progressed more quickly than in the United States. Further, we find that women are less likely than men to be a household head in both countries, but that the gap is larger in Canada.

This paper explores the 12 dimensions of language cognition assessment system using factor analysis. Principal component analysis and correlation analysis of factors within the system were conducted on the language cognition assessment system. Factor analysis and principal component analysis: KMO: 0.934, weak bias correlation, suitable for factor analysis, and the contribution of principal component variance was 79.837%, respectively. The correlations between the 12 factors ranged from 0.611-0.903. The correlation values between the remaining 11 factor assignments and the total score of the system were higher than the correlations between the subscales. The system is constructed with good structural validity, but the correlations between the factors are strong. It is suggested that the indicators be combined or the form of the questions be modified to provide guidance for the modification of the language cognition assessment system.

ABSTRACTThe homeownership rate of young adults has surged to an unprecedented level in urban China, despite rising housing prices and significant rural-urban migration. A trend analysis of nationally representative microdata shows that household formation is the missing link in the paradox and that many young adults aged 18–44 have failed to form independent households from 2011 to 2017, thereby delaying the start of their housing pathways. When factors such as socioeconomic and institutional attributes are controlled for, age differences in household formation decrease as expected. However, the age differences grow surprisingly larger over the study period, reflective of reform-induced changes in resource allocation. Further analysis demonstrates significant heterogeneity in headship status. While local young adults are squeezing into homeownership, migrants are overrepresented in the relatively stunted rental sector. Thus, while migration has brought newcomers to urban China and kept the headship rates from falling even further, institutional barriers have blocked migrants’ housing pathways. Overall, the pace of change is breathtaking. There is a growing divergence in young adults’ housing pathways, which depends on the timing of market entry, institutional attributes, housing prices, and personal income.

Electroreductive dehalogenation as an efficient and green approach has attracted much attention in pollution remediation. Herein, we have employed a shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) technique to in situprobe the electroreductive dehalogenation process of aryl halides with thiol groups at Ag/aqueous solution interfaces. It is found that 4-bromothiophenol (BTP) and 4-chlorothiophenol (CTP) can turn into mixed products of 4,4′-biphenyldithiol (BPDT) and thiophenol (TP) as the electrode potential decreases. The conversion ratios estimated from the Raman intensity variations of C–Cl and C–Br vibrations are 44% and 58% for CTP and BTP in neutral solution, respectively. Furthermore, the quantitative analysis of benzene ring vibrations reveals a C–C cross coupling between the benzene free radical intermediate and adjacent TP product, which results in increased selectivity for biphenyl products at negative potentials.

A series of pyrazole-based ligands and their corresponding cationic N,S-chelate half-sandwich iridium complexes were successfully synthesized. All iridium complexes exhibited good anticancer activity against the MCF-7 and MDA-MB-231 human breast cancer cells. The cytotoxic activity of unsubstituted iridium complex 1is greater than that of cisplatin against MCF-7 cells. In addition, the cationic half-sandwich iridium complexes are also efficient in antiplasmodial study and complex 1displayed the best activity as its IC50was observed to be approximately 0.11 μM against the CQS-NF54 strain. These iridium complexes generally exhibited enhanced activity against the CQS-NF54 strain in comparison with that against the CQR-K1 strain. An “IC50speed assay” investigation against the CQS-NF54 strain indicated complexes 1–3to be fast-acting complexes that reach their lowest IC50values within 16 hours. All complexes were fully characterized by IR spectroscopy, NMR spectroscopy, and elemental analysis, and the structure of the iridium complex was confirmed by single-crystal X-ray diffraction.

AbstractRecently, we reviewed the specimens belonging to Rhopalopsolewe have collected and compared them with the holotype and paratypes of Rhopalopsole vespertilioChen & Du, 2017. We found that R. vespertilioshould be synonymized with R. xuiYang, Li & Zhu, 2004. Additionally, a new species of the genus Rhopalopsole, R. nanlingasp. nov. from Guangdong Province of southwestern China, is described and illustrated. The new species is compared to the Rhopalopsole vietnamicawest group.

Objective: The aim of this study was to construct a collagen-related prognostic model for thyroid cancer and to investigate prognostic value of collagen family genes for thyroid cancer. Methods: A LASSO Cox regression model for thyroid cancer was developed based on the expression profiles of collagen-related genes. Kaplan–Meier survival analysis was performed for high and low risk groups. The ROC method was used to assess its predictive performance. Predictive independence was verified by multivariate Cox regression analysis. The relationship between this feature and immune cell infiltration was analyzed by tumor microenvironment. COL18A1 was validated by immunohistochemistry and RT-PCR in thyroid cancer tissues. The effect of COL18A1 on cell proliferation, migration and invasion ability of tumor cells were further valuated by CCK-8 assay and transwell assay. The effect of COL18A1 on the immune escape ability of tumor cells was further valuated by cytotoxicity assays. Results: A model including 4 collagen family genes was developed to predict thyroid cancer prognosis. Patients with high-risk score had a poorer prognosis than those with low-risk scores for 1-, 2-, 3-, and 5- year survival. The model independently predicted prognosis after adjusting for other prognostic factors. A nomogram combining risk score and age was constructed with high sensitivity and specificity. This feature was significantly associated with immune cell infiltration. COL18A1 was aberrantly over-expressed in thyroid cancer compared with control tissues and significantly increased proliferative capacity, migration capacity, invasion capacity, and immune escape ability of tumor cells. Conclusion: Our findings establish a signature associated with collagen family genes that can be a promising tool to predict the prognosis of thyroid cancer. High COL18A1 expression significantly correlates with the poor prognosis of patients and enhances the immune escape ability of tumor cells.