•A new model was created to measure the non-uniform corrosion factor R.•The critical value of factor R is used to predict failure mode in corroded rebars.•New non-uniform corrosion factor R was used ...to analyze reliability of RC beams.
To study the non-uniform distribution of cross-sectional areas and mechanical properties of corroded steel bars under a chloride environment, 19 concrete slabs embedded with steel bars were exposed to a chloride environment to achieve accelerated corrosion. A 3D measurement technique was employed to create geometric models of corroded steel bars, and their cross-sectional areas of a corroded steel bar, a probability distribution model of the spatial variability factor R, was developed through signal processing and statistical analysis methods. The R factor is the ratio of the average cross-sectional area to the minimum one and was applied to analyze the mechanical behavior response to the non-uniform corrosion of steel bars under tensile loading. In addition, the relationship between the mechanical properties and R factor of a corroded steel bar was established to predict its critical state of brittle failure. Our novel approach to this analysis enabled the calculation of the time-dependent reliability of a simply supported corroded reinforced concrete beam. The results showed that non-uniform corrosion is the critical factor affecting the time-dependent reliability of a reinforced concrete beam; moreover, changes in the failure mode of a corroded steel bar may severely impair the reliability of a reinforced concrete beam.
Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with ...other proteins in their environment to perform biological functions. Understanding PPIs reveals how cells behave and operate, such as the antigen recognition and signal transduction in the immune system. In the past decades, many computational methods have been developed to predict PPIs automatically, requiring less time and resources than experimental techniques. In this paper, we present a comparative study of various graph neural networks for protein–protein interaction prediction. Five network models are analyzed and compared, including neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions (HGCN). By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets.
Valence tuning of transition metal oxides is an effective approach to design high‐performance catalysts, particularly for the oxygen evolution reaction (OER) that underpins solar/electric water ...splitting and metal‐air batteries. Recently, high‐valence oxides (HVOs) are reported to show superior OER performance, in association with the fundamental dynamics of charge transfer and the evolution of the intermediates. Particularly considered are the adsorbate evolution mechanism (AEM) and the lattice oxygen‐mediated mechanism (LOM). High‐valence states enhance the OER performance mainly by optimizing the eg‐orbital filling, promoting the charge transfer between the metal d band and oxygen p band. Moreover, HVOs usually show an elevated O 2p band, which triggers the lattice oxygen as the redox center and enacts the efficient LOM pathway to break the “scaling” limitation of AEM. In addition, oxygen vacancies, induced by the overall charge‐neutrality, also promote the direct oxygen coupling in LOM. However, the synthesis of HVOs suffers from relatively large thermodynamic barrier, which makes their preparation difficult. Hence, the synthesis strategies of the HVOs are discussed to guide further design of the HVO electrocatalysts. Finally, further challenges and perspectives are outlined for potential applications in energy conversion and storage.
High valence oxides of transition metals represent an emerging group of valence‐engineered catalysts, capable of offering very high catalytic activity and stability. The mechanisms of such “unconventional” characteristics and their synthesis strategies are discussed here. The case for oxygen evolution reaction is reviewed to guide effective development of the catalytic structures for water‐splitting hydrogen generation and metal‐air batteries.
In the face of complex scenarios, the information insufficiency of classification tasks dominated by a single modality has led to a bottleneck in classification performance. The joint application of ...multimodal remote sensing data for surface observation tasks has garnered widespread attention. However, issues such as sample differences between modalities and the lack of correlation in physical features have limited the performance of classification tasks. Establishing effective interaction between multimodal data has become another significant challenge. To fully integrate heterogeneous information from multiple modalities and enhance classification performance, this paper proposes a dual-branch cross-Transformer feature fusion network aimed at joint land cover classification of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. The core idea is to leverage the potential of convolutional operators to represent spatial features, combined with the advantages of the Transformer architecture in learning remote dependencies. The framework employs an improved self-attention mechanism to aggregate features within each modality, highlighting the spectral information of HSI and the spatial (elevation) information of LiDAR. The feature fusion module based on cross-attention integrates deep features from two modalities, achieving complementary information through cross-modal attention. The classification task is performed using jointly obtained spectral and spatial features. Experiments were conducted on three multi-source remote sensing classification datasets, demonstrating the effectiveness of the proposed model compared to existing methods.
Abstract
A new multivariable-based diagnostic fog-forecasting method has been developed at NCEP. The selection of these variables, their thresholds, and the influences on fog forecasting are ...discussed. With the inclusion of the algorithm in the model postprocessor, the fog forecast can now be provided centrally as direct NWP model guidance. The method can be easily adapted to other NWP models. Currently, knowledge of how well fog forecasts based on operational NWP models perform is lacking. To verify the new method and assess fog forecast skill, as well as to account for forecast uncertainty, this fog-forecasting algorithm is applied to a multimodel-based Mesoscale Ensemble Prediction System (MEPS). MEPS consists of 10 members using two regional models the NCEP Nonhydrostatic Mesoscale Model (NMM) version of the Weather Research and Forecasting (WRF) model and the NCAR Advanced Research version of WRF (ARW) with 15-km horizontal resolution. Each model has five members (one control and four perturbed members) using the breeding technique to perturb the initial conditions and was run once per day out to 36 h over eastern China for seven months (February–September 2008). Both deterministic and probabilistic forecasts were produced based on individual members, a one-model ensemble, and two-model ensembles. A case study and statistical verification, using both deterministic and probabilistic measuring scores, were performed against fog observations from 13 cities in eastern China. The verification was focused on the 12- and 36-h forecasts.
By applying the various approaches, including the new fog detection scheme, ensemble technique, multimodel approach, and the increase in ensemble size, the fog forecast accuracy was steadily and dramatically improved in each of the approaches: from basically no skill at all equitable threat score (ETS) = 0.063 to a skill level equivalent to that of warm-season precipitation forecasts of the current NWP models (0.334). Specifically, 1) the multivariable-based fog diagnostic method has a much higher detection capability than the liquid water content (LWC)-only based approach. Reasons why the multivariable approach works better than the LWC-only method were also illustrated. 2) The ensemble-based forecasts are, in general, superior to a single control forecast measured both deterministically and probabilistically. The case study also demonstrates that the ensemble approach could provide more societal value than a single forecast to end users, especially for low-probability significant events like fog. Deterministically, a forecast close to the ensemble median is particularly helpful. 3) The reliability of probabilistic forecasts can be effectively improved by using a multimodel ensemble instead of a single-model ensemble. For a small ensemble such as the one in this study, the increase in ensemble size is also important in improving probabilistic forecasts, although this effect is expected to decrease with the increase in ensemble size.
Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is ...insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.
Surface-enhanced Raman spectroscopy (SERS) as a powerful qualitative analysis method has been widely applied in many fields. However, SERS for quantitative analysis still suffers from several ...challenges partially because of the absence of stable and credible analytical strategy. Here, we demonstrate that the optimal hotspots created from dynamic surfaced-enhanced Raman spectroscopy (D-SERS) can be used for quantitative SERS measurements. In situ small-angle X-ray scattering was carried out to in situ real-time monitor the formation of the optimal hotspots, where the optimal hotspots with the most efficient hotspots were generated during the monodisperse Au-sol evaporating process. Importantly, the natural evaporation of Au-sol avoids the nanoparticles instability of salt-induced, and formation of ordered three-dimensional hotspots allows SERS detection with excellent reproducibility. Considering SERS signal variability in the D-SERS process, 4-mercaptopyridine (4-mpy) acted as internal standard to validly correct and improve stability as well as reduce fluctuation of signals. The strongest SERS spectra at the optimal hotspots of D-SERS have been extracted to statistics analysis. By using the SERS signal of 4-mpy as a stable internal calibration standard, the relative SERS intensity of target molecules demonstrated a linear response versus the negative logarithm of concentrations at the point of strongest SERS signals, which illustrates the great potential for quantitative analysis. The public drugs 3,4-methylenedioxymethamphetamine and α-methyltryptamine hydrochloride obtained precise analysis with internal standard D-SERS strategy. As a consequence, one has reason to believe our approach is promising to challenge quantitative problems in conventional SERS analysis.
Ship targets are high-value military and civilian targets with broad application prospects. However, the precise focusing of ships is still a difficult issue because of their complicated ...six-degree-of-freedom motions on the sea surface. This paper focused on investigating the effect of ship six-degree-of-freedom oscillation on Synthetic Aperture Radar imaging. Firstly, based on the six-degree-of-freedom motions, the accurate range models for ship linear oscillation and angular oscillation were built, and the superiority was verified by comparing them with the models described in published literature. Secondly, we used the Taylor formula and Bessel function to expand the phase error introduced by ship oscillation, then their effects on imaging were further analyzed. Finally, based on the measured ship attitude data, we generated the semi-physical echoes of the oscillatory ship to validate the analysis throughout this article. Based on the proposed range model, we also made some tentative on the phase compensation method by fitting ship attitude angles with multiple sinusoidal functions.
Tumor-specific neoantigens have attracted much attention since they can be used as biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as potential targets for cancer ...immunotherapy. In this study, we developed a comprehensive tumor-specific neoantigen database (TSNAdb v1.0), based on pan-cancer immunogenomic analyses of somatic mutation data and human leukocyte antigen (HLA) allele information for 16 tumor types with 7748 tumor samples from The Cancer Genome Atlas (TCGA) and The Cancer Immunome Atlas (TCIA). We predicted binding affinities between mutant/wild-type peptides and HLA class I molecules by NetMHCpan v2.8/v4.0, and presented detailed information of 3,707,562/1,146,961 potential neoantigens generated by somatic mutations of all tumor samples. Moreover, we employed recurrent mutations in combination with highly frequent HLA alleles to predict potential shared neoantigens across tumor patients, which would facilitate the discovery of putative targets for neoantigen-based cancer immunotherapy. TSNAdb is freely available at http://biopharm.zju.edu.cn/tsnadb.
Aqueous zinc-ion batteries (AZIBs) are promising for large-scale energy storage systems due to their high safety, large capacity, cost-effectiveness, and environmental friendliness. However, their ...commercialization is currently hindered by several challenging issues, including cathode degradation and zinc dendrite growth. Recently, metal-organic frameworks (MOFs) and their derivatives have gained significant attention and are widely used in AZIBs due to their highly porous structures, large specific surface area, and ability to design frameworks for Zn
shuttle. Based on preceding contributions, this review aims to generalize two design principles for MOF-based materials in AZIBs: cathode preparation and anode protection. For cathode preparation, we mainly introduce novel MOF-based electrode materials such as pure MOFs, porous carbon materials, metal oxides, and their compounds, focusing on the analysis of the specific capacity of AZIBs. For anode protection, we systematically analyze MOF-based materials used as 3D Zn architecture, solid electrolyte interfaces, novel separators, and solid-state electrolytes, highlighting the improvement in the cyclic stability of Zn anodes. Finally, we propose the future development of MOF-based materials in AZIBs. Our work can give some clues for raising the practical application level of aqueous ZIBs.
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