With the rapid development of new era and new media, opportunities and challenges of the development of public service advertising coexist. Against the backdrop of the times and the development of ...media, public service advertising needs to incorporate more subjects, so that the audience can pay more attention to and understand such advertisements. Based on the above social background, how to better publicize the mainstream values of society among the public with the help of public service advertising is what the current government needs to focus on and solve. This research combines the idealized cognitive models to elaborate the social value of public service advertising in the new era, with a view to giving suggestions to the state, government, society and other subjects, and contributing to the vigorous development of public service advertising in China.
Bismuth (Bi) has been considered as a promising alloying-type anode for potassium-ion batteries (PIBs), owing to its high theoretical capacity and suitable working voltage plateaus. However, Bi ...suffers from dramatic volume fluctuation and significant pulverization during the discharge/charge processes, resulting in fast capacity decay. Herein, we synthesize Bi nanoparticles confined in carbonaceous nanospheres (denoted as Bi@C) for PIBs by first utilizing BiOCl nanoflakes as a hard template and a Bi precursor. The construction of the loose structure buffers the mechanical stresses resulting from the volume expansion of Bi during the alloying reaction and avoids the fracture of the electrode structure, thus improving the cycling performance. Moreover, the carbonaceous layers increase the electronic conductivity and disperse the Bi nanoparticles, enhancing the charge transportation and ionic diffusion, which further promotes the rate capability of Bi@C. It exhibits a superior capacity (389 mAh g–1 at 100 mA g–1 after 100 cycles), excellent cycling stability (206 mAh g–1 at 500 mA g–1 over 1000 cycles), and an improved rate capability (182 mAh g–1 at 2.0 A g–1). This work provides a new structuring strategy in alloying materials for boosting reversible and stable potassium-ion storage.
Abstract Black carbon (BC) aerosol is one of the most important factor in global warming. BC radiative forcing remains unconstrained, mainly because of the uncertain parameterizations of its ...absorption and scattering properties in the atmosphere. The single sphere model is widely used in current climate assessment of BC aerosols due to its computational convenience, however, their complex morphologies in particle level are excessively simplified which leads to computed inaccuracy. In this study, we present a dynamic model for optical calculations of BC aerosol ensembles considering their complex fractal aggregate morphologies with the constraint of max monomer numbers ( N s, max ) and radius ( a max ). We show that the simulation accuracy of the dynamic model with suitable values of N s, max and a max may achieve ∼95% while the computation time may reduce to ∼6%. We find that optical properties of BC aerosol ensembles can be simulated for higher accuracy or faster calculation by performing different selections of monomer numbers and radius in their size distributions. This method enables extensive and accurate optical calculations of BC particles with complex morphologies, which would be useful for the remote sensing inversion and the assessment of climate.
Traffic loading monitoring plays an important role in bridge structural health monitoring, which is helpful in overloading detection, transportation management, and safety evaluation of ...transportation infrastructures. Bridge weigh-in-motion (BWIM) is a method that treats traffic loading monitoring as an inverse problem, which identifies the traffic loads of the target bridge by analyzing its dynamic strain responses. To achieve accurate prediction of vehicle loads, the configuration of axles and vehicle velocity must be obtained in advance, which is conventionally acquired via additional axle-detecting sensors. However, problems arise from additional sensors such as fragile stability or expensive maintenance costs, which might plague the implementation of BWIM systems in practice. Although data-driven methods such as neural networks can estimate traffic loadings using only strain sensors, the weight data of vehicles crossing the bridge is difficult to obtain. In order to overcome these limitations, a modified encoder-decoder architecture grafted with signal-reconstruction layer is proposed in this paper to identify the properties of moving vehicles (i.e., velocity, wheelbase, and axle weight) using merely the bridge dynamic response. Encoder-decoder is an unsupervised method extracting higher features from original data. The numerical bridge model based on vehicle-bridge coupling vibration theory is established to illustrate the applicability of this new encoder-decoder method. The identification results demonstrate that the proposed approach can predict traffic loadings without using additional sensors and without requiring vehicle weight labels. Parametric studies also show that this new approach achieves better stability and reliability in identifying the properties of moving vehicles, even under the circumstances of large data pollution.
In general, microgrids have a high renewable energy abandonment rate and high grid construction and operation costs. To improve the microgrid renewable energy utilization rate, the economic ...advantages, and environmental safety of power grid operation, we propose a hybrid energy storage capacity optimization method for a wind–solar–diesel grid-connected microgrid system, based on an augmented ε- constraint method. First, the battery is coupled with a seasonal hydrogen energy storage system to establish a hybrid energy storage model that avoids the shortcomings of traditional microgrid systems, such as a single energy storage mode and a small capacity. Second, by considering the comprehensive cost and carbon emissions of the power grid within the planning period as the objective function, the abandonment rate of renewable energy as the evaluation index, and the electric energy storage and seasonal hydrogen energy storage system operating conditions as the main constraints, the capacity allocation model of the microgrid can be constructed. Finally, an augmented ε- constraint method is implemented to optimize the model above; the entropy–TOPSIS method is used to select the configuration scheme. By comparative analysis, the results show that the optimization method can effectively improve the local absorption rate of wind and solar radiation, and significantly reduce the carbon emissions of microgrids.
The method of Support Vector Machine (SVM) based on Dissolved Gas Analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings, such ...as the fuzzy boundaries of DGA data, and SVM parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on Kernel Principal Component Analysis (KPCA) and a hybrid improved Seagull Optimization Algorithm to optimize the SVM (TISOA-SVM). Firstly, KPCA is used to extract features from DGA feature quantities. In addition, TISOA is further proposed to optimize the SVM parameters to build the optimal diagnosis model based on SVM. For the SOA, three improvement methods are proposed. An improved tent map is used to replace the original population initialization to improve population diversity. In addition, the nonlinear inertia weight and random double helix formula are proposed to improve the optimization accuracy and efficiency of the SOA. Then, benchmarking functions are used to test the optimization performance of TISOA and six algorithms, and the results show that TISOA has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on KPCA and TISOA-SVM is obtained, and it is noteworthy that three examples are tested to verify the diagnostic performance of the proposed method. These results show that the proposed method has higher diagnostic accuracy, shorter diagnosis time, stronger significance and validity than other methods. Therefore, a research idea is provided for solving practical engineering problems in the field of fault diagnosis.
Cell junctions, which are typically associated with dynamic cytoskeletons, are essential for a wide range of cellular activities, including cell migration, cell communication, barrier function and ...signal transduction. Observing cell junctions in real-time can help us understand the mechanisms by which they regulate these cellular activities. This study examined the binding capacity of a modified tridecapeptide from Connexin 43 (Cx43) to the cell junction protein zonula occludens-1 (ZO-1). The goal was to create a fluorescent peptide that can label cell junctions. A cell-penetrating peptide was linked to the modified tridecapeptide. The heterotrimeric peptide molecule was then synthesized. The binding of the modified tridecapeptide was tested using pulldown and immunoprecipitation assays. The ability of the peptide to label cell junctions was assessed by adding it to fixed or live Caco-2 cells. The testing assays revealed that the Cx43-derived peptide can bind to ZO-1. Additionally, the peptide was able to label cell junctions of fixed cells, although no obvious cell junction labeling was observed clearly in live cells, probably due to the inadequate affinity. These findings suggest that labeling cell junctions using a peptide-based strategy is feasible. Further efforts to improve its affinity are warranted in the future.
Background
Lymph node (LN) involvement is a critical prognostic factor in patients with gallbladder carcinoma (GBC). Controversy exists regarding optimal categorization of nodal metastasis status, ...including anatomical location of positive nodes (AJCC 7th N staging), number of metastatic lymph nodes (NMLN), log odds of metastatic LNs (LODDS), and lymph node ratio (LNR).
Methods
Patients who underwent curative-intent resection for GBC from six Chinese tertiary hospitals between 2008 and 2013 were analyzed retrospectively. The relative discriminative abilities of the different LN staging systems were assessed by different models including the tree-augmented naïve Bayesian (TAN) model, Cox proportional hazards regression model, and binary logistic regression model.
Results
A total of 226 patients were involved in this cohort. Based on the TAN model and composite importance measures, the most important factor affecting the prognosis in the different LN staging systems was NMLN. Among the four TAN models which were built with 4 metastatic LN markers and baseline variables, the accuracy of the NMLN-based prognostic model was 88.15%, higher than 7th N staging (86.44%), LNR (87.34%), and LODDS (85.19%). The Cox model based on NMLN (C-index: 0.763, AIC: 1371.62) had a higher fitness than the others (7th N staging C-index: 0.756, AIC: 1375.51; LNR C-index: 0.759, AIC: 1378.82; LODDS C-index 0.748, AIC: 1390.99). The AUCs of different staging binary logistic regression models were NMLN (0.872), LNR (0.872), 7th N staging (0.869) and LODDS (0.856), respectively.
Conclusions
NMLN was the optimal LN staging system in evaluating prognosis of GBC.
Tree skeletons play an important role in tree structure analysis and 3D model reconstruction. However, it is a challenge to extract a skeleton from a tree point cloud with complex branches. In this ...paper, an automatic and fast tree skeleton extraction method (FTSEM) based on voxel thinning is proposed. In this method, a wood–leaf classification algorithm was introduced to filter leaf points for the reduction of the leaf interference on tree skeleton generation, tree voxel thinning was adopted to extract a raw tree skeleton quickly, and a breakpoint connection algorithm was used to improve the skeleton connectivity and completeness. Experiments were carried out in Haidian Park, Beijing, in which 24 trees were scanned and processed to obtain tree skeletons. The graph search algorithm (GSA) was used to extract tree skeletons based on the same datasets. Compared with the GSA method, the FTSEM method obtained more complete tree skeletons. The time cost of the FTSEM method was evaluated using the runtime and time per million points (TPMP). The runtime of FTSEM was from 1.0 s to 13.0 s, and the runtime of GSA was from 6.4 s to 309.3 s. The average value of TPMP was 1.8 s for FTSEM and 22.3 s for GSA, respectively. The experimental results demonstrate that the proposed method is feasible, robust, and fast with good potential for tree skeleton extraction.
In this work, we grow intrinsically Cu-doped TiO2 nanotubes (TiNTs) by self-organizing anodization of Ti–Cu binary alloys. We demonstrate that up to a copper concentration of 1.5 at.% in the alloy, ...self-ordered Cu2+-doped nanotubes can be grown. Under UV illumination the Cu2+ ion-doped oxide structures can be converted to nanotubes that carry metallic nanoparticles (NPs) uniformly decorated on top of the TiNTs. We investigate the formation of these metallic nanoparticles under UV illumination by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR). The resulting intrinsic copper-doped and decorated TiNTs have a strongly enhanced photocatalytic activity for H2 evolution in comparison to pristine TiNTs. Key is the light-induced conversion of the intrinsic Cu dopant to metallic copper nanoparticles that act as a stable co-catalyst for H2 generation.
•Intrinsically Cu doped TiO2 nanotubes were grown by anodization of Ti-Cu alloys.•Up to a copper concentration of 1.5 at.% in the alloy, nanotubes can be grown.•Under UV illumination the doped Cu2+ can be converted to metallic nanoparticles.•Decorated TiO2 nanotubes provide a strongly enhanced photocatalytic activity for H2.•The light induced nanoparticles act as a stable co-catalyst for H2 generation.
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