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This study examines the evolution of advertising from traditional media to social media platforms and analyzes how consumer engagement on different social media platforms impacts advertising effectiveness. Starting with advertising through print, then radio, TV, and later through digital channels, research traces back the historical development of advertising, focusing on unique characteristics of social media advertising. Through a comprehensive literature review of recent scholarly articles and industry reports, we investigate how platform-specific features and user experiences shape engagement with both social media content and embedded advertising. The study reveals engagement is highly context-specific; thus, each of the social media platforms provides a very particular set of experiences conditioning how users engage with and react to advertising content. When developing their social media marketing plans, advertisers should be sensitive to the differences between platforms in terms of the strategies applied for engagement. This paper concludes by providing a conceptual framework from which to think about the relationships between platformspecific engagement and the outcome of social media advertising. This research contributes to the increased volume of literature surrounding digital advertising and has practical implications for how marketers can maximize returns from their social media advertising activity in an increasingly fragmented, complex media environment.

The first labeled and published event-based neuromorphic vision sensor benchmarks were created from the MNIST digit recognition dataset by jiggling the image on the screen (see Serrano-Gotarredona and Linares-Barranco, 2015 for an informative history) and later to reduce frame artifacts by jiggling the camera view with a pan-tilt unit (Orchard et al., 2015). Materials and Methods The DVS data are generated by displaying existing benchmark videos on a monitor, and recording with a stationary DAViS240C vision sensor under controlled lighting conditions. Because of the dynamic nature of the displayed video, the sensor will generate events for local brightness changes. Because the original datasets are frame based, we characterized the artifacts produced by the stroboscopic video sequence presentations and monitor refresh rate. 2.1. The spectrum is generated using the same method as in supplementary materials of Serrano-Gotarredona and Linares-Barranco (2015), where also methods are described to potentially remove artifacts. Since other post-processing techniques could be used, we have decided to provide the original, unprocessed

A novel heterotrophic bacterium capable of heterotrophic nitrification and aerobic denitrification was isolated from ammonium contaminated landfill leachate and physiochemical and phylogenetically identified as Zobellella taiwanensis DN-7. DN-7 converted nitrate, nitrate, and ammonium to N 2 as the primary end product. Single factor experiments suggested that the optimal conditions for ammonium removal were trisodium citrate as carbon source, C/N ratio 8, pH 8.0–10.0, salinity less than 3 %, temperature 30 °C, and rotation speed more than 150 rpm. Specifically, DN-7 could remove 1000.0 and 2000.0 mg/L NH 4 + -N completely within 96 and 216 h, with maximum removal rates of 19.6 and 17.3 mg L −1  h −1 , respectively. These results demonstrated that DN-7 is a promising candidate for application of high-strength ammonium wastewater treatments.

Satellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improve the prediction accuracy and stability of the GRU, this paper proposes a satellite clock bias forecasting model, termed ITSSA-GRU, which combines the improved sparrow search algorithm (SSA) and the GRU, avoiding the problems of GRU’s sensitivity to hyperparameters and its tendency to fall into local optimal solutions. The model improves the initialization population phase of the SSA by introducing iterative chaotic mapping and adopts an iterative update strategy based on t-step optimization to enhance the optimization ability of the SSA. Five models, namely, ITSSA-GRU, SSA-GRU, GRU, LSTM, and GM(1,1), are used to forecast the satellite clock bias data in three different types of orbits of the BDS-3 system: MEO, IGSO, and GEO. The experimental results show that, as compared with the other four models, the ITSSA-GRU model has a stronger generalization ability and forecasting effect in the clock bias forecasting of all three types of satellites. Therefore, the ITSSA-GRU model can provide a new means of improving the accuracy of navigation satellite clock bias forecasting to meet the needs of high-precision positioning.

The scientific evaluation of passenger satisfaction for public transport is helpful to enhance the attraction of public transport. To improve the accuracy of passenger satisfaction evaluation for public transport and the scientificity and objectivity of the index weighting, combining the characteristics of analytic hierarchy process (AHP), entropy weight method (EWM) and fuzzy comprehensive evaluation(FCE) method, the passenger satisfaction evaluation system for Ningbo's urban public transportation was built. The paper analyzed 5046 questionnaires on conventional bus transit and 1682 questionnaires on rail transit in Ningbo city, Passenger satisfaction for Ningbo city's public transport was evaluated comprehensively, and the evaluation results showed that the overall passenger satisfaction of the public transport in Ningbo was 91.2 in 2019, The case study shows that the application of the AHP-EWM-FCE model on the multi-mode public transport system can objectively quantify passengers' feelings about urban public transport service, and thus provide a theoretical basis for the improvement of passenger satisfaction in Ningbo.

This study investigates the dynamic response and failure mechanism of the polyline-shaped large-span double-layer grid space structures subjected to progressive collapse. A grid model was designed and fabricated to represent a typical area of a large-span double-layer grid space structure from a specific engineering project. Three representative locations were selected to simulate failure of the test model, and dynamic collapse tests were conducted. In the tests, four conditions were considered: D1 (120 kg, failure at A), D2 (120 kg, failure at B), D3 (120 kg, failure at C), and D4 (200 kg, failure at C). The dynamic response of the structure under various conditions was studied by comparing strain, displacement, and failure patterns derived from the test analysis. Furthermore, the collapse process and mechanism of the structure were analyzed. The results indicate that the upper chord rods are key components in collapse resistance design. Under test conditions D4, significant vertical displacement occurred, and out-of-plane deformation increased markedly after the lateral constraints were removed, causing the structure to tilt towards the side without a failure device. The strain and displacement changes were most significant under test conditions D3 and D4, especially near the failure locations. Under condition D3, the strain change is 1109 microstrain larger than that before the failure, with the maximum vertical displacement increase being 59.109 mm. Under condition D4, the strain change is -1126 microstrain larger than that before the failure, with the maximum vertical displacement increase being 74.795 mm. Through multi-condition testing, the collapse mechanisms at different failure locations in the structure were clarified. The failure of web members and lower chord rods led to a redistribution of internal forces, but the effect on the double-layer grid structure was minimal. After the failure of the upper chord rods, significant displacements occurred near the failure location, and buckling of surrounding members was observed.

Airborne distributed coherent aperture radar is of great significance for expanding the detection capability of the system. However, the extra observation dimension introduced by its sparse configuration also deteriorates the performance of traditional adaptive processing in a non-uniform environment. This paper focuses on moving target detection when the system works in a clutter–jamming-coexisting environment. In order to make full use of the specific low-rank structure to reduce the requirement for training data, this paper proposes a two-stage adaptive scheme that cancels jamming and clutter separately. The proposed suppression scheme first excludes the mainlobe jamming component from the training data based on the prior clutter subspace projection and performs intra-node clutter suppression. Then, the remaining jamming is jointly canceled based on the covariance obtained with its inter-pulse mixture model. Numerical examples show that this scheme can effectively reduce the blocking effect of main lobe jamming on high-speed targets but, due to the inaccuracy of the prior subspace, there is a certain additional loss of signal-to-noise ratio for near stationary targets. The simulation also shows that the proposed scheme is equally applicable to systems with a time-varying distributed geometry.

Ding Ma, Hui Wang, Xun Xu and colleagues report a genome-wide map of HPV integration sites in cervical cancer samples and cell lines. In addition to discovering new integration hot spots, the authors identify microhomology-mediated DNA repair as a likely mechanism by which HPV integrates into the human genome. Human papillomavirus (HPV) integration is a key genetic event in cervical carcinogenesis 1 . By conducting whole-genome sequencing and high-throughput viral integration detection, we identified 3,667 HPV integration breakpoints in 26 cervical intraepithelial neoplasias, 104 cervical carcinomas and five cell lines. Beyond recalculating frequencies for the previously reported frequent integration sites POU5F1B (9.7%), FHIT (8.7%), KLF12 (7.8%), KLF5 (6.8%), LRP1B (5.8%) and LEPREL1 (4.9%), we discovered new hot spots HMGA2 (7.8%), DLG2 (4.9%) and SEMA3D (4.9%). Protein expression from FHIT and LRP1B was downregulated when HPV integrated in their introns. Protein expression from MYC and HMGA2 was elevated when HPV integrated into flanking regions. Moreover, microhomologous sequence between the human and HPV genomes was significantly enriched near integration breakpoints, indicating that fusion between viral and human DNA may have occurred by microhomology-mediated DNA repair pathways 2 . Our data provide insights into HPV integration-driven cervical carcinogenesis.

Improving travel time prediction for public transit effectively enhances service reliability, optimizes travel structure, and alleviates traffic problems. Its greater time-variance and uncertainty make predictions for short travel times (≤35min) more subject to be influenced by random factors. It requires higher precision and is more complicated than long-term predictions. Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction. Kalman filter model has high accuracy in one-step prediction and can be used to calculate a large amount of data. This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time prediction model (DTM); the evaluation criteria and indexes of the models are given. The error analysis of the prediction results is carried out based on AVL data by case study. Results show that under the precondition of multi-source data, the public transportation prediction model can meet the accuracy requirement for travel time prediction and the prediction effect of the whole route is superior to that of the route segment between stops.

River channels are fundamental geomorphological and hydrological features that play a critical role in regulating the Earth’s water cycle and ecosystems and influencing human activities. This study utilized Digital Elevation Model (DEM) data and multi-source remote sensing imagery (including GF-1 WFV, Sentinel-1, and Sentinel-2) to determine river channel dimensions. River water masks were obtained from multiple remote sensing imagery sources and processed through triangulation and segmentation to generate river reach results. Based on these segmented river reaches, buffer analysis was conducted. The buffer analysis results were then used to refine and clip the 5 m DEM and 12.5 m DEM datasets. Finally, river channels were extracted from the clipped DEM data using the natural breaks classification method. The classification accuracy was assessed using a confusion matrix. Experimental results demonstrate a high overall classification accuracy, reaching or exceeding 0.985, with classification consistency (Kappa coefficient) ranging from 0.78 to 0.81. The 5 m resolution DEM exhibited superior performance compared to the 12.5 m resolution DEM in river channel extraction, especially regarding the classification consistency (Kappa coefficient), with the 5 m resolution model outperforming the latter. This approach effectively delineates the river channel boundaries, transcends the constraints of a singular data source, enhances the precision and resilience of river extraction, and possesses several practical applications. The extracted data can support analyses of river evolution, facilitate hydrological modeling at the basin scale, improve flood disaster monitoring, and contribute to various other research domains.