NeuralStory: an Interactive Multimedia System for Video Indexing and Re-use

In the last years video has been swamping the Internet: websites, social networks, and business multimedia systems are adopting video as the most important form of communication and information. Video are normally accessed as a whole and are not indexed in the visual content. Thus, they are often uploaded as short, manually cut clips with user-provided annotations, keywords and tags for retrieval. In this paper, we propose a prototype multimedia system which addresses these two limitations: it overcomes the need of human intervention in the video setting, thanks to fully deep learning-based solutions, and decomposes the storytelling structure of the video into coherent parts. These parts can be shots, key-frames, scenes and semantically related stories, and are exploited to provide an automatic annotation of the visual content, so that parts of video can be easily retrieved. This also allows a principled re-use of the video itself: users of the platform can indeed produce new storytelling by means of multi-modal presentations, add text and other media, and propose a different visual organization of the content. We present the overall solution, and some experiments on the re-use capability of our platform in edutainment by conducting an extensive user valuation %with students from primary schools

Linkage and linkage disequilibrium analysis of the lipoprotein lipase gene with lipid profiles in Chinese hypertensive families

A B S T R A C T Elevated TG [triacylglycerol (triglyceride)] is a significant independent risk factor for cardiovascular disease. LPL (lipoprotein lipase) is one of the key enzymes in the metabolism of the TG-rich lipoproteins which hydrolyses TG from the chylomicrons and very-LDL (low-density lipoprotein). To investigate the relationship between the LPL gene and lipid profiles, especially TG, in 148 hypertensive families, we have chosen seven flanking microsatellite markers and four internal markers of the LPL gene and conducted linkage analysis by SOLAR and S.A.G.E. (statistical analysis for genetic epidemiology)/SIBPAL 2 programs, and linkage disequilibrium analysis by QTDT (quantitative transmission/disequilibrium test) and GOLD (graphical overview of linkage disequilibrium). There were statistically significant differences in lipid levels between subjects without and with hypertension within families. A maximum LOD score of 1.3 with TG at the marker D8S261 was observed by SOLAR. Using S.A.G.E./SIBPAL 2, we identified a linkage with TG at the marker 'ATTT' located within intron 6 of the LPL gene (P = 0.0095). Two SNPs (single nucleotide polymorphisms), HindIII and HinfI, were found in linkage disequilibrium with LDLcholesterol levels (P = 0.0178 and P = 0.0088 respectively). A strong linkage disequilibrium was observed between the HindIII in intron 8 and HinfI in the exon 9 (P < 0.00001, D = 0.895). Linkage disequilibrium was also found between the 'ATTT' polymorphism in intron 6 and two SNPs (P = 0.0021 and D = 0.611 for HindIII; and P = 0.00004, D = 0.459 for HinfI). The present study in the Chinese families with hypertension suggested that the LPL gene might influence lipid levels, especially TG metabolism. Replication studies both in Chinese and other populations are warranted to confirm these results

Role of multiple dual-phase 18F-FDG PET/CT metabolic parameters in differentiating adenocarcinomas from squamous cell carcinomas of the lung

Purpose: To evaluate the ability of multiple dual-phase 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic parameters to distinguish the histological subtypes of non-small cell lung cancer (NSCLC). Methods: Data from 127 patients with non-small cell lung cancer who underwent preoperative dual-phase 18F-FDG PET/CT scanning at the PET-CT center of our hospital from December 2020 to October 2021 were collected, and the metabolic parameters of their primary lesions were measured and analyzed retrospectively. Intraclass correlation coefficients (ICC) were calculated for consistency between readers. Metabolic parameters in the early (SUVpeak, SUVmean, SUVmin, SUVmax, MTV, and TLG) and delayed phases (dpSUVpeak, dpSUVmean, dpSUVmin, dpSUVmax, dpMTV, and dpTLG) were calculated. We drew receiver operating characteristic (ROC) curves to compare the differences in different metabolic parameters between the adenocarcinoma (AC) and squamous cell carcinoma (SCC) groups and evaluated the ability of different metabolic parameters to distinguish AC from SCC. Results: Inter-reader agreement, as assessed by the intraclass correlation coefficient (ICC), was good (ICC = 0.71, 95% CI:0.60–0.79). The mean MTV, SUVmax, TLG, SUVpeak, SUVmean, dpSUVmax, dpTLG, dpSUVpeak, dpSUVmean, and dpSUVmin of the tumors were significantly higher in SCC lesions than in AC lesions (P = 0.049, < 0.001, 0.016, < 0.001, 0.001, < 0.001, 0.018, < 0.001, 0.001, and 0.001, respectively). The diagnostic efficacy of the metabolic parameters in 18F-FDG PET/CT for differentiating adenocarcinoma from squamous cell carcinoma ranged from high to low as follows: SUVpeak (AUC = 0.727), SUVmax (AUC = 0.708), dpSUVmax (AUC = 0.699), dpSUVpeak (AUC = 0.698), TLG (AUC = 0.695), and dpTLG (AUC = 0.692), SUVmean (AUC = 0.690), dpSUVmean (AUC = 0.687), dpSUVmin (AUC = 0.680), SUVmin (AUC = 0.676), and MTV (AUC = 0.657). Conclusions: Squamous cell carcinoma of the lung had higher mean MTV, SUVmax, TLG, SUVpeak, SUVmean, SUVmin, dpSUVpeak, dpSUVmean, dpSUVmin, dpSUVmax, and dpTLG than AC, which can be helpful tools in differentiating between the two. The metabolic parameters of the delayed phase (2 h after injection) 18F-FDG PET/CT did not improve the diagnostic efficacy in distinguishing lung AC from SCC. Conventional dual-phase 18F-FDG PET/CT is not recommended

Hydrodynamic Analysis of a Multi-Pile-Supported Offshore Wind Turbine Integrated with an Aquaculture Cage

The integrated development of offshore wind power and marine aquaculture is becoming increasingly important. However, the impact mechanism of integrating a net cage on the dynamic characteristics of offshore wind turbines remains unclear. This paper presents a design scheme for a multi-pile-supported offshore wind turbine integrated with an aquaculture net cage and conducts a preliminary theoretical analysis of the influence of an additional net cage on the wind turbine. The analysis reveals that the primary effect is an increase in hydrodynamic loads on the wind turbine foundation, while the structural frequency of the wind turbine remains largely unaffected. Furthermore, computational fluid dynamics (CFD) numerical models, whose accuracy is verified by physical experiments, are utilized to compare the hydrodynamic characteristics of the offshore wind turbine foundation with and without the net cage, considering different net solidities. The simulations identify significant changes in the flow field surrounding the foundation due to the presence of the net cage, resulting in a considerable increase in the overall hydrodynamic load on the foundation. Moreover, the mutual interference between the netting and the foundation amplifies their respective hydrodynamic loads and concentrates these loads at the upstream section of the structure. The maximum increase in hydrodynamic load for a single pile reaches 6.32 times its original value, posing significant risks to the structure. Finally, a preliminary feasibility analysis of the scheme was conducted. The results presented in this article can serve as a theoretical basis for the design of such innovative structures

Exploring the mechanism by which aqueous Gynura divaricata inhibits diabetic foot based on network pharmacology, molecular docking and experimental verification

Abstract Background To predict and validate the potential mechanism by which Gynura divaricata (GD) functions in the treatment of diabetic foot (DF). Methods The main chemical constituents of GD were identified by reviewing the literature, the traditional Chinese medicine database platform (TCMIP) and the BATMAN-TCM platform. DF disease targets were identified with the GeneCards database, and the compound-target network was constructed by using the intersection of drugs and disease. The STRING platform was used to construct the protein–protein interaction (PPI) network, and Cytoscape 3.7.2 software was used to visualize the results. Moreover, the Metascape database was used for Gene Ontology (GO) enrichment analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking of the active ingredients of GD and core protein targets of DF was performed using AutoDock software. Finally, the predicted results were preliminarily verified with experiments. Results A total of 140 potential targets of GD were identified and associated with DF. According to the PPI network analysis, GD accelerated DF wound healing, and the mechanism may be related to proteins such as AKT1, TP53, IL6, CASP3, TNF, and VEGFA. GO and KEGG enrichment analyses indicated that GD may play a role in the treatment of diabetic foot by affecting various signaling pathways. Molecular docking results showed that the proteins AKT1, TP53, IL6, CASP3, TNF, and VEGFA were closely associated with the components of GD. The animal experiments showed that GD reduced the levels of IL-6 and TNF-α and increased the mRNA and protein expression of VEGFA in rats with DF. Conclusions GD regulates multiple targets and multiple pathways to promote wound healing in DF

Long-Strip Target Detection and Tracking with Autonomous Surface Vehicle

As we all know, target detection and tracking are of great significance for marine exploration and protection. In this paper, we propose one Convolutional-Neural-Network-based target detection method named YOLO-Softer NMS for long-strip target detection on the water, which combines You Only Look Once (YOLO) and Softer NMS algorithms to improve detection accuracy. The traditional YOLO network structure is improved, the prediction scale is increased from threeto four, and a softer NMS strategy is used to select the original output of the original YOLO method. The performance improvement is compared totheFaster-RCNN algorithm and traditional YOLO methodin both mAP and speed, and the proposed YOLO–Softer NMS’s mAP reaches 97.09%while still maintaining the same speed as YOLOv3. In addition, the camera imaging model is used to obtain accurate target coordinate information for target tracking. Finally, using the dicyclic loop PID control diagram, the Autonomous Surface Vehicle is controlled to approach the long-strip target with near-optimal path design. The actual test results verify that our long-strip target detection and tracking method can achieve gratifying long-strip target detection and tracking results

A Study on Local Scour of Large-Diameter Monopile under Combined Waves and Current

The complex-load environment of offshore monopile foundations makes the combination of waves and current, and the variation in water depth, important factors for local scour. A flume test model of flow–pile–soil coupling for large-diameter monopile foundations is established, which comprehensively considers the combined reciprocating action of wave/current and the influence of tide depth. The precision of the experiment is ensured by the extension method of series models. The results show that the local scour caused by tides and the wave–current combination is obviously different from the unidirectional wave–current combination. The equilibrium scour depth obtained by the test is found to have some deviation from the predicted scour-depth equation. The maximum scour depth decreased with the increase in water depth within the range of 3 to 6 times the pile diameter. The bed shear-stress equation was generally consistent with the scouring-depth rule measured by the model, which can be considered to evaluate the rationality of the test results

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas