Experimental Study on Bearing Capacity of Glass Bridge Deck under Different Wheel Compression Positions

In order to solve the problem of lighting in the building structure, landscape area or car city and the vehicle yard to achieve green energy saving, it is proposed to replace the full concrete bridge deck scheme with the glass bridge deck and concrete bridge deck, and to utilize the light transmittance of the glass material. In order to explore the technical feasibility of applying glass bridge panels to automobile passages, the paper intends to carry out the bearing capacity test of three-layer PVB glass sheets and single-layer glass bridge decks under different wheel pressures under the wheel load, paying attention to the continuity of load application and researching the load, respectively the force of the glass plate when acting in different positions. Six laminated glass plates and three single-layer glass plates were designed and fabricated, and the rubber load was used to simulate the wheel load for test loading. The ratio of the bearing edge and the plate angle of the single-layer glass to the load at the center of the plate was 78.7% and 64.8%, respectively. The ratio of the bearing edge of the laminated glass and the plate angle of the laminated glass to the load was 98.3% and 86.5%, respectively. For laminated glass, the effect of the load position on the ultimate bearing capacity of the glass sheet is weaker than that of the single layer glass. The ultimate bearing capacity under wheel load acting on the center, side and corner of the laminated glass plate is 2.78, 3.47 and 3.66 times of that of the laminated glass plate at the same position. The test results in this paper can be of practical value for the further application of glass bridge panels in engineering applications

Experimental Study on the Bearing Capacity of Glass Deck under the Condition of Vehicle Traffic

In order to study the mechanical properties of the glass plate structure applied to the automobile bridge deck, the bearing capacity test of the glass bridge deck under the wheel load is carried out, and the failure mode, load displacement curve and safety function of the glass plate under the boundary, position and number of layers of the wheel load are analyzed. The results show that the ultimate bearing capacity of laminated glass under the condition of simply supported boundary on both sides is about four sides supported 71.8%. The ultimate bearing capacity of single-layer glass under the boundary condition of simple support on both sides is about four sides 51.4% from the point of view of meeting the structural strength requirements. The loading test is carried out by applying different multiple wheel loads at the plate angle and the center of the plate. The test results can provide reference for the application of the glass bridge deck in engineering

Multi-Behavior Recommendation with Cascading Graph Convolution Networks

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior's embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively.Comment: Accepted by WWW 202

Interaction of a symmetrical α,α',δ,δ'-Tetramethyl-cucurbit[6]uril with Ln³⁺ : potential applications for isolation of lanthanides

The interaction of a symmetrical α,α′,δ,δ′-tetramethyl-cucurbit[6]uril (TMeQ[6]) with a series of lanthanide cations (Ln³⁺) was investigated in neutral water and in acidic solution. Analysis by single crystal X-ray diffraction revealed that different isomorphous families formed under different synthetic conditions. Such differences in the interaction between TMeQ[6] and Ln³⁺ could potentially be used for isolating heavier Ln³⁺ from their lighter counterparts in neutral solution, and lighter lanthanide cations from their heavier counterparts in acidic solution

The Impact of Neighborhoods on Diabetes Risk and Outcomes: Centering Health Equity.

Neighborhood environments significantly influence the development of diabetes risk factors, morbidity, and mortality throughout an individuals life. The social, economic, and physical environments of a neighborhood all affect the health risks of individuals and communities and also affect population health inequities. Factors such as access to healthy food, green spaces, safe housing, and transportation options can impact the health outcomes of residents. Social factors, including social cohesion and neighborhood safety, also play an important role in shaping neighborhood environments and can influence the development of diabetes. Therefore, understanding the complex relationships between neighborhood environments and diabetes is crucial for developing effective strategies to address health disparities and promote health equity. This review presents landmark findings from studies that examined associations between neighborhood socioeconomic, built and physical, and social environmental factors and diabetes-related risk and outcomes. Our framework emphasizes the historical context and structural and institutional racism as the key drivers of neighborhood environments that ultimately shape diabetes risk and outcomes. To address health inequities in diabetes, we propose future research areas that incorporate health equity principles and place-based interventions

Environmental Pollution Analysis and Impact Study-A Case Study for the Salton Sea in California

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in PM2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality (O3 and CO) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O3 and CO, respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly PM2.5 and PM10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for PM2.5 and PM10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily PM2.5 and PM10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978