Effectiveness of WeChat-Group-Based Parental Health Education in Preventing Unintentional Injuries Among Children Aged 0-3: Randomized Controlled Trial in Shanghai

BACKGROUND: Unintentional injuries to children are a major public health problem. The online social media is a potential way to implement health education for caregivers in online communities. Using WeChat, a free and popular social media service in China, this study evaluated the effectiveness of social online community-based parental health education in preventing unintentional injuries in children aged 0-3. METHODS: We recruited 365 parents from two community health centers in Shanghai and allocated them into intervention and control groups randomly. Follow-up lasted for one year. The intervention group received and followed their WeChat group and a WeChat official account for dissemination of reliable medical information. The control group received only the WeChat group. RESULTS: Between the intervention and control groups, changes in unintentional injuries (OR = 1.71, 95% CI: 1.02-2.87, P = .04), preventability (β = 0.344, 95% CI: 0.152-0.537, P \u3c .001), daily supervision behavior (β = 0.503, 95% CI: 0.036-0.970, P = .04), and behaviors for preventing specific injuries (β = 2.198, 95% CI: 1.530-2.865, P \u3c .001) were significantly different, and change in first-aid skills for treating a tracheal foreign body were nearly significant (P = .06). CONCLUSIONS: The WeChat-group-based parental health education can reduce the occurrence of unintentional child injuries by improving parents\u27 skills, beliefs, and behaviors. Online social communities promote health education and reduce unintentional injuries among children. TRIAL REGISTRATION: ChiCTR1900020753. Registered on January 17, 2019

FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network's weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions.Comment: 13 pages, 6 figures, Accepted at Medical Image Analysis Journal (MedIA

Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

Research on safety resource allocation of coal mine production logistics system

For safety resource allocation of coal mine production logistics system, regression analysis method was used to fit an objective function of safety risk and cost on the basis of constructing safety resource index system of safety risk and cost. A multi-objective optimization model of safety resource allocation of coal mine production logistics system was constructed and adaptive evolutionary particle swarm algorithm was used to solve the optimization model. The example analysis result shows that the adaptive evolutionary particle swarm optimization algorithm can obtain different feasible solutions which satisfy multi-objective optimization requirements of safety resource allocation of coal mine production logistics system

Safety evaluation model of coal mine based on principal component and cluster analysis

Firstly, an evaluation index system of coal mine safety was established from five aspects including people's behavior, safety management, equipment facilities, natural conditions and safety technology and supervision mechanism. Secondly, a safety evaluation model of coal mine was established based on principal component analysis and cluster analysis. Principal component analysis is used to choose comprehensive indexes, so as to reduce the number of evaluation indexes. Cluster analysis is used to classify and evaluate safety status of each coal mine enterprise, so as to analyze similarities and differences. Finally, safety status of 40 coal mine enterprises in a province was evaluated, and application steps of the model were introduced. The application result shows that evaluation result concluded by the model can reflect safety status of coal mine simply and intuitively

Fast Searching The Densest Subgraph And Decomposition With Local Optimality

Densest Subgraph Problem (DSP) is an important primitive problem with a wide range of applications, including fraud detection, community detection and DNA motif discovery. Edge-based density is one of the most common metrics in DSP. Although a maximum flow algorithm can exactly solve it in polynomial time, the increasing amount of data and the high complexity of algorithms motivate scientists to find approximation algorithms. Among these, its duality of linear programming derives several iterative algorithms including Greedy++, Frank-Wolfe and FISTA which redistribute edge weights to find the densest subgraph, however, these iterative algorithms vibrate around the optimal solution, which are not satisfactory for fast convergence. We propose our main algorithm Locally Optimal Weight Distribution (LOWD) to distribute the remaining edge weights in a locally optimal operation to converge to the optimal solution monotonically. Theoretically, we show that it will reach the optimal state of a specific linear programming which is called locally-dense decomposition. Besides, we show that it is not necessary to consider most of the edges in the original graph. Therefore, we develop a pruning algorithm using a modified Counting Sort to prune graphs by removing unnecessary edges and nodes, and then we can search the densest subgraph in a much smaller graph.Comment: Need more revisio

Networks in small-group and whole-class structures in large knowledge building communities

This study compared small-group and whole-class structures in two large knowledge building communities. We analyzed students’ online notes on Knowledge Forum by KBDex (Oshima, Oshima, & Matsuzawa, 2012). Results found that students in the small-group structure showed better community knowledge advancement. However, the unbalanced distribution of expertise in the small-group class was also observed