BACKGROUND China’s older population is facing serious health challenges, including malnutrition and multiple chronic conditions. There is a critical need for tailored food recommendation systems. Knowledge graph–based food recommendations offer considerable promise in delivering personalized nutritional support. However, the integration of disease-based nutritional principles and preference-related requirements needs to be optimized in current recommendation processes. OBJECTIVE This study aims to develop a knowledge graph–based personalized meal recommendation system for community-dwelling older adults and to conduct preliminary effectiveness testing. METHODS We developed ElCombo, a personalized meal recommendation system driven by user profiles and food knowledge graphs. User profiles were established from a survey of 96 community-dwelling older adults. Food knowledge graphs were supported by data from websites of Chinese cuisine recipes and eating history, consisting of 5 entity classes: dishes, ingredients, category of ingredients, nutrients, and diseases, along with their attributes and interrelations. A personalized meal recommendation algorithm was then developed to synthesize this information to generate packaged meals as outputs, considering disease-related nutritional constraints and personal dietary preferences. Furthermore, a validation study using a real-world data set collected from 96 community-dwelling older adults was conducted to assess ElCombo’s effectiveness in modifying their dietary habits over a 1-month intervention, using simulated data for impact analysis. RESULTS Our recommendation system, ElCombo, was evaluated by comparing the dietary diversity and diet quality of its recommended meals with those of the autonomous choices of 96 eligible community-dwelling older adults. Participants were grouped based on whether they had a recorded eating history, with 34 (35%) having and 62 (65%) lacking such data. Simulation experiments based on retrospective data over a 30-day evaluation revealed that ElCombo’s meal recommendations consistently had significantly higher diet quality and dietary diversity compared to the older adults’ own selections (<i>P</i><.001). In addition, case studies of 2 older adults, 1 with and 1 without prior eating records, showcased ElCombo’s ability to fulfill complex nutritional requirements associated with multiple morbidities, personalized to each individual’s health profile and dietary requirements. CONCLUSIONS ElCombo has shown enhanced potential for improving dietary quality and diversity among community-dwelling older adults in simulation tests. The evaluation metrics suggest that the food choices supported by the personalized meal recommendation system surpass autonomous selections. Future research will focus on validating and refining ElCombo’s performance in real-world settings, emphasizing the robust management of complex health data. The system’s scalability and adaptability pinpoint its potential for making a meaningful impact on the nutritional health of older adults.
Background Nutrient needs vary over the lifespan. Improving knowledge of both population groups and care providers can help with healthier food choices, thereby promoting population health and preventing diseases. Providing evidence-based food knowledge online is credible, low cost, and easily accessible. Objective This study aimed to develop an online multimodal food data exploration platform for easy access to evidence-based diet- and nutrition-related data. Methods We developed an online platform named Food Atlas in collaboration with a multidisciplinary expert group from the National Institute for Nutrition and Health and Peking Union Medical College Hospital in China. To demonstrate its feasibility for Chinese food for pregnant women, a user-friendly and high-quality multimodal food knowledge graph was constructed, and various interactions with graph-structured data were developed for easy access, including graph-based interactive visualizations, natural language retrieval, and image-text retrieval. Subsequently, we evaluated Food Atlas from both the system perspective and the user perspective. Results The constructed multimodal food knowledge graph contained a total of 2011 entities, 10,410 triplets, and 23,497 images. Its schema consisted of 11 entity types and 26 types of semantic relations. Compared with 5 other online dietary platforms (Foodwake, Boohee, Xiachufang, Allrecipes, and Yummly), Food Atlas offers a distinct and comprehensive set of data content and system functions desired by target populations. Meanwhile, a total of 28 participants representing 4 different user groups were recruited to evaluate its usability: preparing for pregnancy (n=8), pregnant (n=12), clinicians (n=5), and dietitians (n=3). The mean System Usability Scale index of our platform was 82.5 (SD 9.94; range 40.0-82.5). This above-average usability score and the use cases indicated that Food Atlas is tailored to the needs of the target users. Furthermore, 96% (27/28) of the participants stated that the platform had high consistency, illustrating the necessity and effectiveness of health professionals participating in online, evidence-based resource development. Conclusions This study demonstrates the development of an online multimodal food data exploration platform and its ability to meet the rising demand for accessible, credible, and appropriate evidence-based online dietary resources. Further research and broader implementation of such platforms have the potential to popularize knowledge, thereby helping populations at different life stages make healthier food choices.
Aiming at the complexity and uncertainty of rock and soil body, the paper the paper introducing a new global intelligent optimization arithmetic – Difference Evolution (DE) and combined with stress return mapping nonlinear elastic-plastic Finite Element Method(FEM), realizing rapid elastic plastic parameters identification of surrounding rock. The analytical theory and method are introduced in detail, analyzes the tunnel of Dalian Metro by the proposed method, and gets satisfied results.
Aiming at the complexity and uncertainty of rock and soil body, the paper proposed a tunnel surrounding rock parameters identification method combining numerical simulation, particle swarm optimization and artificial neural network. The method acquired data set between rock soil parameters and monitoring displacement and trained artificial neural network. The analytical theory and method are introduced in detail, analyzes the tunnel of Dalian Metro by the proposed method, and gets satisfied results. Which states that the parameters identification method based on PSO-ANN is feasible and has good foreground.
The existing music projects terminal are mostly websites that computer can access, the mobile client and rare television set-top box terminals. Under the new forms of triple play, network music system of integrated information system supports three nets four terminal and with key technology such as network music creation, transmission and display and so on. In computer, mobile phone, radio and television four terminal, this paper mainly introduces the network music terminal system based on IPTV set-top box in the design thought, system architecture and main technology and so on, and present the final test data.
The rise of 5G edge computing technology brings new development opportunities for video applications. In this paper, a multi-channel video analysis method based on cloud-edge collaboration is designed. Considering edge computing resources and network transmission time, a cloud-edge collaborative multi-channel live video processing framework is proposed. The proposed method makes full use of the processing advantages of edge computing and the cloud-edge collaboration, which can effectively meet the needs of efficient computing and network transmission for live video streaming. This paper analyzes the cloud-edge collaborative multi-channel video processing process in details. Finally, this paper conducts the test comparison between 4G and 5G live network environment, and the deployment verification in 5G network environment. The effectiveness of the cloud-edge collaborative multi-channel video solution in the 5G environment is verified through the test.
Dietary monitoring is critical to maintaining human health. Social media platforms are widely used for daily recording and communication for individuals' diets and activities. The textual content shared on social media offers valuable resources for dietary monitoring.This study aims to describe the development of iFood, an applet providing personal dietary monitoring based on social media content, and validate its usability, which will enable efficient personal dietary monitoring.The process of the development and validation of iFood is divided into four steps: Diet datasets construction, diet record and analysis, diet monitoring applet design, and diet monitoring applet usability assessment. The diet datasets were constructed with the data collected from Weibo, Meishijie, and diet guidelines, which will be used as the basic knowledge for further model training in the phase of diet record and analysis. Then, the friendly user interface was designed to link users with backend functions. Finally, the applet was deployed as a WeChat applet and 10 users from the Beijing Union Medical College have been recruited to validate the usability of iFood.Three dietary datasets, including User Visual-Textual Dataset, Dietary Information Expansion Dataset, and Diet Recipe Dataset have been constructed. The performance of 4 models for recognizing diet and fusing unimodality data was 40.43%(dictionary-based model), 18.45%(rule-based model), 59.95%(Inception-ResNet-v2), and 51.38% (K-nearest neighbor), respectively. Furthermore, we have designed a user-friendly interface for the iFood applet and conducted a usability assessment, which resulted in an above-average usability score.iFood is effective for managing individual dietary behaviors through its seamless integration with social media data. This study suggests that future products could utilize social media data to promote healthy lifestyles.
With the development of Web applications, large scale data are popular; and they are not only getting richer, but also ubiquitously interconnected with users and other objects in various ways, which brings about multi-view data with implicit structure. In this paper, we propose a novel hierarchical Bayesian mixture regression model, which discovers and then exploits the relationships among multiple views of the data to perform various machine learning tasks. A stochastic EM inference and learning algorithm is derived; and a parallel implementation in Hadoop MapReduce [9] paradigm is developed to scale up the learning. We apply the developed model and algorithm on click-through-rate (CTR) prediction and campaign targeting recommendation in online advertising to measure its effectiveness. The experiments on both synthetic data and large scale ads serving data from a real world online advertising exchange demonstrate the superior CTR prediction accuracy of our method compared to existing state-of-the-art methods. The results also show that our model can recommend high performance targeting features for online advertising campaigns.
Properly allocation of virtual machines is important for computing infrastructures scheduling. This paper presents systemic method on virtual machine array optimization control based on artificial intelligence and matrix control theory. According to request service data from users to provide proper VMs roughly via intelligent pattern recognition based on RBFNN, the data is sent to a multiple-targets optimization process to produce VMs allocation matrix precisely, thus enable to minimize the cast and enhance efficiency of the whole array to achieve low consumption optimization and ensure the stability of the system. Simulation experiments confirmed the effectiveness of this model and adaption ability in online dynamics.
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