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Fat is one of the most important nutritional components in foods. It not only endows foods with unique flavor and taste and provides the energy needed by the human body, but also has a significant impact on the structure and overall quality of foods. However, excessive intake of fat is not conducive to human health. Therefore, it is hoped that the use of fat mimics to replace fat in food will reduce the harm of excessive intake of fat to the human body while maintaining the original flavor of foods as much as possible. This article reviews the classification of fat replacers and fat mimics and recent research on the role and application of fat mimics in foods, outlines the basis for the classification of fat replacers into fat substitutes and fat mimics, with emphasis on the mechanisms of action, advantages, limitations, roles and applications of single and mixed fat mimics. It also proposes future strategies for the research and development of fat mimics in order to provide a direction for the development of healthy and safe fat mimics.

As the most widely used supervised classification algorithm, K nearest neighbor (KNN) algorithm is often inefficient in the processing of large-scale and multidimensional data. Therefore, an improved KNN algorithm for high dimensional large data processing is proposed. Firstly, deep neural networks (DNN) is used as feature extractor and dimension reduction is carried out to learn the most appropriate representation form of depth feature. Then, the appropriate number of clusters and the initial clustering center, obtained through the density Canopy algorithm become the input parameters of the subsequent K-means clustering. Finally, the learned data are clustered, and Hashing strategy in approximate similarity search (ASS) is used to cluster partitioning according to its approximate similarity, and the result is token as a new training sample of KNN classifier. In addition, considering that the nearest neighbor samples to be searched may fall in different clusters and the performance of KNN search is reduced, an additional clustering enhancement strategy is adopted in clustering, which effectively alleviates this situation. Five different data sets are used for comparison test. The results show that, compared with the experimental algorithms, this algorithm can not only greatly improve the accuracy of KNN classification, but also effectively improves the classification efficiency of the algorithm, reduces the distance required for searching, and has good robustness for noise data.

Unlike dense and regularly distributed 2D raster images, 3D point clouds are irregular and disordered, thus applying convolution on them can be difficult. A method for direct convolution of original 3D point clouds is proposed. This method uses Gaussian kernel density estimation and multi-layer perceptron (MLP) network to learn the density function. Besides, the density scale of the learning point and the relative position of the point are fitted by the weight function of the approximated MLP network to obtain the weight value of each point in the local area. The whole convolution kernel can be seen as a nonlinear function of 3D local coordinates composed of the weight function and the density function, which can be used for translation invariant and permutation invariant convolution of any set of points in 3D space. In addition, multi- scale sampling grouping and normal features are employed to achieve the best effect of this network. In the classification experiment of point cloud datasets of ModelNet40 and ModelNet10, the accuracy of the network is 92.8% and 94.7% respectively, which acquires better performance than baseline. The image datasets of CIFAR-10 and MNIST are converted into point clouds to test, experiments on aforementioned datasets show that the performance of the network in 2D images is basically equivalent to that of the traditional 2D convolutional network. [ABSTRACT FROM AUTHOR]

The traditional consensus algorithm based on the Internet of Vehicles (IoV) system has the disadvantages of high latency, low reliability, and weak fault tolerance, and it cannot make real-time adjustments according to the actual environment, making the system vulnerable to malicious control, inefficiency, and poor environmental adaptability. To solve this problem, we propose a gradually accelerating environment adaptive consensus algorithm, AE-PBFT, that can be applied to IoV. It includes a trust management model that achieves gradual acceleration by recording the historical continuous behavior of nodes, thereby improving the efficiency of screening nodes with different intentions, accelerating the consensus process, and reducing latency. At the same time, we introduce a dynamic consensus group division mechanism based on environmental adaptive changes, which can adaptively adjust the number of nodes participating in the consensus process according to the needs of the operating environment, to deal with extreme situations, thereby improving the reliability and fault tolerance of the system. Experiments confirm that the performance of our proposed solution is superior to current solutions in terms of consensus latency and fault tolerance and is more suitable for the operating environment of IoV.

Introduction: Frailty significantly influences the prognosis of elderly patients diagnosed with heart failure. The assessment of frailty is a critical initial step in the management of these patients, as a systematic and precise evaluation facilitates the identification of individuals at high risk. This identification enables timely and targeted interventions, which can subsequently reduce the likelihood of adverse cardiovascular events and improve the quality of life for elderly patients with heart failure. Nevertheless, there exists a notable deficiency in research regarding the most effective frailty assessment tools specifically for elderly patients with heart failure in China. The objective of this study is to identify the frailty assessment tool that demonstrates the highest predictive value for outcomes in this population., Methods and Analysis: This study is a multicentre, prospective cohort investigation that commenced in October 2023 across three tertiary hospitals in Beijing, China. Employing a continuous enrolment strategy, the study encompasses all elderly patients diagnosed with heart failure who are undergoing either outpatient or inpatient treatment, continuing until an adequate sample size is achieved. Follow-up evaluations are scheduled every 3 months from the point of patient enrolment, extending until the 12th month post-enrolment. Comprehensive data collection, which includes demographic information, heart failure-related metrics, frailty assessments and significant biochemical test results, is conducted through face-to-face interviews at baseline., Ethics and Dissemination: Participant inclusion will depend on obtaining written informed consent from the patient or guardian. The trial protocol was approved by the Central Ethics Committee of Beijing Hospital. The approval letter number is 2023BJYYEC-356-01. Outcomes of the study will be published in a peer-reviewed scientific journal., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)