Research on Guidance Methods of Digital Twin Robotic Arms Based on User Interaction Experience Quantification.

The evolution of the manufacturing sector coupled with advancements in digital twin technology has precipitated the extensive integration of digital twin robotic arms within the industrial domain. Notwithstanding this trend, there exists a paucity of studies examining the interaction of these robotic arms in virtual reality (VR) contexts from the user's standpoint. This paper delves into the virtual interaction of digital twin robotic arms by concentrating on effective guidance methodologies for the input of their target motion trajectories. Such a focus is pivotal to optimize input precision and efficiency, thus contributing to research on the virtual interaction interfaces of these robotic arms. During empirical evaluations, metrics related to human-machine interaction, such as objective operational efficiency, precision, and subjective workload, were meticulously quantified. Moreover, the influence of disparate guidance methods on the interaction experience of digital twin robotic arms and their corresponding scenarios was investigated. Consequent findings offer pivotal insights regarding the efficacy of these guidance methods across various scenarios, thereby serving as an invaluable guide for future endeavors aiming to bolster interactive experiences in devices akin to digital twin robotic arms.

[Association between the diet balance index-based dietary quality and cognitive function among Zhejiang population aged 40 years and older].

OBJECTIVE: To evaluation the dietary quality of Zhejiang population aged 40 years and older using the Dietary Balance Index(DBI) and to analyze the association between dietary quality and cognitive function. METHODS: The dietary information was collected with the help of questionnaire survey, a 3-day dietary recall and household condiment weighing method from Zhejiang participants of the 2018 wave of the China Health and Nutrition Survey aged 40 years and older, and the food and energy intakes were calculated. The cognitive function was assessed by the Mini Mental Status Examination. Dietary quality was evaluated using the DBI method. A multivariate Logistic regression model was used to examine the association between dietary quality and the risk of cognitive impairment. RESULTS: Among 640 participants aged 40 years and older, 14.2% had cognitive impairment. Univariate analysis showed that those with cognitive impairment had higher cereal(P=0.001), particularly, higher rice and products intake(P<0.001), as well as higher egg intake(P=0.008) than those with normal cognitive function; while the intake of soybean and its product(P=0.025) was lower. Those with cognitive impairment had higher DBI score of cereal(P=0.006) and high bound score(HBS)(P=0.028)than those with normal cognitive function. After adjustment for possible confounding factors, Logistic regression showed that moderated and severe over-consumption was positively associated with cognitive impairment(OR=2.486, 95% CI 1.130-5.470, P=0.024). CONCLUSION: Over-consumption may increase the risk of cognitive impairment among aged Zhejiang population, and should be used to prevent or reduce cognitive decline by improving the quality of the diet through a reasonable dietary mix.

Autoencoder-assisted latent representation learning for survival prediction and multi-view clustering on multi-omics cancer subtyping.

Cancer subtyping (or cancer subtypes identification) based on multi-omics data has played an important role in advancing diagnosis, prognosis and treatment, which triggers the development of advanced multi-view clustering algorithms. However, the high-dimension and heterogeneity of multi-omics data make great effects on the performance of these methods. In this paper, we propose to learn the informative latent representation based on autoencoder (AE) to naturally capture nonlinear omic features in lower dimensions, which is helpful for identifying the similarity of patients. Moreover, to take advantage of survival information or clinical information, a multi-omic survival analysis approach is embedded when integrating the similarity graph of heterogeneous data at the multi-omics level. Then, the clustering method is performed on the integrated similarity to generate subtype groups. In the experimental part, the effectiveness of the proposed framework is confirmed by evaluating five different multi-omics datasets, taken from The Cancer Genome Atlas. The results show that AE-assisted multi-omics clustering method can identify clinically significant cancer subtypes.

A New Many-Objective Evolutionary Algorithm Based on Generalized Pareto Dominance.

In the past several years, it has become apparent that the effectiveness of Pareto-dominance-based multiobjective evolutionary algorithms deteriorates progressively as the number of objectives in the problem, given by M , grows. This is mainly due to the poor discriminability of Pareto optimality in many-objective spaces (typically M ≥ 4 ). As a consequence, research efforts have been driven in the general direction of developing solution ranking methods that do not rely on Pareto dominance (e.g., decomposition-based techniques), which can provide sufficient selection pressure. However, it is still a nontrivial issue for many existing non-Pareto-dominance-based evolutionary algorithms to deal with unknown irregular Pareto front shapes. In this article, a new many-objective evolutionary algorithm based on the generalization of Pareto optimality (GPO) is proposed, which is simple, yet effective, in addressing many-objective optimization problems. The proposed algorithm used an "( M-1 ) + 1" framework of GPO dominance, ( M-1 )-GPD for short, to rank solutions in the environmental selection step, in order to promote convergence and diversity simultaneously. To be specific, we apply M symmetrical cases of ( M-1 )-GPD, where each enhances the selection pressure of M-1 objectives by expanding the dominance area of solutions, while remaining unchanged for the one objective left out of that process. Experiments demonstrate that the proposed algorithm is very competitive with the state-of-the-art methods to which it is compared, on a variety of scalable benchmark problems. Moreover, experiments on three real-world problems have verified that the proposed algorithm can outperform the others on each of these problems.

Hierarchical Topology-Based Cluster Representation for Scalable Evolutionary Multiobjective Clustering.

Evolutionary multiobjective clustering (MOC) algorithms have shown promising potential to outperform conventional single-objective clustering algorithms, especially when the number of clusters k is not set before clustering. However, the computational burden becomes a tricky problem due to the extensive search space and fitness computational time of the evolving population, especially when the data size is large. This article proposes a new, hierarchical, topology-based cluster representation for scalable MOC, which can simplify the search procedure and decrease computational overhead. A coarse-to-fine-trained topological structure that fits the spatial distribution of the data is utilized to identify a set of seed points/nodes, then a tree-based graph is built to represent clusters. During optimization, a bipartite graph partitioning strategy incorporated with the graph nodes helps in performing a cluster ensemble operation to generate offspring solutions more effectively. For the determination of the final result, which is underexplored in the existing methods, the usage of a cluster ensemble strategy is also presented, whether k is provided or not. Comparison experiments are conducted on a series of different data distributions, revealing the superiority of the proposed algorithm in terms of both clustering performance and computing efficiency.