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Opportunistic Mobile Social Networks (OMSNs), formed by mobile users with social relationships and characteristics, enhance spontaneous communication among users that opportunistically encounter each other. Such networks can be exploited to improve the performance of data forwarding. Discovering optimal relay nodes is one of the important issues for efficient data propagation in OMSNs. Although traditional centrality definitions to identify the nodes features in network, they cannot identify effectively the influential nodes for data dissemination in OMSNs. Existing protocols take advantage of spatial contact frequency and social characteristics to enhance transmission performance. However, existing protocols have not fully exploited the benefits of the relations and the effects between geographical information, social features and user interests. In this paper, we first evaluate these three characteristics of users and design a routing protocol called Geo-Social-Interest (GSI) protocol to select optimal relay nodes. We compare the performance of GSI using real INFOCOM06 data sets. The experiment results demonstrate that GSI overperforms the other protocols with highest data delivery ratio and low communication overhead.

Ontology user portraits describe the semantic structure of users’ interests. It is very important to study the similar relationship between user portraits to find the communities with overlapping interests. The hierarchical characteristics of user interest can generate multiple similarity relations, which is conducive to the formation of interest clusters. This paper proposed a method of overlapping community detection combining the hierarchical characteristics of user interest and the module distribution entropy of node. First, a hierarchical user interest model was constructed based on the ontology knowledge base to measure the multi-granularity topic similarity of users. Then, a heterogeneous hypergraph was established by using the multi-granularity topic similarity and the following similarity of users to represent the interest network. Based on the mechanism of module distribution entropy of nodes, the community detection algorithm was applied to identify the interested community. The real performance of the proposed algorithm on multiple networks was verified by experiments. The experimental results show that the proposed algorithm is better than the typical overlapping community detection algorithm in terms of accuracy and recall rate.

The Belgian theoretical particle physicists François Baron Englert is the 2013 Nobel Prize Laureate in Physics and is currently Professor emeritus at the Université libre de Bruxelles in Brussels, Belgium. He is also affiliated with the School of Physics and Astronomy of Tel Aviv University, Israel and the Institute for Quantum Studies at Chapman University, USA. Prof. Englert was awarded many notable awards, such as the J. J. Sakurai Prize for Theoretical Particle Physics in 2010, the Wolf Prize in Physics in 2004 and the High Energy and Particle Prize of the European Physical Society in 1997. Peter W. Higgs and he were jointly awarded the Nobel Prize in Physics 2013 for "the theoretical discovery of a mechanism that contributes to our understanding of the origin of mass of subatomic particles."

For an hour on 27 May 2015, six C.N. Yang scholars from Nanyang Technological University had the privilege of conducting an informal discussion with Prof. Yang. Coming from different faculties and subject groups, the students represented the spectrum of subject areas that were influenced by Prof Yang's work. Centring on the topics of inspiration and research, Prof. Yang, accompanied by Prof. Choo Hiap Oh (Professor, Department of Physics, National University of Singapore) and Prof. Kok Khoo Phua (Director, Institute of Advanced Studies, Nanyang Technological University), dispensed advice with some humour.

For human beings, facial expression is one of the most powerful and natural ways to communicate their emotions and intentions. A human being is able to recognize facial expressions effortlessly, but for a machine, this task is very difficult. Today, facial expression recognition is proving to be one of the most relevant applications in many fields such as human–computer interaction, medicine, security, education, etc. Facial Action Coding System (FACS) is a method that describes face movements. This later became a main description tool used in the studies concerned with facial expression. In this paper, we propose an action units recognition system which reflects interest emotion using deep learning, particularly the Convolutional Neural Network (CNN) architecture “MobileNetV2”. Our choice of this system is motivated by its success in image classification. Our system allows detecting action units which define interest emotion from an input image. In other words, our classifier differentiates interest facial movements from other affective states. This identification is very useful in several areas, particularly in e-learning: For example, knowing whether a distance learner is interested or not in the course certainly influences the quality of learning and reduces the dropout rate. Our proposed approach presented a very satisfactory recognition rate despite the absence of a large database.

This study explores the use of physics toys as an innovative tool in high school education to bring abstract theories to life and stimulate student curiosity. These educational aids engage students, make complex principles tangible, and promote active learning. Our investigation includes examples from the NUS High School of Mathematics and Science in Singapore, demonstrating that physics toys enhance understanding and critical thinking across a range of student abilities. We conclude that integrating toys into physics education offers a dynamic and interactive approach that not only enriches the learning experience but also fosters the development of future innovative thinkers and problem solvers.