Cooperative order and excitation spectra in the bicomponent spin networks

A ferrimagnetic spin model composed of $S=1/2$ spin-dimers and $S=5/2$ spin-chains is studied by combining the bond-operator representation (for $S=1/2$ spin-dimers) and Holstein-Primakoff transformation (for $S=5/2$ spins). A finite interaction $J_{\rm DF}$ between the spin-dimer and the spin chain makes the spin chains ordered antiferromagnetically and the spin dimers polarized. The effective interaction between the spin chains, mediated by the spin dimers, is calculated up to the third order. The staggered magnetization in the spin dimer is shown proportional to $J_{\rm DF}$. It presents an effective staggered field reacting on the spin chains. The degeneracy of the triplons is lifted due to the chain magnetization and a mode with longitudinal polarization is identified. Due to the triplon-magnon interaction, the hybridized triplon-like excitations show different behaviors near the vanishing $J_{\rm DF}$. On the other hand, the hybridized magnon-like excitations open a gap $\Delta_A\sim J_{\rm DF}$. These results consist well with the experiments on Cu$_{2}$Fe$_{2}$Ge$_{4}$O$_{13}$.Comment: 7 pages, 5 figure

Application of Big Data in Tourism Destination Management: A Case Study of Changsha City

In the era of information technology, the utilization of big data technology is rapidly growing, leading to significant changes in the tourism industry. Big data not only creates more business opportunities for the industry but also drives the transformation and enhancement of tourist destinations and the implementation of efficient management. This study employs two research methods: literature review and case analysis. Firstly, by reviewing relevant literature, the latest research findings and trends in big data technology for tourism destination management are summarized. Secondly, through case analysis, a comprehensive understanding of the current situation and challenges in the application of big data technology in tourism destination management in Changsha is obtained. Leveraging the Changsha cultural and tourism data platform, this study retrieves information such as tourist reception data of tourism destinations in Changsha and assesses the impact of Changsha’s big data technology on tourism destination management. The research reveals limitations and challenges in the application of big data technology in Changsha’s tourism destination management, including data privacy protection and technical security, which require further exploration in future practices. The goal of this study is to offer insights for the application of big data in tourism destination management in Changsha and provide guidance for destination managers in similar cities

La fragilité comme prédicteur de la durée du séjour hospitalier après les chirurgies orthopédiques majeures électives chez les patients âgés

Avec le vieillissement de la population et le développement des maladies ostéoarticulaires chroniques, le nombre de chirurgies de remplacement de genou et de hanche est en augmentation au Canada. La fragilité, définie comme un état de perte de réserve physiologique et une incapacité à répondre adéquatement à un stresseur externe est aussi en hausse. Nous avons donc réalisé une étude prospective servant à déterminer la prévalence de la fragilité dans une population âgée en attente de chirurgie orthopédique élective et avons évalué si la fragilité est associée avec des pronostics postopératoires cliniques (transfert en centre de réhabilitation et séjour hospitalier prolongé). Quatre-vingt-sept patients de 65 ans et plus ont été recrutés à la clinique préopératoire de l’Hôpital Maisonneuve-Rosemont. La prévalence de la fragilité était de 4,5 % avec l’échelle de fragilité clinique et de 22,9 % avec l’échelle FRAIL. La moyenne d’âge de notre cohorte était de 73 ans et 65.5% des patients étaient des femmes. Les patients non robustes sur l’échelle de fragilité clinique étaient statistiquement associés avec un plus grand taux de congé à un centre de réadaptation (p=0.038) et un séjour prolongé (p=0.005), alors qu’il n’y avait pas cette association avec l’échelle FRAIL. Par contre, plus d’études doivent être réalisées pour confirmer nos données avant des études de type interventionnelles.With the ageing population and the development of osteoarthritis, total joint replacement procedures are increasing in numbers. Frailty, defined as a lack of physiological reserves and the inability to adequately respond to external stressors is also rising. Therefore, we conducted a prospective study to assess the prevalence of undetected frailty among elderly patients awaiting elective orthopedic surgery and to assess whether it is associated with adverse outcomes (transfer to rehabilitation center and prolonged hospital length of stay). A total of 87 patients 65 years and older were recruited at the preoperative evaluation clinic at Maisonneuve-Rosemont hospital. The Clinical Frailty Scale classified 4.5% of all patients are frail while the FRAIL scale classified 22.9% as frail. The mean age of our cohort was 73 years old and 65.5% were female patients. Patient classified as not robust on the Clinical Frailty Scale was significantly associated with discharge to rehabilitation center (p=0.038) and longer hospital length of stay (p=0.005) while not for those classified on the FRAIL scale. More studies are needed to confirm our findings before future interventional trials

Partial wave effects in the heavy quarkonium radiative electromagnetic decays

In a previous paper \cite{Bc}, it was pointed out that the wave functions of all particles are not pure waves, besides the main partial waves, they all contain {other partial waves}. It is very interesting to know what role these different partial waves play in particle transitions. Therefore, by using the Bethe-Salpeter equation method, we study the radiative electromagnetic decays $\psi\rightarrow\gamma\chi_{_{cJ}}$ and $\Upsilon\rightarrow\gamma\chi_{_{bJ}}$ ($J=0,1,2$). We find that for the $S$ and $P$ wave dominated states, like the $\psi(2S)$, $\Upsilon(2S)$, $\chi_{_{cJ}}(1P)$, and $\chi_{_{bJ}}(1P)$ etc., the dominant $S$ and $P$ waves provide main and nonrelativistic contrition to the decays; other partial waves mainly contribute to the relativistic correction. For the states like the $\psi(1D)$, $\Upsilon(2D)$, $\chi_{c2}(1F)$, and $\chi_{b2}(1F)$ etc., they are the $S-P-D$ mixing state dominated by $D$ wave or the $P-D-F$ mixing state dominated by $F$ wave. Large decay widths are found in the transitions $\psi(2D)\to \chi_{c2}(1F)$, $\Upsilon(1D)\to \chi_{bJ}(1P)$, and $\Upsilon(2D)\to \chi_{bJ}(2P)$ etc., which may be helpful to study the missing states $\chi_{c2}(1F)$, $\Upsilon(1D)$, and $\Upsilon(2D)$.Comment: 31 pages, 19 table

Multiplayer Serious Games Supporting Programming Learning

Computational thinking (CT) is crucial in education for providing a multifaceted approach to problem-solving. However, challenges exist such as supporting teachers' knowledge of CT and students' desire to learn it, particularly for non-technical students. To combat these challenges, Computer Supported Collaborative Learning (CSCL) has been introduced in classrooms and implemented using a variety of technologies, including serious games, which have been adopted across several domains aiming to appeal to various demographics and skill levels. This research focuses on a Collaborative Multiplayer Serious Game (MSG) for CT skill training. The architecture is aimed at young students and is designed to aid in the learning of programming and the development of CT skills. The purpose of this research is to conduct an empirical study to assess the multiplayer game gameplay mechanics for collaborative CT learning. The proposed game leverages a card game structure and contains complex multi-team multi-player processes, allowing students to communicate and absorb sequential and conditional logics as well as graph routing in a 2D environment. A preliminary experiment was conducted with four fourth-graders and eight sixth-graders from a French school in Morocco who have varying levels of understanding of CT. Participants were split into three groups each with two teams and were required to complete a 16-question multiple-choice quiz before and after playing the same game to assess their initial structural programming logics and the effectiveness of the MSG. Questionnaires were collected along with an interview to gather feedback on their gaming experiences and the game’s role in teaching and learning. The results demonstrate that the proposed MSG had a favourable effect on the participants’ test scores as the scores of 4 of the teams increased and 1 remained the same. All students performed well on the sequential and conditional logics, which was significantly better than the achievement of the Bebras test of the graph routing. Furthermore, according to the participants, the game provides an appealing environment that allows players to immerse themselves in the game and the competitive aspect of the game adds to its appeal and helps develop teamwork, coordination, and communication skills

Swarm Intelligence Optimization Algorithms and Their Application

Swarm intelligence optimization algorithm is an emerging technology tosimulate the evolution of the law of nature and acts of biological communities, it has simple and robust characteristics. The algorithm has been successfully applied in many fields. This paper summarizes the research status of swarm intelligence optimization algorithm and application progress. Elaborate the basic principle of ant colony algorithm and particle swarm algorithm. Carry out a detailed analysis of drosophila algorithm and firefly algorithm developed in recent years, and put forward deficiencies of each algorithm and direction for improvement

Marginal Nodes Matter: Towards Structure Fairness in Graphs

In social network, a person located at the periphery region (marginal node) is likely to be treated unfairly when compared with the persons at the center. While existing fairness works on graphs mainly focus on protecting sensitive attributes (e.g., age and gender), the fairness incurred by the graph structure should also be given attention. On the other hand, the information aggregation mechanism of graph neural networks amplifies such structure unfairness, as marginal nodes are often far away from other nodes. In this paper, we focus on novel fairness incurred by the graph structure on graph neural networks, named \emph{structure fairness}. Specifically, we first analyzed multiple graphs and observed that marginal nodes in graphs have a worse performance of downstream tasks than others in graph neural networks. Motivated by the observation, we propose \textbf{S}tructural \textbf{Fair} \textbf{G}raph \textbf{N}eural \textbf{N}etwork (SFairGNN), which combines neighborhood expansion based structure debiasing with hop-aware attentive information aggregation to achieve structure fairness. Our experiments show \SFairGNN can significantly improve structure fairness while maintaining overall performance in the downstream tasks.Comment: SIGKDD Explorations (To Appear

Adaptive loose optimization for robust question answering

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalizability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in \url{https://github.com/reml-group/ALO}.Comment: 13 pages,8 figure