Digitalization is an important direction for the transformation of higher education, but the teaching of music majors still has problems, such as outdated methods and rigid models. In order to ...explore the digital transformation path of music education in colleges and universities, this paper designs and constructs a music emotion recognition model, which provides technical support solutions for the video-singing audition, appreciation of famous artists, and practice and correction of errors in the music classroom. Through feature extraction, the form of music expression emotion is transformed into music emotion features, using the wrapping method and filtering method for feature selection, using self-attention BiLSTM algorithm to establish the interoperability relationship between features and emotion model, and recognizing music emotion. The constructed model is utilized to aid in the teaching of music majors at a university as a teaching experiment object, and the teaching effect is examined. It was found that after the model-assisted teaching, the experimental class’s scores of music emotion comprehension ability, work element differentiation ability, emotion expression ability, and music psychology ability were all improved to different degrees, and compared with the pre-test scores showed a strongly significant difference (sig.<0.05). A significant improvement of 20.44% was seen in the total average score, which was 94.75. After the use of model-assisted teaching and the digitalization of music education, the quality of teaching in the experimental class was significantly improved, and this paper’s exploration of the digital transformation path of music education in colleges and universities was fruitful. This study has conducted an innovative exploration of the integration of modern information technology and music education, as well as the digital teaching reform of music majors in colleges and universities, and achieved good results.
Cancer will soon become the leading cause of death in every country in the twenty-first century. This study aimed to analyze the mortality and morbidity of 29 types of cancer in 204 countries or ...regions from 1990 to 2019 to guide global cancer prevention and control.
Detailed information for 29 cancer groups was collected from the Global Burden of Disease Study in 2019. The age-standardized incidence rate (ASIR) and age-standardized death rate (ASDR) of the 29 cancer groups were calculated based on sex, age, region, and country. In addition, separate analyses were performed for major cancer types.
In 2019, more than 10 million people died from cancer, which was approximately twice the number in 1990. Tracheal, bronchus, and lung (TBL) cancers collectively showed the highest death rate, and the ASDR of pancreatic cancer increased by 24%, which was cancer with the highest case fatality rate (CFR). The global cancer ASIR showed an increasing trend, with testicular cancer, thyroid cancer, and malignant skin melanoma showing a significant increase. The ASDR and ASIR of cancer in males were about 1.5 times higher than that in females. Individuals over 50 years had the highest risk of developing cancer, with incidences and deaths in this age group accounting for more than 85% of cancers in all age groups. Asia has the heaviest cancer burden due to its high population density, with esophageal cancer in this region accounting for 53% of the total fatalities related to this type of cancer in the world. In addition, the mortality and morbidity of most cancers increased with the increase in the development or socio-demographic index (SDI) in the SDI regions based on the World Bank's Human Development Index (HDI), with cancer characteristics varying in the different countries globally.
The global cancer burden continues to increase, with substantial mortality and morbidity differences among the different regions, ages, countries, gender, and cancer types. Effective and locally tailored cancer prevention and control measures are essential in reducing the global cancer burden in the future.
Pickering emulsions are an excellent platform for interfacial catalysis. However, developing simple and efficient strategies to achieve product separation and catalyst and emulsifier recovery is ...still a challenge. Herein, we report the reversible transition between emulsification and demulsification of a light‐responsive Pickering emulsion, triggered by alternating between UV and visible light irradiation. The Pickering emulsion is fabricated from Pd‐supported silica nanoparticles, azobenzene ionic liquid surfactant, n‐octane, and water. This phase behavior is attributed to the adsorption of azobenzene ionic liquid surfactant on the surface of the nanoparticles and the light‐responsive activity of ionic liquid surfactant. The Pickering emulsion can be used as a microreactor that enables catalytic reaction, product separation as well as emulsifier and catalyst recycling. Catalytic hydrogenation of unsaturated hydrocarbons at room temperature and atmospheric pressure has been performed in this system to demonstrate product separation and emulsifier and catalyst re‐use.
A light‐responsive Pickering emulsion was designed and prepared, which could be reversibly switched between stable and unstable upon exposure to visible and UV light, respectively. The reversible, light‐responsive phase behavior of the Pickering emulsion enabled its use as a microreactor, allowing highly efficient catalytic hydrogenation, followed by product separation and emulsifier and catalyst recycling.
Mobile edge computing is a new paradigm that can enhance the computation capability of end devices and alleviate communication traffic loads during transmission. Mobile edge computing is highly ...useful for emerging resource-hungry mobile applications. However, a key challenge for mobile edge computing systems is multiple resources allocation between Mobile Edge Clouds (MECs) and End Users (EUs), especially for multiple heterogeneous MECs and EUs. To address this problem, we propose a Stackelberg game-based framework in which EUs and MECs act as followers and leaders, respectively. The proposed framework aims to compute a Stackelberg equilibrium solution in which each MEC achieves the maximum revenue while each EU obtains utility-maximized resources under budget constraints. We decompose the multiple resources allocation and pricing problem into a set of subproblems in which each subproblem only considers a single resource type. The Stackelberg game framework is constructed for each subproblem wherein each player (i.e., an EU) can selfishly maximize its utility by selecting an appropriate strategy in the strategy space. We prove the existence of the subgame Stackelberg equilibrium and develop algorithms to determine the Stackelberg equilibrium for each resource type, including an optimal demand computation algorithm, to determine the best resource demand strategy for an EU and an iterative algorithm to find an equilibrium price. The equilibrium solutions of all subgames constitute the equilibrium solution of the original problem. We also conduct simulation experiments of our game, such as numerical data for the Stackelberg equilibrium, numerical data for the convergence of the Stackelberg equilibrium, and numerical data as the system size increases. Finally, we demonstrate that an EU with idle resources can play the role of an MEC.
•A Stackelberg game-based framework is proposed for mobile edge clouds and end users in mobile edge computing.•The existence of the equilibrium for the Stackelberg game has been proved.•Proposed algorithms can compute a Stackelberg equilibrium.•An end user with idle resources can play the role of a mobile edge cloud.
Sparse representation has been widely exploited to develop an effective appearance model for object tracking due to its well discriminative capability in distinguishing the target from its ...surrounding background. However, most of these methods only consider either the holistic representation or the local one for each patch with equal importance, and hence may fail when the target suffers from severe occlusion or large-scale pose variation. In this paper, we propose a simple yet effective approach that exploits rich feature information from reliable patches based on weighted local sparse representation that takes into account the importance of each patch. Specifically, we design a reconstruction-error based weight function with the reconstruction error of each patch via sparse coding to measure the patch reliability. Moreover, we explore spatio-temporal context information to enhance the robustness of the appearance model, in which the global temporal context is learned via incremental subspace and sparse representation learning with a novel dynamic template update strategy to update the dictionary, while the local spatial context considers the correlation between the target and its surrounding background via measuring the similarity among their sparse coefficients. Extensive experimental evaluations on two large tracking benchmarks demonstrate favorable performance of the proposed method over some state-of-the-art trackers.
Given growing attention toward the effects on COVID-19 on tourism, a number of institutions have made macro-level predictions related to the disease. More micro-level research are, however, needed. ...This study seeks to advance the understanding of tourists' potential behavioral transformation by reviewing psychological distance and construal level theory, as well as the relationship between psychological distance and perceived risk. Multiple dimensions of psychological distance and perceived risk are summarized with respect to COVID-19. The discussion suggests that global health emergencies evoke three types of tourism pattern: from general to elaborate, from open-hearted to closed, and from radical to conservative. These categories provide a conceptual foundation for empirical research considering contextual and individual stimuli. Practically, this paper highlights strategies to reduce individuals’ risk perceptions, encourage specific types of tourism, and regulate unethical consumption. The recommendations also encourage the analysis of crisis recovery and relevant market analysis by tourism professionals and marketers.
•Uses the rationality of applying psychological distance and construal level theory to tourism research on COVID-19.•Investigates the mechanism between psychological distance and perceived risk.•Identifies tourists' psychological distance dimensions and perceived risks.•Summarizes the transformation of tourists' behavior patterns after the outbreak of COVID-19.•Proposes research opportunities based on the conceptual model put forth in this paper.
In this paper, we propose a novel local sparse representation-based tracking framework for visual tracking. To deeply mine the appearance characteristics of different local patches, the proposed ...method divides all local patches of a candidate target into three categories, which are stable patches, valid patches, and invalid patches. All these patches are assigned different weights to consider the different importance of the local patches. For stable patches, we introduce a local sparse score to identify them, and discriminative local sparse coding is developed to decrease the weights of background patches among the stable patches. For valid patches and invalid patches, we adopt local linear regression to distinguish the former from the latter. Furthermore, we propose a weight shrinkage method to determine weights for different valid patches to make our patch weight computation more reasonable. Experimental results on public tracking benchmarks with challenging sequences demonstrate that the proposed method performs favorably against other state-of-the-art tracking methods.
Nanopatterned surfaces are believed to kill bacteria through physical deformation, a mechanism that has immense potential against biochemical resistance. Because of its elusive nature, this mechanism ...is mostly understood through biophysical modeling. Problematically, accurate descriptions of the contact mechanics and various boundary conditions involved in the bacteria-nanopattern interaction remain to be seen. This may underpin conflicting predictions, found throughout the literature, regarding two important aspects of the mechanism—that is, its critical action site and relationship with geometry. Herein, a robust computational analysis of bacteria-nanopattern interaction is performed using a three-dimensional finite element modeling that incorporates relevant continuum mechanical properties, multilayered envelope structure, and adhesion interaction conditions. The model is applied to more accurately study the elusory mechanism and its enhancement via nanopattern geometry. Additionally, micrographs of bacteria adhered on a nanopatterned cicada wing are examined to further inform and verify the major modeling predictions. Together, the results indicate that nanopatterned surfaces do not kill bacteria predominantly by rupture in between protruding pillars as previously thought. Instead, nondevelopable deformation about pillar tips is more likely to create a critical site at the pillar apex, which delivers significant in-plane strains and may locally rupture and penetrate the cell. The computational analysis also demonstrates that envelope deformation is increased by adhesion to nanopatterns with smaller pillar radii and spacing. These results further progress understanding of the mechanism of nanopatterned surfaces and help guide their design for enhanced bactericidal efficiency.
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