Qatar FIFA 2022 was the first FIFA Football World Cup to be hosted by an Arab state and was predicted by some to fail. However, it did not only succeed but also showed a new display of destination sustainability upon hosting mega-sport events and linked tourism. Yet, some impacts tend to be long-term and need further analysis. The study aims to understand both positive and negative impacts on destination sustainability resulting from hosting mega-sport events, using bibliometric analysis of published literature during the last forty-seven years, and reflecting on the recent World Cup 2022 tournament in Qatar. A total of 2519 sources containing 665 open-access articles with 10,523 citations were found using the keywords “sport tourism” and “mega-sport”. The study found various literature researching the economic impacts in-depth, less on environmental impacts, and much less on social and cultural impacts on host communities. Debates exist in the literature concerning presumed economic benefits and motivations for hosting, and less on actual results achieved. Although World Cup 2022 is considered the most expensive among previous versions, destination sustainability seems to have benefited from the event’s hosting. Socio-cultural impacts of hosting mega-sport events seem to be addressed to an extent in the Qatar version of the World Cup, as well as environmental impacts while creating a unique image for FIFA 2022 and the destination itself. FIFA showcased this as using carbon-neutral technologies to create the micro-climate including perforated walls in the eight state-of-the-art stadiums, with the incorporation of a circular modular design for energy and water efficiency and zero-waste deconstruction post-event. The global event also drew attention and respect to the local community and underprivileged groups such as people with disabilities. Further research is needed to understand the demand-side perspective including the local community of Qatar and the event’s participants, and to analyze the long-term impacts and lessons learned from the Qatari experience.

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.

This study investigates the influence of service quality, destination facilities, destination image, and tourist satisfaction on tourist loyalty in the Pasar Lama Chinatown area of Tangerang City. Utilizing data from 400 respondents, the study employed structured questionnaires analyzed through descriptive statistics, reliability analysis, exploratory and confirmatory factor analysis, and structural equation modeling (SEM). The results reveal that service quality (β = 0.47, p < 0.001), destination facilities (β = 0.33, p < 0.001), and destination image (β = 0.4, p < 0.001) all significantly enhance tourist satisfaction, which in turn has a strong positive effect on loyalty (β = 0.58, p < 0.001). Direct paths also show that service quality, destination facilities, and destination image independently contribute to tourist loyalty. Bootstrapping confirms satisfaction’s mediating role between these factors and loyalty. Practical recommendations suggest prioritizing service quality improvements, facility enhancements, and a positive destination image to foster loyalty and promote tourism sustainability in Pasar Lama, China. These insights assist tourism managers in developing strategies to enhance long-term visitor retention and engagement in the area.

In view of the fact that the convolution neural network segmentation method lacks to capture the global dependency of infected areas in COVID-19 images, which is not conducive to the complete segmentation of scattered lesion areas, this paper proposes a COVID-19 lesion segmentation method UniUNet based on UniFormer with its strong ability to capture global dependency. Firstly, a U-shaped encoder-decoder structure based on UniFormer is designed, which can enhance the cooperation ability of local and global relations. Secondly, Swin spatial pyramid pooling module is introduced to compensate the influence of spatial resolution reduction in the encoder process and generate multi-scale representation. Multi-scale attention gate is introduced at the skip connection to suppress redundant features and enhance important features. Experiment results show that, compared with the other four methods, the proposed model achieves better results in Dice, loU and Recall on COVID-19-CT-Seg and CC-CCIII dataset, and achieves a more complete segmentation of the lesion area.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.