Network of Tourism–Industrial Complex in Cities: Typologies and Implications through a Critical Literature Review

Although tourism has increasingly become an important activity with wide influences on the economic, social, and spatial development of a city, knowledge and interest mostly remain on its industrial performance and promotion. The synergy between tourism and city development is largely overlooked in many cases, resulting in suboptimal design and planning of city tourism activities and unfledged potentials of city development. The aim of the paper is to propose a view of tourism–industrial complex based on a synergistic perspective in order to clarify the systematic characteristics of urban tourism in an integrated, sustainable manner. Availing of bibliometric methods and drawing on city/urban tourism literature, this paper proposes a concept of tourism–industrial complex to cover current complicated and various tourism activities that are embedded in cities at diverse levels regardless of social, economic, and spatial factors. Then, four types of tourism–industrial complexes are proposed, including demand-driven, resource-dependent, externally forced, and hybrid-driven models. Due to the networked connectivity of urban tourism, urban backgrounds, tourism industry, and external circumstances all contribute to a coupling the tourism city development system. The results provide theoretical constructs and policy recommendations for optimization and sustainable city and tourism development.

Diagnostic performance of CO-RADS for COVID-19: a systematic review and meta-analysis.

OBJECTIVES: To investigate the diagnostic performance of the coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) for detecting COVID-19. METHODS: We searched PubMed, EMBASE, MEDLINE, Web of Science, Cochrane Library, and Scopus database until September 21, 2021. Statistical analysis included data pooling, forest plot construction, heterogeneity testing, meta-regression, and subgroup analyses. RESULTS: We included 24 studies with 8382 patients. The pooled sensitivity and specificity and the area under the curve (AUC) of CO-RADS ≥ 3 for detecting COVID-19 were 0.89 (95% confidence interval (CI) 0.85-0.93), 0.68 (95% CI 0.60-0.75), and 0.87 (95% CI 0.84-0.90), respectively. The pooled sensitivity and specificity and AUC of CO-RADS ≥ 4 were 0.83 (95% CI 0.79-0.87), 0.84 (95% CI 0.78-0.88), and 0.90 (95% CI 0.87-0.92), respectively. Cochran's Q test (p < 0.01) and Higgins I2 heterogeneity index revealed considerable heterogeneity. Studies with both symptomatic and asymptomatic patients had higher specificity than those with only symptomatic patients using CO-RADS ≥ 3 and CO-RADS ≥ 4. Using CO-RADS ≥ 4, studies with participants aged < 60 years had higher sensitivity (0.88 vs. 0.80, p = 0.02) and lower specificity (0.77 vs. 0.87, p = 0.01) than studies with participants aged > 60 years. CONCLUSIONS: CO-RADS has favorable performance in detecting COVID-19. CO-RADS ≥ 3/4 might be applied as cutoff values given their high sensitivity and specificity. However, there is a need for more well-designed studies on CO-RADS. KEY POINTS: • CO-RADS shows a favorable performance in detecting COVID-19. • CO-RADS ≥ 3 had a high sensitivity 0.89 (95% CI 0.85-0.93), and it may prove advantageous in screening the potentially infected people to prevent the spread of COVID-19. • CO-RADS ≥ 4 had high specificity 0.84 (95% CI 0.78-0.88) and may be more suitable for definite diagnosis of COVID-19.

Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression.

Automatic cough detection in the patients' realistic audio recordings is of great significance to diagnose and monitor respiratory diseases, such as COVID-19. Many detection methods have been developed so far, but they are still unable to meet the practical requirements. In this paper, we present a deep convolutional bidirectional long short-term memory (C-BiLSTM) model with boundary regression for cough detection, where cough and non-cough parts need to be classified and located. We added convolutional layers before the LSTM to enhance the cough features and preserve the temporal information of the audio data. Considering the importance of the cough event integrity for subsequent analysis, the novel model includes an embedded boundary regression on the last feature map for both higher detection accuracy and more accurate boundaries. We delicately designed, collected and labelled a realistic audio dataset containing recordings of patients with respiratory diseases, named the Corp Dataset. 168 h of recordings with 9969 coughs from 42 different patients are included. The dataset is published online on the MARI Lab website (https://mari.tongji.edu.cn/info/1012/1030.htm). The results show that the system achieves a sensitivity of 84.13%, a specificity of 99.82% and an intersection-over-union (IoU) of 0.89, which is significantly superior to other related models. With the proposed method, all the criteria on cough detection significantly increased. The open source Corp Dataset provides useful material and a benchmark for researchers investigating cough detection. We propose the state-of-the-art system with boundary regression, laying the foundation for identifying cough sounds in real-world audio data.