Hotel employee resilience during a crisis: conceptual and scale development

This research aims to address the lack of research on hotel employee resilience during a crisis (HERC) and the absence of a measurement scale to assess it. A mixed-method approach was used to conceptualize HERC, identify its dimensions, and build a measurement scale. In Study 1, an online survey of 69 employees from upscale hotels was conducted, revealing a five-factor HERC model comprising resistance, adaptability, cooperation, restoration, and thriving. Study 2 developed preliminary measurement items for HERC, which were refined through exploratory factor analysis (EFA). Study 3 conducted another round of surveys and used a confirmatory factor analysis (CFA) to verify the factors generated from the second study. This research provides a comprehensive five-factor model of employee resilience during a crisis and a corresponding measurement scale, offering a theoretical foundation for hotel managers to develop effective strategies to manage crises. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Effects of risk messages on tourists' travel intention: Does distance matter?

This study aims to examine how distance to risk center in the COVID-19 context moderates the effects of two contrasting risk message frames (amplifying vs. attenuating) on tourists' post-pandemic travel intention via the mediation of ontological security threat and perceived coping efficacy. Two experiments were designed to test the proposed conceptual model. Results of experiment 1 showed that risk messages predicted tourists' ontological security threat, perceived coping efficacy, and travel intention. Results of experiment 2 showed that ontological security threat and perceived coping efficacy partially mediated the effects of risk messages on travel intention. Moreover, distance to risk center moderated the relationships between risk message frames and travel intention via ontological security threat and perceived coping efficacy, demonstrating different patterns (i.e., "ripple effect”, "psychological typhoon eye effect”, "marginal zone effect”). This study contributes to an enhanced understanding of the effect of risk message framing in the COVID-19 context by clarifying the role of geographic distance, which is beneficial for destinations to adopt differentiated risk communication strategies for different pandemic areas and levels of pandemic severity. © 2023 The Authors

Effects of risk message on loneliness and travel intention: roles of conflictive family atmosphere and risk propensity in the context of COVID-19 pandemic

This study examines the effects of risk message frames on tourists' post-pandemic travel intention via the meditation of loneliness and went further to investigate the roles of conflictive family atmosphere and risk propensity in moderating these effects. A situational experiment was conducted in China resulting 622 valid responses. The study found that respondents in risk attenuating frame had higher travel intention than those in risk amplifying frame;social loneliness partially mediated the effect of risk message on travel intention. Conflictive family atmosphere moderated the effects of risk message on social loneliness and travel intention. And risk propensity alleviated the negative impact of risk message on travel intention. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Constructing custom-made radiotranscriptomic signatures from CT angiograms: An application in COVID-19 vascular inflammation

Background: Advances in computational methodologies have enabled processing of large datasets originating from imaging studies. However, most imaging biomarkers suffer from a lack of direct links with underlying biology, as they are only observationally correlated with pathophysiology. Purpose(s): To develop and validate a novel AI-assisted image analysis platform, by applying quantitative radiotranscriptomics that quantifies cytokinedriven vascular inflammation from routine CT angiograms (CTA) performed as part of clinical care in COVID-19. Method(s): We used this platform to train the radiotranscriptomic signature C19-RS, derived from the perivascular space around the aorta and the internal mammary artery in routine chest CTAs, to best describe cytokinedriven vascular inflammation, defined using transcriptomic profiles from RNA sequencing data from human arterial biopsies (A). This signature was validated externally in 358 clinically indicated CT pulmonary angiograms from patients with or without COVID-19 from 3 different geographical regions. Result(s): First, 22 patients who had a CTA before the pandemic underwent repeat CTA <6 months post COVID-19 infection (B). Compared with 22 controls (matched for age, gender, and BMI) C19-RS was increased only in the COVID-19 group (C). Next, C19-RS was calculated in a cohort of 331 patients hospitalised during the pandemic, and was higher in COVID-19 positives (adjusted OR=2.97 [95% CI: 1.43-6.27], p=0.004, D). C19-RS had prognostic value for in-hospital mortality in COVID-19, with HR=3.31 ([95% CI: 1.49-7.33], p=0.003) and 2.58 ([95% CI: 1.10-6.05], p=0.028) in two testing cohorts respectively (E, F), adjusted for clinical factors and biochemical biomarkers of inflammation and myocardial injury. The corrected HR for in-hospital mortality was 8.24 [95% CI: 2.16-31.36], p=0.002 for those who received no treatment with dexamethasone, but only 2.27 [95% CI: 0.69-7.55], p=0.18 in those who received dexamethasone subsequently to the C19-RS based image analysis, suggesting that vascular inflammation may have been a therapeutic target of dexamethasone in COVID-19. Finally, C19-RS was strongly associated (r=0.61, p=0.0003) with a whole blood transcriptional module representing dysregulation of coagulation and platelet aggregation pathways. Conclusion(s): We present the first proof of concept study that combines transcriptomics with radiomics to provide a platform for the development of machine learning derived radiotranscriptomics analysis of routine clinical CT scans for the development of non-invasive imaging biomarkers. Application in COVID-19 produced C19-RS, a marker of cytokine-driven inflammation driving systemic activation of coagulation, that predicts inhospital mortality and identifies people who will have better response to anti-inflammatory treatments, allowing targeted therapy. This AI-assisted image analysis platform may have applications across a wide range of vascular diseases, from infections to autoimmune diseases.

LOCATION AND CAPACITY PLANNING FOR PREVENTIVE HEALTHCARE FACILITIES WITH CONGESTION EFFECTS

A painful lesson got from pandemic COVID-19 is that preventive healthcare service is of utmost importance to governments since it can make massive savings on healthcare expenditure and promote the welfare of the society. Recognizing the importance of preventive healthcare, this research aims to present a methodology for designing a network of preventive healthcare facilities in order to prevent diseases early. The problem is formulated as a bilevel non-linear integer programming model. The upper level is a facility location and capacity planning problem under a limited budget, while the lower level is a user choice problem that determines the allocation of clients to facilities. A genetic algorithm (GA) is developed to solve the upper level problem and a method of successive averages (MSA) is adopted to solve the lower level problem. The model and algorithm is applied to analyze an illustrative case in the Sioux Falls transport network and a number of interesting results and managerial insights are provided. It shows that solutions to medium-scale instances can be obtained in a reasonable time and the marginal benefit of investment is decreasing. © 2023, Journal of Industrial and Management Optimization. All Rights Reserved.

Internationalism in climate action and China's role

The world is facing dual challenges of generating an economic recovery from the COVID-19 crisis, and transitioning to a low-carbon economy to tackle climate change. Strongly interrelated global challenges will require an integrated and coordinated response by all countries to manage the risk and lay the foundation for building back better. As the world's biggest emitter and the second-largest economy, China is a very important player in international collaboration and coordination in climate action. Against this backdrop, this paper looks into the increasingly crucial role that China is playing in global climate action, especially focusing on three aspects: China's domestic and foreign policymaking for the energy transition;its role in promoting multilateralism and international collaboration on building a sustainable world;and how it could accelerate climate action and diplomacy through research, development and innovation. In the critical decade of the 2020s, China has a great opportunity to further transform and upgrade its energy and industrial structures, promote research, development and the application of green and low-carbon technologies and intensify international climate cooperation on climate change. China should aim to be at the forefront of raising climate ambition and accelerating climate action for a sustainable and more equitable world. © 2023 IOP Publishing Ltd.

Systemic risk spillovers and the determinants in the stock markets of the Belt and Road countries

We construct time-varying tail risk networks to investigate systemic risk spillovers in the Belt and Road (B&R) stock markets during 2008–2021. Network metrics clearly reflect aggregate risk level and individual risk accumulation for the B&R stock markets under extreme events (e.g., 2008 financial crisis and COVID-19 pandemic). Tail-event driven network quantile regression analysis shows that network impacts of the B&R stock markets under different risk levels are asymmetric and regional heterogeneity. Panel analysis on determinants of systemic risk spillovers shows that cross-border investment and international trade are significant contagion channels while economic freedom is potential driver. © 2023 Elsevier B.V.

Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a high-frequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting;the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias;and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility. © 2022 Elsevier Inc.

Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning

We study the co-movement between innovative financial assets (i.e., FinTech-related stocks, green bonds and cryptocurrencies) and traditional assets. We construct a co-movement mode transmission network and discuss the network topology during the pre-COVID-19 and COVID-19 periods. We extract network topology information to predict the co-movement mode by machine learning algorithms. We further propose dynamic trading strategies based on the co-movement mode prediction. The empirical results show that (i) the evolution of co-movement is dominated by some key modes, and the mode transmission relies on intermediate modes and shows certain periodicity;(ii) the co-movement relationships are influenced by the ongoing COVID-19 outbreak;and (iii) the novel approach, which combines complex network and machine learning, is superior in co-movement mode prediction and can effectively bring diversification benefits. Our work provides valuable insights for market participants. © 2022 Elsevier B.V.

Interconnected multilayer networks: Quantifying connectedness among global stock and foreign exchange markets

This paper proposes a novel interconnected multilayer network framework based on variance decomposition and block aggregation technique, which can be further served as a tool of linking and measuring cross-market and within-market contagion. We apply it to quantifying connectedness among global stock and foreign exchange (forex) markets, and demonstrate that measuring volatility spillovers of both stock and forex markets simultaneously could support a more comprehensive view for financial risk contagion. We find that (i) stock markets transmit the larger spillovers to forex markets, (ii) the French stock market is the largest risk transmitter in multilayer networks, while some Asian stock markets and most forex markets are net risk receivers, and (iii) interconnected multilayer networks could signal the financial instability during the global financial crisis and the COVID-19 crisis. Our work provides a new perspective and method for studying the cross-market risk contagion. © 2023 Elsevier Inc.

Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a highfrequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting;the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias;and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility.

Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs

Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables for social distance monitoring in indoor spaces, permitting the recognition of basic shapes, without revealing the personal details of individuals. In this work, we demonstrate that an accurate detection of social distance violations can be achieved processing the raw output of a 8x8 IR array sensor with a small-sized Convolutional Neural Network (CNN). Furthermore, the CNN can be executed directly on a Microcontroller (MCU)-based sensor node.With results on a newly collected open dataset, we show that our best CNN achieves 86.3% balanced accuracy, significantly outperforming the 61% achieved by a state-of-the-art deterministic algorithm. Changing the architectural parameters of the CNN, we obtain a rich Pareto set of models, spanning 70.5-86.3% accuracy and 0.18-75k parameters. Deployed on a STM32L476RGMCU, these models have a latency of 0.73-5.33ms, with an energy consumption per inference of 9.38-68.57\muJ. © 2022 IEEE.

Cardiopulmonary exercise testing excludes significant disease in patients recovering from COVID-19.

OBJECTIVE: Post-COVID-19 syndrome presents a health and economic challenge affecting ~10% of patients recovering from COVID-19. Accurate assessment of patients with post-COVID-19 syndrome is complicated by health anxiety and coincident symptomatic autonomic dysfunction. We sought to determine whether either symptoms or objective cardiopulmonary exercise testing could predict clinically significant findings. METHODS: 113 consecutive military patients were assessed in a comprehensive clinical pathway. This included symptom reporting, history, examination, spirometry, echocardiography and cardiopulmonary exercise testing (CPET) in all, with chest CT, dual-energy CT pulmonary angiography and cardiac MRI where indicated. Symptoms, CPET findings and presence/absence of significant pathology were reviewed. Data were analysed to identify diagnostic strategies that may be used to exclude significant disease. RESULTS: 7/113 (6%) patients had clinically significant disease adjudicated by cardiothoracic multidisciplinary team (MDT). These patients had reduced fitness (V̇O2 26.7 (±5.1) vs 34.6 (±7.0) mL/kg/min; p=0.002) and functional capacity (peak power 200 (±36) vs 247 (±55) W; p=0.026) compared with those without significant disease. Simple CPET criteria (oxygen uptake (V̇O2) >100% predicted and minute ventilation (VE)/carbon dioxide elimination (V̇CO2) slope <30.0 or VE/V̇CO2 slope <35.0 in isolation) excluded significant disease with sensitivity and specificity of 86% and 83%, respectively (area under the receiver operating characteristic curve (AUC) 0.89). The addition of capillary blood gases to estimate alveolar-arterial gradient improved diagnostic performance to 100% sensitivity and 78% specificity (AUC 0.92). Symptoms and spirometry did not discriminate significant disease. CONCLUSIONS: In a population recovering from SARS-CoV-2, there is reassuringly little organ pathology. CPET and functional capacity testing, but not reported symptoms, permit the exclusion of clinically significant disease.

Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale

Single-cell datasets continue to grow in size, posing computational challenges for dealing with expanded scale, extended modality and inevitable batch effects. Deep learning-based approaches have recently emerged to address these points by deriving nonlinear cell embeddings. Here we present contrastive learning of cell representations, Concerto, which leverages a self-supervised distillation framework to model multimodal single-cell atlases. Simply by discriminating each cell from the others, Concerto can be adapted to various downstream tasks such as automatic cell type classification, data integration and especially reference mapping. Unlike current mainstream packages, Concerto’s contrastive setting well supports operating on all genes to preserve biological variations. Concerto can flexibly generalize to multiomics to obtain unified cell representations. Benchmarking on both simulated and real datasets, Concerto substantially outperforms competing methods. By mapping to a comprehensive reference, Concerto recapitulates differential immune responses and discovers disease-specific cell states in patients with COVID-19. Concerto is easily parallelizable and efficiently scalable to build a 10-million-cell reference within 1.5 h and query 10,000 cells within 8 s. Overall, Concerto will facilitate biomedical research by enabling iteratively constructing single-cell reference atlases and rapidly mapping novel dataset against them to transfer relevant cell annotations. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.

Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference

Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adaptive inference solution consisting of the cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN), which is only invoked for difficult-to-classify frames. Deploying such adaptive system on a IoT Microcontroller, we show that, when processing the output of a 8 × 8 low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach, with an accuracy drop of less than 2% (83% balanced accuracy). © 2022 IEEE.

Utility of ultrasound-guided supraclavicular lymph node sampling in lung cancer diagnosis – remodelling the pathway in the era of COVID-19

Introduction: Ultrasound guided sampling (USGS) of supraclavicular lymph nodes (SCLN) is a minimally invasive method for obtaining cytological diagnosis in metastatic lung cancer. Same day USGS service may improve timeliness of investigations, minimise hospital visits and reduce invasive procedures. Methods: We performed a 3-year retrospective analysis of patients with SCLN amenable to biopsy detect-ed on 2 week-wait (2WW) CT. We identified those who underwent USGS or other procedures, diagnostic yield and their timeliness were determined. Results: 49 patients (26%) had amenable SCLN, of whom 37 (75.5%) had USGS. USGS alone sufficient for 27 (73%) patients. Diagnostic yield is better for larger nodes (<1cm 62.5% positive;≥1cm 86.2% positive, 95% CI 0.13-0.93, p=0.011). The overall diagnostic yield of USGS SCLN was 81% (30/37, 95% CI 65% to 92%). Al-though faster to obtain USGS, no statistically significant difference was reached between USGS and other methods (USGS median 15.5 days (IQR 11.2), other procedures median 17.5 days (IQR 26.5), Mann-Whitney U p=0.42). Conclusion: USGS SCLN has potential utility in early lung cancer diagnosis, even in lymph nodes <1cm, and is an underutilized diagnostic investigation. A prospective study of same day 2WW outpatient clinic and USGS procedure is now required to assess its effect on an accelerated diagnostic pathway. © 2022, Zita Medical Managent. All rights reserved.

LOCATION AND CAPACITY PLANNING FOR PREVENTIVE HEALTHCARE FACILITIES WITH CONGESTION EFFECTS

A painful lesson got from pandemic COVID-19 is that preventive healthcare service is of utmost importance to governments since it can make massive savings on healthcare expenditure and promote the welfare of the society. Recognizing the importance of preventive healthcare, this research aims to present a methodology for designing a network of preventive healthcare facilities in order to prevent diseases early. The problem is formulated as a bilevel non-linear integer programming model. The upper level is a facility location and capacity planning problem under a limited budget, while the lower level is a user choice problem that determines the allocation of clients to facilities. A genetic algorithm (GA) is developed to solve the upper level problem and a method of successive averages (MSA) is adopted to solve the lower level problem. The model and algorithm is applied to analyze an illustrative case in the Sioux Falls transport network and a number of interesting results and managerial insights are provided. It shows that solutions to medium-scale instances can be obtained in a reasonable time and the marginal benefit of investment is decreasing.

China's new growth story: linking the 14th Five-Year Plan with the 2060 carbon neutrality pledge

China has announced its commitment to achieving carbon neutrality by 2060, and for this challenging goal to be reached within just four decades, there is a real urgency of shaping the low-carbon agenda in its 14th Five-Year Plan and to ratchet up ambition on climate policy in the near term to peak emissions early. This paper argues that China will have to change the way of development by take a sustainable pathway to growth. And this new approach does not mean sacrificing economic growth;quite the opposite, it can boost growth by providing great opportunities in terms of jobs, efficiency, demand, and many other aspects, while reducing carbon emissions and enabling great benefits with regards to pollution, ecological restoration, biodiversity and well-beings. The COVID-19 pandemic has provided a window of opportunity for China and other countries to cooperate to link the post-pandemic economic recovery with the fight against climate change.

Engaging Students in Distance Learning of Science With Remote Labs 2.0

During the COVID-19 pandemic, many students lost opportunities to explore science in labs due to school closures. Remote labs provide a possible solution to mitigate this loss. However, most remote labs to date are based on a somehow centralized model in which experts design and conduct certain types of experiments in well-equipped facilities, with a few options of manipulation provided to remote users. In this paper, we propose a distributed framework, dubbed remote labs 2.0, that offers the flexibility needed to build an open platform to support educators to create, operate, and share their own remote labs. Similar to the transformation of the Web from 1.0 to 2.0, remote labs 2.0 can greatly enrich experimental science on the Internet by allowing users to choose and contribute their subjects and topics. As a reference implementation, we developed a platform branded as Telelab. In collaboration with a high school chemistry teacher, we conducted remote chemical reaction experiments on the Telelab platform with two online classes. Pre/post-test results showed that these high school students attained significant gains (t(26)=8.76, p<0.00001) in evidence-based reasoning abilities. Student surveys revealed three key affordances of Telelab: live experiments, scientific instruments, and social interactions. All 31 respondents were engaged by one or more of these affordances. Students behaviors were characterized by analyzing their interaction data logged by the platform. These findings suggest that appropriate applications of remote labs 2.0 in distance education can, to some extent, reproduce critical effects of their local counterparts on promoting science learning. IEEE