Vigilance behaviour during the calving season in female Tibetan antelopes (Pantholopshodgsonii).

Tibetan antelopes (Pantholopshodgsonii) migrate great distances to specific delivery and calving areas. In the current study, we investigated calving site selection and vigilance behaviour during delivery and nursing in migratory female Tibetan antelopes at Zonag Lake. According to observations and analysis, the females were distributed south of Zonag Lake, where vegetation was abundant. We determined their dates of migration (crossing the Qinghai-Tibet Highway observation site), showing a shift of one month during the period from June in 2008 to May 2021. Results also showed that 81.4% of females expressed high vigilance behaviour during calving and nursing compared to those without calves (7.1%). From delivery until calf standing, females were highly vigilant and spent considerable time scanning, with 96% of females showing vigilance behaviour. Females with calves (average 9.94 ± 0.62 s) spent more time on vigilance behaviour than females without calves (average 6.25 ± 1.38 s). Females with newborns spent the greatest amount of time being vigilant (average 51.63 ± 4.24 s). These results not only identify basic Tibetan antelope calving behaviour, but also provide scientific analysis and evidence for further ethological research on female Tibetan antelopes.

Resilience patterns of human mobility in response to extreme urban floods.

Large-scale disasters can disproportionately impact different population groups, causing prominent disparity and inequality, especially for the vulnerable and marginalized. Here, we investigate the resilience of human mobility under the disturbance of the unprecedented '720' Zhengzhou flood in China in 2021 using records of 1.32 billion mobile phone signaling generated by 4.35 million people. We find that although pluvial floods can trigger mobility reductions, the overall structural dynamics of mobility networks remain relatively stable. We also find that the low levels of mobility resilience in female, adolescent and older adult groups are mainly due to their insufficient capabilities to maintain business-as-usual travel frequency during the flood. Most importantly, we reveal three types of counter-intuitive, yet widely existing, resilience patterns of human mobility (namely, 'reverse bathtub', 'ever-increasing' and 'ever-decreasing' patterns), and demonstrate a universal mechanism of disaster-avoidance response by further corroborating that those abnormal resilience patterns are not associated with people's gender or age. In view of the common association between travel behaviors and travelers' socio-demographic characteristics, our findings provide a caveat for scholars when disclosing disparities in human travel behaviors during flood-induced emergencies.

Urban flood risk differentiation under land use scenario simulation.

The frequent urban floods have seriously affected the regional sustainable development in recent years. It is significant to understand the characteristics of urban flood risk and reasonably predict urban flood risk under different land use scenarios. This study used the random forest and multi-criteria decision analysis models to assess the spatiotemporal characteristics of flood risk in Zhengzhou City, China, from 2005 to 2020, and proposed a robust method coupling Bayesian network and patch-generating land use simulation models to predict future flood risk probability. We found that the flood risk in Zhengzhou City presented an upward trend from 2005 to 2020, and its spatial pattern was "high in the middle and low in the surrounding areas". In addition, land use patterns under the sustainable development scenario would be more conducive to reducing flood risk. Our results can provide theoretical support for scientifically optimizing land use to improve urban flood risk management.

Spatial pattern evolution and prediction scenario of habitat quality in typical fragile ecological region, China: A case study of the Yellow River floodplain area.

The Yellow River basin is an important area for China to implement ecological protection policies. Studying the habitat quality of the Yellow River floodplain area is of great significance to the ecological security and sustainable development of the entire basin. This study primarily investigated the spatial pattern of habitat quality in the Yellow River floodplain area from 2000 to 2020, then, we also simulated changes of habitat quality in 2025-2035 and analyzed the influencing factors by coupling the PLUS (Patch-generating Land Use Simulation) model, InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model and RF (Random Forest) model. The results showed that:(1) From 2000 to 2020, cultivated land and build-up land constituted an important part of the Yellow River floodplain area, and the growth rate of build-up land was fast. (2) We also found that the ecological land (forest land, grassland, waterbody) had a higher contribution value to the habitat quality, while the build-up land had a lower contribution value to the habitat quality. (3) Overall, the habitat quality of the floodplain area showed a degradation trend from 2000 to 2020. In addition, the regions with low habitat quality accounted for the major proportion. (4) Based on the calculation results of the Random Forest (RF) model, we found that topographical relief (TR) and land use intensity (LUI) were the two most important factors affecting habitat quality of the floodplain area. (5) According to the four scenarios from 2025 to 2035, it is found that the habitat quality level would be the highest under the ecological protection scenario, while under the urban development scenario its level would be the lowest. This study attempts to combine the RF model with PLUS model to improve the objectivity and accuracy of the future prediction scenario of habitat quality, which can provide scientific reference for ecological governance and policy formulation in the Yellow River floodplain area.

Modeling Production-Living-Ecological Space for Chengdu, China: An Analytical Framework Based on Machine Learning with Automatic Parameterization of Environmental Elements.

Cities worldwide are facing the dual pressures of growing population and land expansion, leading to the intensification of conflicts in urban productive-living-ecological spaces (PLES). Therefore, the question of "how to dynamically judge the different thresholds of different indicators of PLES" plays an indispensable role in the studies of the multi-scenario simulation of land space changes and needs to be tackled in an appropriate way, given that the process simulation of key elements that affect the evolution of urban systems is yet to achieve complete coupling with PLES utilization configuration schemes. In this paper, we developed a scenario simulation framework combining the dynamic coupling model of Bagging-Cellular Automata (Bagging-CA) to generate various environmental element configuration patterns for urban PLES development. The key merit of our analytical approach is that the weights of different key driving factors under different scenarios are obtained through the automatic parameterized adjustment process, and we enrich the study cases for the vast southwest region in China, which is beneficial for balanced development between eastern and western regions in the country. Finally, we simulate the PLES with the data of finer land use classification, combining a machine learning and multi-objective scenario. Automatic parameterization of environmental elements can help planners and stakeholders understand more comprehensively the complex land space changes caused by the uncertainty of space resources and environment changes, so as to formulate appropriate policies and effectively guide the implementation of land space planning. The multi-scenario simulation method developed in this study has offered new insights and high applicability to other regions for modeling PLES.

Changes in tourist mobility after COVID-19 outbreaks.

We comparatively examined tourist mobility changes in the entire country and explicitly covered two distinct waves of COVID-19 outbreaks, based on mobile phone data from 277.15 million tourists from 2019 to 2021 in China. The results show that domestic tourism in Beijing was even higher after the pandemic than prior to it. In addition, we found that female and elderly groups had a slower recovery after the first wave, whereas this was the opposite one year later, after the second wave. Additionally, wealthier, larger cities were notably hit the hardest. Overall, our findings provide a better understanding of tourism management in public health crises and policy-making during post-pandemic recovery and for future outbreaks.

A topology-based evaluation of resilience on urban road networks against epidemic spread: Implications for COVID-19 responses.

Road closure is an effective measure to reduce mobility and prevent the spread of an epidemic in severe public health crises. For instance, during the peak waves of the global COVID-19 pandemic, many countries implemented road closure policies, such as the traffic-calming strategy in the UK. However, it is still not clear how such road closures, if used as a response to different modes of epidemic spreading, affect the resilient performance of large-scale road networks in terms of their efficiency and overall accessibility. In this paper, we propose a simulation-based approach to theoretically investigate two types of spreading mechanisms and evaluate the effectiveness of both static and dynamic response scenarios, including the sporadic epidemic spreading based on network topologies and trajectory-based spreading caused by superspreaders in megacities. The results showed that (1) the road network demonstrates comparatively worse resilient behavior under the trajectory-based spreading mode; (2) the road density and centrality order, as well as the network's regional geographical characteristics, can substantially alter the level of impacts and introduce heterogeneity into the recovery processes; and (3) the resilience lost under static recovery and dynamic recovery scenarios is 8.6 and 6.9%, respectively, which demonstrates the necessity of a dynamic response and the importance of making a systematic and strategic recovery plan. Policy and managerial implications are also discussed. This paper provides new insights for better managing the resilience of urban road networks against public health crises in the post-COVID era.

Emotional wellbeing in intercity travel: Factors affecting passengers' long-distance travel moods.

The travel mood perception can significantly affect passengers' mental health and their overall emotional wellbeing when taking transport services, especially in long-distance intercity travels. To explore the key factors influencing intercity travel moods, a field survey was conducted in Xi'an to collect passengers' individual habits, travel characteristics, moods, and weather conditions. Travel mood was defined using the 5-Likert scale, based on degrees of happiness, panic, anxiety, and tiredness. A support vector machine (SVM) and ordered logit model were used in tandem for determinant identification and exploring their respective influences on travel moods. The results showed that gender, age, occupation, personal monthly income, car ownership, external temperature, precipitation, relative humidity, air quality index, visibility, travel purposes, intercity travel mode, and intercity travel time were all salient influential variables. Specifically, intercity travel mode ranked the first in affecting panic and anxiety (38 and 39% importance, respectively); whereas occupation was the most important factor affecting happiness (23% importance). Moreover, temperature appeared as the most important influencing factor of tiredness (22% importance). These findings help better understand the emotional health of passengers in long-distance travel in China.

Modeling the resumption of work and production of enterprises during COVID-19: An SIR-based quantitative framework.

The ongoing COVID-19 pandemic has evolved beyond being a public health crisis as it has exerted worldwide severe economic impacts, triggering cascading failures in the global industrial network. Although certain powerful enterprises can remain its normal operation during this global shock, what's more likely to happen for the majority, especially those small- and medium-sized firms, is that they are experiencing temporary suspension out of epidemic control requirement, or even permanent closure due to chronic business losses. For those enterprises that sustain the pandemic and only suspend for a relatively short period, they could resume work and production when epidemic control and prevention conditions are satisfied and production and operation are adjusted correspondingly. In this paper, we develop a novel quantitative framework which is based on the classic susceptible-infectious-recovered (SIR) epidemiological model (i.e., the SIR model), containing a set of differential equations to capture such enterprises' reactions in response to COVID-19 over time. We fit our model from the resumption of work and production (RWP) data on industrial enterprises above the designated size (IEDS). By modeling the dynamics of enterprises' reactions, it is feasible to investigate the ratio of enterprises' state of operation at given time. Since enterprises are major economic entities and take responsibility for most output, this study could potentially help policy makers better understand the economic impact caused by the pandemic and could be heuristic for future prevention and resilience-building strategies against suchlike outbreaks of public health crises.

A resilience-oriented approach for quantitatively assessing recurrent spatial-temporal congestion on urban roads.

Traffic congestion brings not only delay and inconvenience, but other associated national concerns, such as greenhouse gases, air pollutants, road safety issues and risks. Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart community. A considerable amount of works has made contributions to tackle the problem. Several methods, such as time-based approaches and level of service, can be effective for characterizing congestion on urban streets. However, studies with systemic perspectives have been minor in congestion quantification. Resilience, on the other hand, is an emerging concept that focuses on comprehensive systemic performance and characterizes the ability of a system to cope with disturbance and to recover its functionality. In this paper, we symbolized recurrent congestion as internal disturbance and proposed a modified metric inspired by the well-applied "R4" resilience-triangle framework. We constructed the metric with generic dimensions from both resilience engineering and transport science to quantify recurrent congestion based on spatial-temporal traffic patterns and made the comparison with other two approaches in freeway and signal-controlled arterial cases. Results showed that the metric can effectively capture congestion patterns in the study area and provides a quantitative benchmark for comparison. Also, it suggested not only a good comparative performance in measuring strength of proposed metric, but also its capability of considering the discharging process in congestion. The sensitivity tests showed that proposed metric possesses robustness against parameter perturbation in Robustness Range (RR), but the number of identified congestion patterns can be influenced by the existence of ϵ. In addition, the Elasticity Threshold (ET) and the spatial dimension of cell-based platform differ the congestion results significantly on both the detected number and intensity. By tackling this conventional problem with emerging concept, our metric provides a systemic alternative approach and enriches the toolbox for congestion assessment. Future work will be conducted on a larger scale with multiplex scenarios in various traffic conditions.

Characterisation of survivability resilience with dynamic stock interdependence in financial networks.

This paper examines the dynamic evolutionary process in the London Stock Exchange and uses network statistical measures to model the resilience of stock. A large historical dataset of companies was collected over 40 years (1977-2017) and conceptualised into weighted, temporally evolving and signed networks using correlation-based interdependences. Our results revealed a "fission-fusion" market growth in network topologies, which indicated the dynamic and complex characteristics of its evolutionary process. In addition, our regression and modelling results offer insights for construction a "characterisation tool" which can be used to predict stocks that have delisted and continuing performance relatively well, but were less adequate for stocks with normal performance. Moreover, the analysis of deviance suggested that the survivability resilience could be described and approximated by degree-related centrality measures. This study introduces a novel alternative for looking at the bankruptcy in the stock market and is potentially helpful for shareholders, decision- and policy-makers.