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Research on healthcare accessibility is developing with a focus on multimodal transport modes (MTMs) and multiple time-series variations. A dynamic traffic environment leads travelers to make distinct decisions at different time slots, which impacts spatiotemporal accessibility markedly. Our article proposes a methodological framework to measure spatiotemporal accessibility with multimodal transportation modes and its variation at multiple time series, while accounting for traffic congestion and the probability of residential transport mode choices in a dynamic traffic environment. We selected Nanjing, China, as the study area and pediatric clinic services (PCS) as specific healthcare services to estimate the spatiotemporal accessibility at four representative time slots. The results indicate that the weight estimation of travel time based on MTMs is more propitious than the travel time of single-mode to present real-world travel characteristics and reflects the spatiotemporal distribution and variation of services synthetically. Furthermore, the spatiotemporal accessibility variation of PCS in peripheral suburbs is more pronounced than that in urban centers and rural areas. This work holds pragmatic implications for policymakers in terms of services planning and allocation optimization to improve the equity of resource supply.

In the post‐pandemic era, tourism has emerged as the most popular form of recreation in densely populated areas, driven by the pursuit of improved quality of life and the younger generation's growing enthusiasm for connecting with nature or history. Accurate measurement of the accessibility of tourist attractions has become crucial due to this trend. However, existing methods for measuring accessibility, such as the widely used two‐step floating catchment area (2SFCA) method, show limitations in multi‐modal transport networks as it overlooks multi‐modal travels and assumes uniform access for each transport mode within the catchment area. In this article, we present a novel multi‐modal network‐based two‐step floating catchment area (MMN‐2SFCA) method, which incorporates a super‐network to explicitly model the multi‐modal travels. The proposed method emphasizes the modeling of transfer behavior, resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The proposed method emphasizes the modeling of transfer behavior, travel mode choice, and travel demand estimation by multi‐source big data (including mobile phone signaling data, travel survey data, subway smart card data, and bike‐sharing data), resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The results of the case study in Suzhou (a famous tourism city in China) demonstrate the effectiveness of the proposed MMN‐2SFCA method. The method can rectify the imbalance in accessibility distribution caused by considering only single‐modal trips, and avoid overestimation of accessibility by accounting for transfer behavior. This study contributes to advancing accessibility measurement for multi‐modal transport networks. Moreover, the MMN‐2SFCA method offers excellent extensibility, enabling authorities to optimize and coordinate multi‐modal transport networks for improving the accessibility of tourist attractions and other facilities. [ABSTRACT FROM AUTHOR]

Hand, Foot, and Mouth Disease (HFMD) is an infectious disease that primarily affects young children. In densely populated Jiangsu Province in China, the impact of extreme meteorological factors on HFMD is a concern. We aimed to examine the association between extreme meteorological variables and HFMD infection risk using daily HFMD infections and meteorological data from 2010 to 2017 in Jiangsu Province. We used distributed lag non‐linear model (DLNM) to analyze the data, which can effectively capture the nuanced non‐linear dynamics and lag effects in the relationship between HFMD and extreme meteorological factors. Comparing the 10th and 90th percentiles of meteorological variables with their respective median values, our results showed that extremely low temperatures and high humidity were significantly associated with increased HFMD infection risk. The greatest effect of extremely low temperatures was observed at a lag of 1–2 days, elevating the risk by 18 ∼ 33% (RR = 1.18 ∼ 1.33). Extremely high humidity was found to increase the risk of infection, starting at a lag of 4 days. In contrast, extremely high temperatures, low humidity, and high wind speed were associated with reduced risk of infection at lag of 0–12 days, with the range of RR values being 0.60–0.98 for extremely high temperatures, 0.69–0.89 for extremely low humidity, and 0.84–0.98 for extremely high wind speed respectively. Our findings suggest that extreme meteorological factors can significantly impact the incidence of HFMD in Jiangsu Province, and highlight the need for effective public health protection measures during the periods of extreme meteorological condition, particularly for vulnerable populations. Plain Language Summary: Meteorological factors including temperature, humidity, rainfall and wind speed, have been recognized in previous studies as significant contributors to the spread, prevalence and severity of Hand, foot, and mouth disease (HFMD) outbreaks. In Jiangsu Province, the likelihood of experiencing such outbreaks due is high due to its dense population and substantial population movements, and it is crucial to comprehend the environmental factors driving HFMD in this region. To address this need, we collected daily HFMD infection and meteorological data from 2010 to 2017 from 13 cities in Jiangsu Province, and utilized a Distributed Lag Non‐linear Model (DLNM) to evaluate the influence of extreme meteorological factors on HFMD infections. Our findings revealed that extremely low temperatures and high humidity increased the risk of HFMD infection, while extremely high temperatures, low humidity, and high wind speed decreased the risk of HFMD infection. The results will contribute to enhancing public health preparedness and response strategies, thereby reducing the societal burden of HFMD in Jiangsu Province and safeguarding the overall health and well‐being of the population. Key Points: Investigated the relationship between extreme meteorological variables and the risk of Hand, Foot, and Mouth Disease infectionQuantified the effect of extreme meteorological variables using the distributed lag non‐linear modelRevealed the effects of extreme meteorological variables on Hand, Foot, and Mouth Disease infections across 13 cities in Jiangsu Province [ABSTRACT FROM AUTHOR]

Empirical orthogonal function (EOF) analysis is widely adopted for identifying spatial patterns in regular latitude‐longitude gridded geographical data. However, a standard EOF analysis could underestimate variances from low latitudes due to the increasing grid cell areas when moving toward the equator. A broadly adopted compensating approach is the area‐weighted EOF, where the grid data are multiplied by a factor proportional to the grid area, thereby forcing each datum to represent identical areas. In this article, we revisit the area‐weighting scheme and discuss its potential drawbacks. In particular, we show that along with compensating the unequal areas, another unaddressed issue is which region of the data is more relevant to our focused problem. We propose a regional attentive EOF (RA‐EOF) method, which resolves area‐compensation and region selection simultaneously. We conduct case studies of the North Atlantic Oscillation (NAO) analysis and perform RA‐EOF to the sea level pressure variability in the North Atlantic sector. Experiments show that our method evidently detects the NAO pattern in the leading mode of sea level pressure variability and suggests the southwestward movement of the southern action centre of NAO during the summers. Our findings clarify the ambiguity about the summer NAO in previous studies. The proposed methodology provides a potent instrument for discerning the spatial distribution and analysing the temporal variability of atmospheric teleconnections and oscillations, particularly in cases characterized by weak signal strength. [ABSTRACT FROM AUTHOR]

Natural gas is considered a transitional energy source in the transition to clean energy owing to its clean, efficient, and ecologically beneficial properties. The trade of liquefied natural gas (LNG) serves as the backbone of the global natural gas trade and significantly influences the dynamics of the global energy trade system. This paper constructs long-term global LNG trade networks, and explores the spatiotemporal evolution and topological structures of the international LNG trade by utilizing multilevel network analysis methods, to provide insights for comprehensively understanding the market dynamics of the global LNG trade system. The findings indicate the following: (1) The global LNG trade volume shows an overall upward trend, and the global LNG trade network exhibits uneven spatial distribution, clear hierarchical differentiation, and an increasingly complicated structure. Global LNG trade is gradually changing from regionalization to globalization, and the international LNG market is undergoing structural reshaping. (2) The global LNG trade network continues to expand in size and density, and the rapidly growing LNG supply and trade relations are driving the formation of the global natural gas market. (3) Global LNG trade is still in a phase of rapid change, with the global efficiency of the network increasing and then decreasing. The trade network has traditionally been centered on ten countries, including Japan, South Korea, the United States, and Qatar. (4) The global LNG trade network exhibits clear core-periphery structures with considerable polarization effects, and the trade network structure is continuously evolving and is growing unbalanced. Finally, we put forward relevant policy suggestions to promote global LNG trade interconnectivity and enhance environmental protection and respond to global climate change. [ABSTRACT FROM AUTHOR]

Existing intervisibility analysis methods suffer from computational inefficiency due to redundant sampling points. To address this issue, we propose a new approximate method called line-of-sight (LoS) zoning, which leverages continuous terrain relief to identify potentially obscuring zones (POZ) of LoS. By limiting the sampling range to a much smaller POZ, the number of sampling points is significantly reduced. The optimal sampling interval of 6 is determined by striking a balance between computational efficiency and accuracy. Through experiments in both mountainous and plain areas, regardless of the height range and resolution conditions, we demonstrate the high efficiency of the LoS zoning method, especially in scenarios with a high proportion of visible LoS. To account for potential visibility errors caused by sharp peaks in the terrain, we conducted experiments under fixed time intervals to assess the calculation quality of different methods. The results show that in mountainous and plain areas, the improvement in detection rate compared to the hopping strategy method is around 4–6 times in most scenarios. This significant performance enhancement highlights the superiority of the LoS zoning method, and shows great promise in terrain avoidance, path planning in the military, and detection of dangerous targets. [ABSTRACT FROM AUTHOR]

Over the last decades, open innovation has received considerable attention. The purpose of this study is to examine the influence mechanism of enterprise social networks on open innovation performance from the dual perspective of inter- and intra-organisation. Structural equation model was used to test on a sample of 311 enterprises from Mainland China. Results show that external social network exerts a positive effect on open innovation performance through the mediation of external knowledge management capability. Internal social network positively affects open innovation performance via the mediating role of internal knowledge management capability. The interaction of external and internal social network enhances open innovation performance through the dual mediating effects of external and internal knowledge management capability. Furthermore, inter-organisational contract control plays a moderating role in the relationship between external knowledge management capability and open innovation performance. Intra-organisational professional control exhibits a moderating effect on the relationship between internal knowledge management capability and open innovation performance. [ABSTRACT FROM AUTHOR]

As a kind of first aid healthcare service, emergency medical services (EMSs) present high spatiotemporal sensitivity due to significant changes in the time-dependent urban environment. Taking full advantage of big spatiotemporal data to realize multiperiod relocation optimization of EMSs can reduce idle resources and improve service utilization efficiency and fairness. First, we established the dynamic time-dependent accessibility and equality model to formulate the multiperiod maximization objective of global equality. Second, we proposed a capacitated integer evolution algorithm that relocates emergency medical vehicles to optimize the scheduling scheme. Based on multiperiod mobile phone records and multiperiod online route planner data, the equality of EMSs in the research metropolis, Nanjing, China, rose by 41.5% on average, which has an incentivizing effect on alleviating the tension of prehospital service and minimizes accessibility disparities without constructing more infrastructure. We also created maps to visualize the changes in equality patterns over time. This relocation optimization approach can be regarded as a trade-off approach to dispatch time-dependent sensitive services and provide a practical tool for healthcare decision-makers to evaluate public healthcare systems and improve strategic urban service planning. [ABSTRACT FROM AUTHOR]

The accurate simulation of climate is always critically important and also a challenge. This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian networks based on the concept of a coarse–fine model. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing a Bayesian network constructed on the interrelationships between internal climate variables of the GREB model to achieve local optimization. To objectively validate the performance and generalization of the improved method, the method is applied to the simulation of surface temperature and temperature of the atmosphere based on the 3.75 ∘ × 3.75 ∘ global data sets by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) from 1985 to 2014. The results demonstrate that the improved model exhibits higher average accuracy and lower spatial differentiation than the original GREB model and is robust in long-term simulations. This approach addresses issues with the accuracy of the GREB model in local areas, which can be attributed to an overreliance on boundary and initial conditions, as well as a lack of fully usable observed data. Additionally, the model overcomes the challenge of poor robustness in statistical models due to ambiguous climate inclusions. Thus, the improved method provides a promising way to give a reliable and stable simulation of climate. [ABSTRACT FROM AUTHOR]

Assessing the trade network connectivity is essential for understanding the trade network structure, optimizing trade development patterns, and improving uneven trade development along the "Belt and Road" (BRI). From the perspective of connectivity, this paper integrates the frontier algorithms in network science and constructs an analytical framework to identify the mesoscale structures, including the community structure, core-periphery structure, and backbone structure embedded in the network, and further explore the structural connectivity of the BRI trade network. The results show that: (1) The BRI trade network represents a trade pattern of "one superpower, many great powers", with three major trade groups in Southeast Asia, the Middle East, and Northern Central and Eastern Europe in terms of geographical space. China is the super core of the BRI trade network, and the most considerable trade links are all centred in China. (2) Five distinctive trade blocs have formed in the BRI trade network. Nevertheless, the structure of the trade blocs shows significant geographical proximity, indicating that geographical distance still plays a vital role in the international trade system at the regional scale. (3) The BRI trade network demonstrates a significant core-periphery structure, with apparent trade clustering among the core countries within the trade network. Among them, nine countries led by China constitute the core structure, and the peripheral structure is large, reaching forty-four. (4) The trade links with China constitute the backbone structure of the whole trade network in the BRI region. In addition, the trade links related to energy trade and re-export trade are also crucial components of the BRI backbone structure. Methodologically, the analytical framework proposed for assessing the network structural connectivity has great potential to be widely applied to other disciplines and fields. [ABSTRACT FROM AUTHOR]

At small granularity (e.g., 10-minutes to hourly), expressway traffic volumes rely heavily on drivers’ driving habits heterogeneity and decision randomness, making it challenging for accurate modeling. In this paper, we propose a small granularity simulation model named Small-Granularity Expressway Traffic Volumes with Quantum Walks (SGETV-QW). The proposed model adopts quantum walks to generate probability patterns of the exiting time of drivers from the expressway. Then, we refine and map the generated probability patterns to empirical traffic-volume data via a stepwise regression and quantify the modeling accuracy in both the time and frequency domain. We validate SGETV-QW for traffic volume data from seven stations along the Nanjing-Changzhou Expressway in China and compare it with Autoregressive Integrated Moving Average Model (ARIMA) and Long and Short-Term Memory (LSTM) networks. The results show that SGETV-QW improves the simulation accuracy at small granularity. In addition, traffic volumes simulated by SGETV-QW have almost the same frequency spectrum as observed traffic volumes. Finally, we conduct a sensibility analysis and show that SGETV-QW can adapt its parameters to model traffic volumes at different granularities. [ABSTRACT FROM AUTHOR]

The multi‐scale characteristic of online car‐hailing traffic volume can reflect the implied distribution pattern, which is crucial for traffic management and even urban planning. Nevertheless, the spatio‐temporal heterogeneity of online car‐hailing traffic volume makes it challenging to analyze its multi‐scale characteristics effectively. Here, a method named multi‐scale characteristic analysis for online car‐hailing traffic volume with quantum walk (MCATV‐QW) is proposed. MCATV‐QW adopts quantum walks to generate multi‐scale probability patterns that online car‐hailing appears at different locations over time. Then stepwise regression is applied to screen the generated multi‐scale probability patterns, to further analyze the multi‐scale characteristic. We validate MCATV‐QW with online car‐hailing traffic volume in the northeast of Chengdu, China. MCATV‐QW not only achieves better simulation performance, but also reveals the distribution pattern that the influence degree of multi‐scale probability patterns weakens from southwest to northeast of study area. MCATV‐QW also reflects the traffic spatial pattern that is dominated by gradual traffic (48%), with both abrupt (26%) and uniform traffic (26%). [ABSTRACT FROM AUTHOR]