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The rapid growth of electric load fuels urbanization but creates unsettling environmental challenges. In that regard, the energy system is a crucial part of consuming water and emitting greenhouse gases. To support reliable and economical energy system operations, with reduced water waste and carbon emissions, we design a viable co-optimization of water-energy-carbon nexus for an integrated energy system by adequately modelling essential interdependencies of power, gas, and water systems. A two-stage distributionally robust optimization method is proposed: the first-stage model minimizes the day-ahead reserve capacity scheduling for the next day, and the second-stage model enables real-time dispatch by considering the variability in renewable power generation. The carbon emissions by power generation and distribution are incorporated in both stages to ensure low-carbon operations. We use a two-stage moment-based distributionally robust approach to capture and represent the uncertainties in renewable generation. Instead of assuming a deterministic distribution, we characterize an ambiguity set with mean vectors and covariance matrices, which produces a family set of distributions. We conduct case studies using a modified IEEE 33-bus power system that includes and links a 20-node gas system and a 10-node water system. The comparative results indicate that the proposed co-optimization of water-energy-carbon nexus is superior than several benchmarks. By considering the close interdependencies of water, energy, and carbon, we create an optimal resource allocation scheme, which is capable of minimizing operation costs, lessening carbon emissions, and reducing water waste. The current research enables effective operation schemes for urban energy systems, illustrates a viable way to curb local environmental emissions, and increases water usage efficiency in the water-energy-carbon nexus.

Water resources are vital to the energy conversion process but few efforts have been devoted to the joint optimization problem which is fundamentally critical to the water-energy nexus for small-scale or remote energy systems (e.g., energy hubs). Traditional water and energy trading mechanisms depend on centralized authorities and cannot preserve security and privacy effectively. Also, their transaction process cannot be verified and is subject to easy tampering and frequent exposures to cyberattacks, forgery, and network failures. Toward that end, water-energy hubs (WEHs) offers a promising way to analyse water-energy nexus for greater resource utilization efficiency. We propose a two-stage blockchain-based transactive management method for multiple, interconnected WEHs. Our method considers peer-to-peer (P2P) trading and demand response, and leverages blockchain to create a secure trading environment. It features auditing and resource transaction record management via system aggregators enabled by a consortium blockchain, and entails spatial-temporal distributionally robust optimization (DRO) for renewable generation and load uncertainties. A spatial-temporal ambiguity set is incorporated in DRO to characterize the spatial-temporal dependencies of the uncertainties in distributed renewable generation and load demand. We conduct a simulation-based evaluation that includes robust optimization and the moment-based DRO as benchmarks. The results reveal that our method is consistently more effective than both benchmarks. Key findings include i) our method reduces conservativeness with lower WEH trading and operation costs, and achieves important performance improvements by up to 6.1%; and ii) our method is efficient and requires 18.7% less computational time than the moment-based DRO. Overall, this study contributes to the extant literature by proposing a novel two-stage blockchain-based WEH transaction method, developing a realistic spatial-temporal ambiguity set to effectively hedge against the uncertainties for distributed renewable generation and load demand, and producing empirical evidence suggesting its greater effectiveness and values than several prevalent methods.

This review studies the cascading impacts of extreme weather events on the Water-Energy-Carbon (WEC) Nexus, with a focus on their combined and sequential effects. It synthesizes research on how droughts, floods, heatwaves, hurricanes, and wildfires each initiate a chain reaction within the interconnected domains of water, energy, and carbon. Key insights include the analysis of drought impacts, like in California, where hydroelectric power's share dropped from 18% to 7%, leading to a 34%increase in emissions from natural gas plants. In Europe, flooding led to operational challenges for power plants, with a projected loss of 0.6–4.6 TWh in energy generation by 2030 due to water temperature rises. The 2023 European heatwave saw Spain's energy demand spike by 20%, driven by increased use of air conditioning, and a corresponding 15–20% rise in carbon emissions in affected countries due to greater reliance on fossil fuels. The review emphasizes the need for integrated resilience strategies, leveraging the provided quantitative data to argue for policies that address these interdependent challenges. It urges for a nuanced understanding of the WEC Nexus's dynamics to inform more effective responses to the rising tide of climate change-induced extreme weather events. Furthermore, this review expands its examination to include cases from developing countries, showcasing how their unique challenges and responses within the WEC Nexus contribute to a more comprehensive understanding of global resilience strategies against extreme weather. This review brings to the forefront the ripple effects of alterations in energy production on water resources and carbon dynamics, underscoring the critical need for a nuanced understanding and integrated approaches in managing the WEC Nexus in the face of extreme weather events.

Information spread on social media has been extensively studied through both model-driven theoretical research and data-driven case studies. Recent empirical studies have analyzed the differences and complexity of information dissemination, but theoretical explanations of its characteristics from a modeling perspective are underresearched. To capture the complex patterns of the information dissemination mechanism, we propose a resistant linear threshold (RLT) dissemination model based on psychological theories and empirical findings. In this article, we validate the RLT model on three types of networks and then quantify and compare the dissemination characteristics of the simulation results with those from the empirical results. In addition, we examine the factors affecting dissemination. Finally, we perform two case studies of the 2019 novel Corona Virus Disease (COVID-19)-related information dissemination. The dissemination characteristics derived by the simulations are consistent with the empirical research. These results demonstrate that the RLT model is able to capture the patterns of information dissemination on social media and thus provide model-driven insights into the interpretation of public opinion, rumor control, and marketing strategies on social media.

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ABSTRACTBackgroundChildren aged under 5 years are particularly vulnerable to influenza infection. In this study, we aim to estimate the number and incidence of influenza among young children and estimate the impact of childhood vaccination in different scenarios from 2013/14 to 2016/17 seasons.MethodsThe number and incidence rate of influenza infections among children aged under 5 years in Beijing was estimated by scaling up observed surveillance data. Then, we used a susceptible–exposed–infected–recovery (SEIR) model to reproduce the weekly number of influenza infections estimated in Beijing during the study seasons, and to estimate the number and proportion of influenza-attributed medically attended acute respiratory infections (I-MAARI) averted by vaccination in each season. Finally, we evaluated the impact of alternative childhood vaccination programs with different coverage and speed of vaccine distribution.ResultsThe estimated average annual incidence of influenza in children aged under 5 years was 33.9% (95% confidence interval (CI): 27.5%, 47.2%) during the study period. With the actual coverage during the included seasons at around 2.9%, an average of 3.9% (95%CI: 3.5%, 4.4%) I-MAARI was reduced compared to a no-vaccination scenario. Reaching 20%, 40%, 50%, 60%, 80% and 100% vaccine coverage would lead to an overall I-MAARI reduction of 25.3%, 42.7%, 51.9%, 57.0%, 65.3% and 71.2%. At 20% coverage scenario, an average of 28.8% I-MAARI will be prevented if intensive vaccination implemented in 2 months since the vaccine released.ConclusionIn Beijing, the introduction of a program for vaccinating young children, even at relatively low vaccine coverage rates, would considerably reduce I-MAARI, particularly if the vaccines can be quickly delivered.

•GIS with big data provides geospatial information to fight COVID-19.•Big data showed power on epidemic transmission analysis and prevention decision making support.•Challenges still continue in data aggregation, knowledge discovery, and dynamic expression.

To investigate the drug release and anti-adhesion effects of a TET (tetrandrine)-loaded PDLLA (poly-dl-lactide) film. Detection of TET release in vitro was carried out by high-performance liquid chromatography (HPLC) every 2 days following immersion of the tetrandrine-loaded PDLLA film in simulated body fluid until the TET content of the eluate could not be detected. For the in vivo test, TET-loaded PDLLA films were implanted into animal laminectomy models and positive and blank control groups were also set up. Postoperative serum tests, and macroscopic and histological analyses at 1, 4, 8, and 12 weeks, were used to assess the effects of the film. Statistical analyses were performed by one-way ANOVA. The drug release of the tetrandrine-loaded PDLLA film in vitro showed two phases with a second release peak. Ultimately, the duration of continuous delivery was up to 66 days and the cumulative delivery rate was up to 93.18%. Scores for the proliferation of epidural scars or adhesion of the dura mater in the test group were much lower than those for the two control groups. Histological analysis revealed the test group had fewer inflammatory cells and fibroblasts, as well as fewer extracellular collagen fibers, and a lower histology score than those of the two control groups at all time points. Tetrandrine-loaded PDLLA film is a novel controlled drug release and anti-adhesion material in vitro and in vivo.

Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township. Two smoothing methods, Bayesian adjustment and spatial smoothing, were applied to identify the spatial risks and spatial transmission trends. Furthermore, we explored various spatial patterns and spatio-temporal evolution of Beijing 2003 SARS epidemic using spatial statistics such as Moran’s I and LISA. Part of this study is targeted at evaluating the effectiveness of public health control measures implemented during the SARS epidemic. The main findings are as follows. (1) The diffusion speed of SARS in the northwest-southeast direction is weaker than that in northeast-southwest direction. (2) SARS’s spread risk is positively spatially associated and the strength of this spatial association has experienced changes from weak to strong and then back to weak during the lifetime of the Beijing SARS epidemic. (3) Two spatial clusters of disease cases are identified: one in the city center and the other in the eastern suburban area. These two clusters followed different evolutionary paths but interacted with each other as well. (4) Although the government missed the opportunity to contain the early outbreak of SARS in March 2003, the response strategies implemented after the mid of April were effective. These response measures not only controlled the growth of the disease cases, but also mitigated the spatial diffusion.

2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyunand then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others.This article gives an overview of commuting patterns in China in 2020, offering the most comprehensive understanding on national mobility at fine resolution across various scenarios in China.

•An adaptively temporal graph convolution model for infectious disease prediction.•Address the multivariate time series prediction in a graph-based approach.•The contact patterns of multiple age groups can improve prediction accuracy.