The ever-increasing entwinement of information and communication technology (ICT) infrastructure with the proliferation of electric vehicles (EVs) has resulted in a congruent coalescence of energy and transportation networks. However, the surfeit of data communication and processing capabilities inherent in these systems also poses a potential peril to cyber security. Hence, a bifurcated logistics operation and cyberattack defense strategy have been propounded for green integrated power-transportation networks (IPTN) with renewable penetration. This strategy leverages the potential of social participation from EVs to amplify the defense operation. The bifurcation comprises of a preclusive stage aimed at fortifying and preserving resource allocation within IPTN and a defensive stage aimed at mitigating the deleterious impacts of cyberattacks through rapid response measures. Conventional measures such as load shedding and operation adjustments are augmented by an innovative defense involvement incentive, designed to elicit additional support from EV users. A mean-risk distributionally robust optimization methodology predicated on Kullback–Leibler divergence is posited to address the limitations in data availability in simulating cyberattack consequences. Empirical investigations through case studies in an urbane IPTN are conducted to evaluate the adverse impacts of cyberattacks and examine countermeasures aimed at mitigating their effects to the greatest extent possible.

As an important market entity, energy storage (ES) can participate in multiple electricity markets simultaneously and benefit from them. However, the current operation models for ES primarily concentrate on calculating market revenue, which fails to assess the risk and correlation of market prices accurately. Thus, this article designs an operation method for ES considering price risk and coupling in markets. First, a multiplexing scheme is designed for ES operation in multimarket, considering market price risk and market coupling. Second, generalized autoregressive conditional heteroskedasticity and exponentially weighted moving average models are introduced to precisely assess price risk, and a dynamic Copula method is designed to reflect the coupling coefficient. Furthermore, it introduces the concept of expected premium calculated through real options theory, allowing for more refined profit maximization strategies under positive fluctuating market conditions. Demonstrated with a real PJM multimarket-based framework, the proposed method leads to an 18.2% increase compared to the traditional portfolio method, which provides a new business model for ES to unlock its potential value.

With the development of internet, transportation network, and other technologies, the transmission of information and disease presents complex and diverse new modes, which are mainly manifested as the coupling transmission of information and disease in the cyber–physical–social space. Inspired by this phenomenon, this article proposes a multilayer network-based information–behavior–disease coupling (IBDN) transmission model for the process of information diffusion–behavior change–disease transmission. The IBDN model considers various factors such as psychological drivers of information dissemination, the impact of herd mentality on behavioral transmission, the disease transmission dynamics of the current COVID-19 Omicron mutant strain and relevant countermeasures, and the interconnections between information, behavior, and disease transmission. Furthermore, within the framework of the COVID-19 Omicron mutant strain pandemic, the proposed IBDN model was leveraged to assess the effects of the propagation parameters of each layer and the interlayer coupling parameters on the magnitude of the COVID-19 outbreak and the strain on medical resources. A sensitivity analysis was carried out to determine the variability of the basic reproductive number of the Omicron mutant strains across various nations. Finally, the findings of the experiment were subjected to a thorough examination of policy implications to furnish valuable perspectives for the formulation of effective epidemic prevention strategies in the face of severe COVID-19 situation.

Decentralized trading schemes involving energy prosumers have prevailed in recent years. Such schemes provide a pathway for increased energy efficiency and can be enhanced by the use of blockchain technology to address security concerns in decentralized trading. To improve transaction security and privacy protection while ensuring desirable social governance, this article proposes a novel two-stage blockchain-based operation and trading mechanism to enhance energy hubs connected with integrated energy systems (IESs). This mechanism includes multienergy aggregators (MAGs) that use a consortium blockchain and its enabled proof-of-work (PoW) to transfer and audit transaction records, with social governance principles for guiding prosumers’ decision-making in the peer-to-peer (P2P) transaction management process. The uncertain nature of renewable generation and load demand are adequately modeled in the two-stage Wasserstein-based distributionally robust optimization (DRO). The practicality of the proposed mechanism is illustrated by several case studies that jointly show its ability to handle an increased renewable generation capacity, achieve a 16.7% saving in the audit cost, and facilitate 2.4% more P2P interactions. Overall, the proposed two-stage blockchain-based trading mechanism provides a practical trading scheme and can reduce redundant trading amounts by 6.5%, leading to a further reduction of the overall operation cost. Compared to the state-of-the-art benchmark methods, our mechanism exhibits significant operation cost reduction and ensures social governance and transaction security for IES and energy hubs.

This article explores social manufacturing (SM) within cyber–physical–social systems (CPSSs), leveraging artificial intelligence (AI) to revolutionize energy prosumer networks. We introduce a blockchain-enabled operation and management mechanism for energy systems, incorporating energy aggregators for efficient transaction audits and employing consortium blockchain and proof-of-work for enhanced security. Guided by social governance principles and utilizing the soft actor–critic (SAC) approach for handling renewable generation and load demand uncertainties, our method offers a resilient and cost-effective solution. Simulated case studies reveal a 16.7% reduction in audit costs and a 2.4% increase in peer-to-peer transactions, highlighting improved network synergy. Our approach also reduces redundant trading by 6.5%and cuts operational costs by up to 6%, demonstrating the effectiveness of blockchain in improving cost-efficiency and enhancing social governance and security in energy manufacturing systems. The findings of this study contribute a novel vista to the ongoing discourse in SM, illustrating the formidable potential of advanced information and AI technologies in amplifying the operational acumen of contemporary manufacturing ecosystems.

The COVID-19 pandemic has had a global impact on communities, economies, and healthcare systems. To control the virus's spread, numerous epidemiological investigations have been made available online, leading to a growing demand for automated tools to extract valuable information from case reports and reduce the burden on news reporters. In response to this growing need, we have meticulously curated a comprehensive data set of COVID-19 epidemiological investigation corpora, specifically designed for named entity recognition (NER) applications. This data set enables researchers and analysts to efficiently identify and extract key information from the case reports, streamlining the process of understanding and communicating the findings. To further enhance the effectiveness of NER in the context of epidemiological investigations, we evaluated and compared the performance of three cutting-edge, pre-trained model-based methods: BERT-BiLSTM-CRF, ERNIE-BiLSTM- CRF and ALBERT-BiLSTM-CRF. All techniques demonstrated impressive performance in recognizing named entities within the case reports, showcasing their potential to revolutionize the way in which epidemiological data is analyzed and disseminated. By leveraging these advanced NER techniques, we aim to facilitate more accurate and timely reporting, ultimately contributing to better-informed decision-making processes and improved public health outcomes.

The rapid spread of the pandemic of coronavirus disease of 2019 (COVID-19) has created an unprecedented, global health disaster. During the outburst period, the paucity of knowledge and research aggravated devastating panic and fears that lead to social stigma and created serious obstacles to contain the disastrous epidemic. We propose a deep learning-based method to detect stigmatized contents on online social network (OSN) platforms in the early stage of COVID-19. Our method performs a semantic-based quantitative analysis to unveil essential spatial-temporal characteristics of COVID-19 stigmatization for timely alerts and risk mitigation. Empirical evaluations are carried out to examine our method’s predictive utilities. The visualization results of the co-occurrence network using Gephi indicate two distinct groups of stigmatized words that pertain to people in Wuhan and their dietary behaviors, respectively. Netizens’ participations and stigmatizations in the Hubei region, where the COVID-19 broke out, are twice ($p < 0.05$) and four ($p < 0.01$) times more frequent and intense than those in other parts of China, respectively. Also, the number of COVID-19 patients is correlated with COVID-19-related stigma significantly (correlation coefficient = 0.838,$p < 0.01$). The responses to individual users’ posts have the power law distribution, while posts by official media appear to attract more responses (e.g., likes, replies, and forward). Our method can help platforms and government agencies manage public health disasters through effective identification and detailed analyses of social stigma on social media.

Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model’s effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water–energy–social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This article develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a U.K. microgrid with actual energy generation and consumption data. This article can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.

Grid-connected electric vehicles (GEVs) and energy-transportation nexus bring a bright prospect to improve the penetration of renewable energy and the economy of microgrids (MGs). However, it is challenging to determine optimal vehicle-to-grid (V2G) strategies due to the complex battery aging mechanism and volatile MG states. This article develops a novel online battery anti-aging energy management method for energy-transportation nexus by using a novel deep reinforcement learning (DRL) framework. Based on battery aging characteristic analysis and rain-flow cycle counting technology, the quantification of aging cost in V2G strategies is realized by modeling the impact of number of cycles, depth of discharge, and charge and discharge rate. The established life loss model is used to evaluate battery anti-aging effectiveness of agent actions. The coordination of GEVs charging is modeled as multiobjective learning by using a DRL algorithm. The training objective is to maximize renewable penetration while reducing MG power fluctuations and vehicle battery aging costs. The developed energy-transportation nexus energy management method is verified to be effective in optimal power balancing and battery anti-aging control on a MG in the U.K. This article provides an efficient and economical tool for MG power balancing by optimally coordinating GEVs charging and renewable energy, thus helping promote a low-cost decarbonization transition.

Resilience problems from cyber-attacks on information communication technologies exist under their wide usage. False data injection (FDI) judiciously designed by attackers may cause severe consequences such as uneconomic operation and blackouts, particularly multivector energy distribution systems (MEDS), which are closely linked and interdependent. This article addresses the cyber resilient issues of an MEDS caused by FDI, considering the uncertainty from renewable resources. A novel two-stage distributionally robust optimization (DRO) is proposed to realize the day-ahead and real-time resilience improvement. The ambiguity set is based on both the Wasserstein distance and moment information. Compared to robust optimization which considers the worst case, DRO yields less-conservative solutions and thus provides more economic operation schemes. The Wasserstein metric-based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Case studies are demonstrated on two representative MEDS networked with energy hubs, illustrating the effectiveness of the proposed cybersecured model. The produced adaptive robust economic operation for MEDS can reduce load shedding and enhance system resilience against severe cyberattacks.

The 2019 Novel Coronavirus Disease (COVID-19) vaccines have been placed significant expectation to end the COVID-19 pandemic sooner. However, issues related to vaccines still need to be resolved urgently, including the vaccination number and range. In this paper, we proposed an epidemic spread model based on the hierarchical weighted network. This model fully considers the heterogeneity of the community social contact network and the epidemiological characteristics of COVID-19 in China, which enables to evaluate the potential impact of vaccine efficacy, vaccination schemes, and mixed interventions on the epidemic. The results show that a mass vaccination can effectively control the epidemic but cannot completely eliminate it. In the case of limited resources, giving vaccination priority to the individuals with high contact intensity in the community is necessary. Joint implementation with non-pharmacological interventions strengthening the control of virus transmission. The results provide insights for decision-makers with effective vaccination plans and prevention and control programs.

COVID-19 pandemic continues to rampage in the world. Before the achievement of global herd immunity, non-pharmacological interventions(NPIs) are crucial to mitigate the pandemic. Although various NPIs have been put into practice, there are many concerns about the impacts and effectiveness of these NPIs. COVID-19 modelling study (CMS) in epidemiology can provide evidence to solve the aforementioned concerns. It is time-consuming to collect evidence manually when dealing with the vast amount of CMS papers. Accordingly, we seek to accelerate evidence collection by developing an information extraction model to automatically identify evidence from CMS papers. This work presents a novel COVID-19 Non-pharmacological Interventions Evidence (CNPIE) Corpus, which contains 597 abstracts of COVID-19 modelling study with richly annotated entities and relations of the impacts of NPIs. We design a semi-supervised document-level information extraction model (SS-DYGIE++) which can jointly extract entities and relations. Our model outperforms previous baselines in both entity recognition and relation extraction tasks by a large margin. The proposed work can be applied towards automatic evidence extraction in the public health domain for assisting the public health decision-making of the government.

Seismic events can severely damage both electricity and natural gas systems, causing devastating consequences. Ensuring the secure and reliable operation of the integrated energy system (IES) is of high importance to avoid potential damage to the infrastructure and reduce economic losses. This article proposes a new optimal two-stage optimization to enhance the reliability of IES planning and operation against seismic attacks. In the first stage, hardening investment on the IES is conducted, featuring preventive measures for seismic attacks. The second stage minimizes the expected operation cost of emergency response. The random seismic attack is modeled as uncertainty, which is realized after the first stage. An explicit damage assessment model is developed to define the budget set of the uncertain seismic activity. Based on the survivability of transmission lines and gas pipelines of IES, an optimal system investment plan is developed. The problem is formulated as a two-stage distributionally robust optimization (DRO) model, which is tested on an integrated IEEE 30-bus system and 20-node gas network. Case studies demonstrate that the two-stage DRO outperforms robust optimization and a single-stage optimization model in terms of minimizing the investment cost and expected economic loss. This article can help system operators to make economical hardening and operation strategies to improve the reliability of IES under seismic attacks, thus managing a more robust and secure energy system.

Energy hub system (EHS) incorporating multiple energy carriers, storage, and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation. This article proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This article proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where 1) the first stage optimizes EHS operation cost, and 2) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modeled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this article justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multienergy carriers.