The identification of spreading influence nodes in social networks, which studies how to detect important individuals in human society, has attracted increasing attention from physical and computer science, social science and economics communities. The identification algorithms of spreading influence nodes can be used to evaluate the spreading influence, describe the node’s position, and identify interaction centralities. This review summarizes the recent progress about the identification algorithms of spreading influence nodes from the viewpoint of social networks, emphasizing the contributions from physical perspectives and approaches, including the microstructure-based algorithms, community structure-based algorithms, macrostructure-based algorithms, and machine learning-based algorithms. We introduce diffusion models and performance evaluation metrics, and outline future challenges of the identification of spreading influence nodes.
Wearing masks is an easy way to operate and popular measure for preventing epidemics. Although masks can slow down the spread of viruses, their efficacy in gathering environments involving heterogeneous person-to-person contacts remains unknown. Therefore, we aim to investigate the epidemic prevention effect of masks in different real-life gathering environments. This study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering environments. The transmission of COVID-19 is simulated using the Monte Carlo simulation method. The heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general society. Furthermore, the heterogeneity of gathering environments causes the epidemic prevention effect of masks to differ. Wearing masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools, high schools, and hospitals. However, the use of masks alone in primary schools and hospitals cannot control outbreaks. In high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse, masks can meet the need for prevention. Given the heterogeneity of individual behavior, if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing, the epidemic prevention effect of masks can be improved. Finally, asymptomatic infection has varying effects on the prevention effect of masks in different environments. The effect can be weakened or eliminated by increasing the usage rate of masks in high schools and workplaces. However, the effect on primary schools and hospitals cannot be weakened. This study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.
The sustainable development of Internet hospitals and e-health platforms relies on the participation of patients and physicians, especially on the provision of health counseling services by physicians. The objective of our study is to explore the factors motivating Chinese physicians to provide online health counseling services from the perspectives of their online and offline reputation. We collect the data of 141029 physicians from 6173 offline hospitals located in 350 cities in China. Based on the reputation theory and previous studies, we incorporate patients’ feedback as physicians’ online reputation and incorporate physicians’ offline professional status as physicians’ offline reputation. Results show that physicians’ online reputation significantly and positively influence their online counseling behaviors, whereas physicians’ offline reputation significantly and negatively influence their online counseling behaviors. We conclude that physician’s online and offline reputations show a competitive and substitute relationship rather than a complementary relationship in influencing physicians to provide online counseling services in Internet hospitals. One possible explanation for the substitute relationship could be the constraints of limited time and effort of physicians.
Post-acute care (PAC) residents in nursing homes (NHs) are recently hospitalized patients with medically complex diagnoses, ranging from severe orthopedic injuries to cardiovascular diseases. A major role of NHs is to maximize restoration of PAC residents during their NH stays with desirable discharge outcomes, such as higher community discharge likelihood and lower re/hospitalization risk. Accurate prediction of the PAC residents’ length-of-stay (LOS) with multiple discharge dispositions (e.g., community discharge and re/hospitalization) will allow NH management groups to stratify NH residents based on their individualized risk in realizing personalized and resident-centered NH care delivery. Due to the highly heterogeneous health conditions of PAC residents and their multiple types of correlated discharge dispositions, developing an accurate prediction model becomes challenging. Existing predictive analytics methods, such as distribution-/regression-based methods and machine learning methods, either fail to incorporate varied individual characteristics comprehensively or ignore multiple discharge dispositions. In this work, a data-driven predictive analytics approach is considered to jointly predict the individualized re/hospitalization risk and community discharge likelihood over time in the presence of varied residents’ characteristics. A sampling algorithm is further developed to generate accurate predictive samples for a heterogeneous population of PAC residents in an NH and facilitate facility-level performance evaluation. A real case study using large-scale NH data is provided to demonstrate the superior prediction performance of the proposed work at individual and facility levels through comprehensive comparison with a large number of existing prediction methods as benchmarks. The developed analytics tools will allow NH management groups to identify the most at-risk residents by providing them with more proactive and focused care to improve resident outcomes.
Conveying consumers’ specific emotions in new products, referred to as emotional product development or emotional design, is strategically crucial for manufacturers. Given that sentiment analysis (SA) can extract and analyze people’s opinions, sentiments, attitudes, and perceptions regarding different products/services, SA-based emotional design may provide manufacturers with real-time, direct, and rapid decision support. Despite its considerable advancements and numerous survey and review articles, SA is seldom considered in emotional design. This study is among the first efforts to conduct a thorough review of SA from the view of emotional design. The comprehensive review of aspect-level SA reveals the following: 1) All studies focus on extracting product features by mixing technical product features and consumers’ emotional perceptions. Consequently, such studies cannot capture the relationships between technical and emotional attributes and thus cannot convey specific emotions to the new products. 2) Most studies use the English language in SA, but other languages have recently received more interest in SA. Furthermore, after conceptualizing emotion as Kansei and introducing emotional product development and Kansei Engineering, a review of the data-driven emotional design is then conducted. A few efforts start to study emotional design with the help of SA. However, these studies only focus on either analyzing consumers’ preferences on product features or extracting emotional opinions from online reviews, thus cannot realize data-driven emotional product development. Finally, some research opportunities are provided. This study opens a broad door to aspect-level SA and its integration with emotional product development.
Entity and relation extraction is an indispensable part of domain knowledge graph construction, which can serve relevant knowledge needs in a specific domain, such as providing support for product research, sales, risk control, and domain hotspot analysis. The existing entity and relation extraction methods that depend on pretrained models have shown promising performance on open datasets. However, the performance of these methods degrades when they face domain-specific datasets. Entity extraction models treat characters as basic semantic units while ignoring known character dependency in specific domains. Relation extraction is based on the hypothesis that the relations hidden in sentences are unified, thereby neglecting that relations may be diverse in different entity tuples. To address the problems above, this paper first introduced prior knowledge composed of domain dictionaries to enhance characters’ dependence. Second, domain rules were built to eliminate noise in entity relations and promote potential entity relation extraction. Finally, experiments were designed to verify the effectiveness of our proposed methods. Experimental results on two domains, including laser industry and unmanned ship, showed the superiority of our methods. The F1 value on laser industry entity, unmanned ship entity, laser industry relation, and unmanned ship relation datasets is improved by +1%, +6%, +2%, and +1%, respectively. In addition, the extraction accuracy of entity relation triplet reaches 83% and 76% on laser industry entity pair and unmanned ship entity pair datasets, respectively.
Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.
Recently, the problem of mobile applications (Apps) leaking users’ private information has aroused wide concern. As the number of Apps continuously increases, effective large-scale App governance is a major challenge. Currently, the government mainly filters out Apps with potential privacy problems manually. Such approach is inefficient with limited searching scope. In this regard, we propose a quantitative method to filter out problematic Apps on a large scale. We introduce Privacy Level (P-Level) to measure an App’s probability of leaking privacy. P-Level is calculated on the basis of Permission-based Privacy Value (P-Privacy) and Usage-based Privacy Value (U-Privacy). The former considers App permission setting, whereas the latter considers App usage. We first illustrate the privacy value model and computation results of both values based on real-world dataset. Subsequently, we introduce the P-Level computing model. We also define the P-Level computed on our dataset as the PL standard. We analyze the distribution of average usage and number of Apps under the levels given in the PL standard, which may provoke insights into the large-scale App governance. Through P-Privacy, U-Privacy, and P-Level, potentially problematic Apps can be filtered out efficiently, thereby making up for the shortcoming of being manual.
Precisely understanding the business relationships between autonomous systems (ASes) is essential for studying the Internet structure. To date, many inference algorithms, which mainly focus on peer-to-peer (P2P) and provider-to-customer (P2C) binary classification, have been proposed to classify the AS relationships and have achieved excellent results. However, business-based sibling relationships and structure-based exchange relationships have become an increasingly nonnegligible part of the Internet market in recent years. Existing algorithms are often difficult to infer due to the high similarity of these relationships to P2P or P2C relationships. In this study, we focus on multiclassification of AS relationship for the first time. We first summarize the differences between AS relationships under the structural and attribute features, and the reasons why multiclass relationships are difficult to be inferred. We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, to solve this multiclassification problem under complex scenes. The proposed framework considers the global network structure and local link features concurrently. Experiments on real Internet topological data validate the effectiveness of our method, that is, AS-GCN. The proposed method achieves comparable results on the binary classification task and outperforms a series of baselines on the more difficult multiclassification task, with an overall metrics above 95%.
The digital twins (DT) has quickly become a hot topic since it was proposed. It appears in all kinds of commercial propaganda and is widely quoted by academic circles. However, the term DT has misstatements and is misused in business and academics. This study revisits DT and defines it to be a more advanced system/product/service modeling and simulation environment that combines most modern information communication technologies (ICTs) and engineering mechanism digitization and characterized by system/product/service life cycle management, physically geometric visualization, real-time sensing and measurement of system operating conditions, predictability of system performance/safety/lifespan, and complete engineering mechanisms-based simulations. The idea of DT originates from modeling and simulation practices of engineering informatization, including virtual manufacturing (VM), model predictive control, and building information modeling (BIM). On the basis of the two-element VM model, we propose a three-element model to represent DT. DT does not have its unique technical characteristics. The existing practices of DT are extensions of the engineering informatization embracing modern ICTs. These insights clarify the origin of DT and its technical essentials.
Wind and solar powers will gradually become dominant energies toward carbon neutrality. Large-scale renewable energies, with strong stochasticity, high volatility, and unadjustable features, have great impacts on the safe operation of power system. Thus, an advanced hydropower energy system serving multiple energies is required to respond to volatility, with expanding role from a “stable energy supplier” to a “flexible efficiency regulator”. Future research and application can be considered from three aspects: 1) system expansion (e.g., the construction of large-scale hydropower/renewable energy bases in China, the construction of transnational hydropower energy internet, and the functional transformation of traditional hydropower reservoirs and generating units); 2) efficiency promotion (e.g., advanced intelligent forecasting, multi-objective operation, and risk management methods); and 3) supporting measures (e.g., market reform, benefit compensation and policy mechanism, technical standards, and laws and regulations).
