The transportation sector significantly contributes to carbon emissions, prompting a surge in research on emission reduction. Carbon estimation methods include bottom-up, top-down, and life cycle assessment (LCA). Unlike the first two, LCA offers a holistic view of emissions throughout the entire transportation system, facilitating precise reduction strategies. This study examines LCA research on transport carbon emissions, revealing scope, impact factors, and policy assessments in 243 selected articles. The primary focus is on road transport and vehicles, with fuel well-to-wheel and vehicle LCAs comprising 32.9% of analyses. While emerging technologies like electric vehicles show direct emission advantages, a life cycle perspective is crucial for accurate assessment. Policy evaluations centers on banning fossil fuel vehicle and promoting zero-emission vehicles. Coordinated policies are essential, as isolated ones may not achieve emission reduction goals, and comprehensive policies must balance stakeholder interests. Future carbon reduction LCA should considering technological innovations, mode optimization, and regional disparities.

Public health surveillance is defined as the ongoing, systematic collection, analysis, and interpretation of health data and is closely integrated with the timely dissemination of information that the public needs to know and upon which the public should act. Public health surveillance is central to modern public health practice by contributing data and information usually through a national notifiable disease reporting system (NNDRS). Although early identification and prediction of future disease trends may be technically feasible, more work is needed to improve accuracy so that policy makers can use these predictions to guide prevention and control efforts. In this article, we review the advantages and limitations of the current NNDRS in most countries, discuss some lessons learned about prevention and control from the first wave of COVID-19, and describe some technological innovations in public health surveillance, including geographic information systems (GIS), spatial modeling, artificial intelligence, information technology, data science, and the digital twin method. We conclude that the technology-driven innovative public health surveillance systems are expected to further improve the timeliness, completeness, and accuracy of case reporting during outbreaks and also enhance feedback and transparency, whereby all stakeholders should receive actionable information on control and be able to limit disease risk earlier than ever before.

Logic deals with the systematic study of the form of valid inference. Formal logic was developed in ancient civilized countries such as India, China, and Greece.

Background Partial- or full-lockdowns, among other interventions during the COVID-19 pandemic, may disproportionally affect people (their behaviors and health outcomes) with lower socioeconomic status (SES). This study examines income-related health inequalities and their main contributors in China during the pandemic. Methods The 2020 China COVID-19 Survey is an anonymous 74-item survey administered via social media in China. A national sample of 10,545 adults in all 31 provinces, municipalities, and autonomous regions in mainland China provided comprehensive data on sociodemographic characteristics, awareness and attitudes towards COVID-19, lifestyle factors, and health outcomes during the lockdown. Of them, 8448 subjects provided data for this analysis. Concentration Index (CI) and Corrected CI (CCI) were used to measure income-related inequalities in mental health and self-reported health (SRH), respectively. Wagstaff-type decomposition analysis was used to identify contributors to health inequalities. Results Most participants reported their health status as “very good” (39.0%) or “excellent” (42.3%). CCI of SRH and mental health were − 0.09 ( p < 0.01 ) and 0.04 ( p < 0.01 ), respectively, indicating pro-poor inequality in ill SRH and pro-rich inequality in ill mental health. Income was the leading contributor to inequalities in SRH and mental health, accounting for 62.7% ( p < 0.01 ) and 39.0% ( p < 0.05 ) of income-related inequalities, respectively. The COVID-19 related variables, including self-reported family-member COVID-19 infection, job loss, experiences of food and medication shortage, engagement in physical activity, and five different-level pandemic regions of residence, explained substantial inequalities in ill SRH and ill mental health, accounting for 29.7% ( p < 0.01 ) and 20.6% ( p < 0.01 ), respectively. Self-reported family member COVID-19 infection, experiencing food and medication shortage, and engagement in physical activity explain 9.4% ( p < 0.01 ), 2.6% (the summed contributions of experiencing food shortage (0.9%) and medication shortage (1.7%), p < 0.01 ), and 17.6% ( p < 0.01 ) inequality in SRH, respectively (8.9% ( p < 0.01 ), 24.1% ( p < 0.01 ), and 15.1% ( p < 0.01 ) for mental health). Conclusions Per capita household income last year, experiences of food and medication shortage, self-reported family member COVID-19 infection, and physical activity are important contributors to health inequalities, especially mental health in China during the COVID-19 pandemic. Intervention programs should be implemented to support vulnerable groups.

BACKGROUND:Lockdown policies were widely adopted during the coronavirus disease 2019 (COVID-19) pandemic to control the spread of the virus before vaccines became available. These policies had significant economic impacts and caused social disruptions. Early re-opening is preferable, but it introduces the risk of a resurgence of the epidemic. Although the World Health Organization has outlined criteria for re-opening, decisions on re-opening are mainly based on epidemiologic criteria. To date, the effectiveness of re-opening policies remains unclear.METHODS:A system dynamics COVID-19 model, SEIHR(Q), was constructed by integrating infection prevention and control measures implemented in Wuhan into the classic SEIR epidemiological model and was validated with real-world data. The input data were obtained from official websites and the published literature.RESULTS:The simulation results showed that track-and-trace measures had significant effects on the level of risk associated with re-opening. In the case of Wuhan, where comprehensive contact tracing was implemented, there would have been almost no risk associated with re-opening. With partial contact tracing, re-opening would have led to a minor second wave of the epidemic. However, if only limited contact tracing had been implemented, a more severe second outbreak of the epidemic would have occurred, overwhelming the available medical resources. If the ability to implement a track-trace-quarantine policy is fixed, the epidemiological criteria need to be further taken into account. The model simulation revealed different levels of risk associated with re-opening under different levels of track-and-trace ability and various epidemiological criteria. A matrix was developed to evaluate the effectiveness of the re-opening policies.CONCLUSIONS:The SEIHR(Q) model designed in this study can quantify the impact of various re-opening policies on the spread of COVID-19. Integrating epidemiologic criteria, the contact tracing policy, and medical resources, the model simulation predicts whether the re-opening policy is likely to lead to a further outbreak of the epidemic and provides evidence-based support for decisions regarding safe re-opening during an ongoing epidemic.KEYORDS:COVID-19; Risk of re-opening; Effectiveness of re-opening policies; IPC measures; SD modelling.

>The past two decades have witnessed the burgeoning of enormous digital technologies and data collected via countless channels. They are combined in numerous ways in different fields,including epidemiology, m Health and modeling of health systems,with the intention to improve human health(e.g., clinical decision support, electronic medical record management) [1–6]. However,this is a new interdisciplinary area where no single scientific discipline knows how to take full advantage of these data and technolo-

This chapter provides a brief introduction on the Error Theory which includes the background, research objects and contents, research methods and objectives, structure and theoretical framework, concepts, methodologies for eliminating errors, avoidance of error, future problems in error-elimination studies, and the future of Error Theory.

This chapter introduces the application of error theory in real project. A case of waste water treatment plant is presented in this section to illustrate how the steps of identifying and eliminating errors are exercised in reality.

Social platforms such as Weibo, Facebook and Twitter have become a part of daily life, where people can exchange information. In this process, people's behaviors often influence each other. Social influence prediction has become one of the hot issues at present. In this paper, NNMLInf social influence prediction model is constructed based on neural network multi-label classification. People's network structure features are taken as the network input, and their behaviors are divided into multiple labels as the network output. Node2vec is adopted to extract network representative features of users. This model combines the network structure with human behaviors, and the prediction results can be more practical. The experiment carried on BlogCatalog, Flickr and Youtube shows that NNMLInf model performs better than traditional approaches such as DT (decision tree), SVM (support vector machine), and better expresses social influence .

For the purpose of investigating transformations of error such as $$T(\mu ) = \mu _1$$ , it is necessary to examine the mechanisms and laws for achieving those transformations. There are three types of operations as follows: (1) solve for $$\mu _1$$ given T and $$\mu $$ ; (2) solve for $$\mu $$ given T and $$\mu _1$$ ; (3) solve for T given $$\mu $$ and $$\mu _1$$ , where T is transformation, $$\mu $$ and $$\mu _1$$ are objects being studied. This chapter intends to explore the theoretical foundations, essential concept, object and content of error logic. Finally, this chapter provides the theory system and application examples for error logic.

Target detection is a hot topic in the research of sea clutter. The solution of this problem can be divided into two aspects. Firstly, find out the different characteristics between the target and sea clutter. Secondly, take advantage of the classifier to realize the feature classification. Thus, we study the characteristics of sea clutter. As a result, the decorrelation time, the K distribution fitting parameters and the Hurst exponent in the FRFT domain are proved to be three feature vectors that can better distinguish the target from sea clutter. Finally, we bring the Extreme Learning Machine (ELM) in the feature classification. Experiment results demonstrate that the chosen feature vectors are effective. Moreover, the ELM is also effective by comparison with SVM.

Objectives: To study the country-level dynamics and influences between population weight status and socio-economic distribution (employment status and family income) in the US and to project the potential impacts of socio-economic-based intervention options on obesity prevalence. Study design: Ecological study and simulation. Methods: Using the longitudinal data from the 2001-2011 Medical Expenditure Panel Survey (N = 88,453 adults), we built and calibrated a system dynamics model (SDM) capturing the feedback loops between body weight status and socio-economic status distribution and simulated the effects of employment-and income-based intervention options. Results: The SDM-based simulation projected rising overweight/obesity prevalence in the US in the future. Improving people's income from lower to middle-income group would help control the rising prevalence, while only creating jobs for the unemployed did not show such effect. Conclusions: Improving people from low- to middle-income levels may be effective, instead of solely improving reemployment rate, in curbing the rising obesity trend in the US adult population. This study indicates the value of the SDM as a virtual laboratory to evaluate complex distributive phenomena of the interplay between population health and economy. (c) 2018 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Background In practical research, it was found that most people made health-related decisions not based on numerical data but on perceptions. Examples include the perceptions and their corresponding linguistic values of health risks such as, smoking, syringe sharing, eating energy-dense food, drinking sugar-sweetened beverages etc. For the sake of understanding the mechanisms that affect the implementations of health-related interventions, we employ fuzzy variables to quantify linguistic variable in healthcare modeling where we employ an integrated system dynamics and agent-based model. Methodology In a nonlinear causal-driven simulation environment driven by feedback loops, we mathematically demonstrate how interventions at an aggregate level affect the dynamics of linguistic variables that are captured by fuzzy agents and how interactions among fuzzy agents, at the same time, affect the formation of different clusters(groups) that are targeted by specific interventions. Results In this paper, we provide an innovative framework to capture multi-stage fuzzy uncertainties manifested among interacting heterogeneous agents (individuals) and intervention decisions that affect homogeneous agents (groups of individuals) in a hybrid model that combines an agent-based simulation model (ABM) and a system dynamics models (SDM). Having built the platform to incorporate high-dimension data in a hybrid ABM/SDM model, this paper demonstrates how one can obtain the state variable behaviors in the SDM and the corresponding values of linguistic variables in the ABM. Conclusions This research provides a way to incorporate high-dimension data in a hybrid ABM/SDM model. This research not only enriches the application of fuzzy set theory by capturing the dynamics of variables associated with interacting fuzzy agents that lead to aggregate behaviors but also informs implementation research by enabling the incorporation of linguistic variables at both individual and institutional levels, which makes unstructured linguistic data meaningful and quantifiable in a simulation environment. This research can help practitioners and decision makers to gain better understanding on the dynamics and complexities of precision intervention in healthcare. It can aid the improvement of the optimal allocation of resources for targeted group (s) and the achievement of maximum utility. As this technology becomes more mature, one can design policy flight simulators by which policy/intervention designers can test a variety of assumptions when they evaluate different alternatives interventions.

Background Systems science approaches, especially simulation approaches have been widely adopted by the researchers in health fields to gain better understanding of nonlinearities and dynamics generated by complex and dynamic interplay of many factors from biology to human behaviors and policy interventions. The range of applications includes but are limited to investigate issues of non‐communicable chronic disease (e.g. obesity, cardiovascular diseases, diabetes), contagious diseases (e.g. influenza, tuberculosis, HIV), healthcare management, health policy and system reform. National Institutes of Health (NIH) has actively promoted the use of systems science, predominately system dynamics model (SDM), agent‐based model(ABM), social network analysis (SNA), and discrete event simulation(DES) in health‐related research. Aims This study is to examine how citation network in the publications regarding application of SDM, ABM, SNA and DES to health research affect their diffusion. Methods We have retrieved and analyzed 3912 journal papers (English) regarding application of these four simulation approaches in health‐related research starting from year of 1963 to 2013. In this study, we employed bibliometric analysis and citation network analysis. Results Our results indicate that the order (from the best to the worst) of diffusion status of these simulation approaches is SNA(1963–2013), ABM(1995–2013), DES (1981–2013), and SDM(1972–2013). Our findings suggest that citation network in specific approach is one of the major driving forces for their rapid diffusion in wide areas of health fields. Results also unfold that more subgroups in the citation network effectively help expand the research areas which subsequently facilitate the diffusion of the corresponding simulation approach. Further findings show that high network centrality reinforced by some key subgroups (in certain field) and adopters (authors) does not help advance diffusion of that simulation approach in health‐related research. Conclusions The accelerated increase in the authors, journals, expansion in research area which rapidly boost the growth in the network of citations are jointly driving the fast diffusion of SNA, ABM, DES, and SDM in health research. Even though some challenges exist thus far, interdisciplinary and/or trans‐disciplinary research bring solution to those challenges. Based on our findings in this study, it is optimistic to claim that the diffusion of above‐mentioned systems simulation approaches in health‐related research remain active for next several decades. Support or Funding Information Funding: NIH U54 center grant, HD070725.