Anticipated cancer burden of low individual fruit and vegetable consumption under climate change: A modelling study in China.

BACKGROUND: Dietary patterns with a high intake of fruits and vegetables (FV) are associated with a reduced risk of various cancers. It is not yet clear where and to what extent a decline in crop productivity caused by climate change may modify the distribution of related cancer burdens through reducing FV consumption in China. To design policies and interventions aimed at improving FV intake, regional monitoring is required on how consumption-changing factors might impact the associated cancer burdens by socio-demographic subpopulations. METHODS: A microsimulation study was conducted from a societal perspective to project the effects of cancers associated with inadequate FV intake attributable to climate change. We linked the International Model for Policy Analysis of Agricultural Commodities and Trade to a health modelling framework for obesity, gastric cancer, lung cancer, and oesophageal cancer in a close-to-reality synthetic population. RESULTS: In the presence of climate constantly change, the relative reduction in FV consumption would induce an additional 9.73 million disability-adjusted life years (DALYs) nationally over the period 2010-2050 ([CrI]: 7.83-12.13). The climate change-induced cancer burden is projected to disproportionately affect socio-demographic index regions from 0.65 to 5.06 million DALYs. CONCLUSIONS: Effects of climate change on FV consumption are anticipated to exacerbate intra-regional inequalities in the associated cancer burdens of China by 2050. By quantitatively analysing the impact of such dietary changes on regional health in light of climate change, our research can inform the design of public health interventions for heterogeneous populations, as health impact assessments based solely on the population as a whole cannot reflect significant differences across subpopulations.

A novel multi-attribute decision-making for ranking mobile payment services using online consumer reviews.

The onset of the COVID-19 pandemic has changed consumer usage behavior towards mobile payment (m-payment) services. Consumer usage behavior towards m-payment services continues to increase due to access to usage experiences shared through online consumer reviews (OCRs). The proliferation of massive OCRs, coupled with quick and effective decisions concerning the evaluation and selection of m-payment services, is a practical issue for research. This paper develops a novel decision evaluation model that integrates OCRs and multi-attribute decision-making (MADM) with probabilistic linguistic information to identify m-payment usage attributes and utilize these attributes to evaluate and rank m-payment services. First and foremost, the attributes of m-payment usage discussed by consumers in OCRs are extracted using the Latent Dirichlet Allocation (LDA) topic modeling approach. These key attributes are used as the evaluation scales in the MADM. Based on an unsupervised sentiment algorithm, the sentiment scores of the text reviews regarding the attributes are calculated. We convert the sentiment scores into probabilistic linguistic elements based on the probabilistic linguistic term set (PLTS) theory and statistical analysis. Furthermore, we construct a novel technique known as probabilistic linguistic indifference threshold-based attribute ratio analysis (PL-ITARA) to discover the weight importance of the usage attributes. Subsequently, the positive and negative ideal-based PL-ELECTRE I methodology is proposed to evaluate and rank m-payment services. Finally, a case study on selecting appropriate m-payment services in Ghana is examined to authenticate the validity and applicability of our proposed decision evaluation methodology.

A look at the focus shift in innovation literature due to Covid-19 pandemic.

This study explores the problems related to the development of innovation research in the field of business and economics and the change in their characteristics following the coronavirus disease 2019 (COVID-19) pandemic. We compile a comprehensive bibliometric analysis of 17,277 pre-epidemic publications and 4,240 post-epidemic publications from the Web of Science. Using bibliometric methods and visualization tools, we present the changes in these publications following the COVID-19 pandemic, and identify the influential countries and regions, sources, and references, and obtain features of keywords over time. The results show that innovation research is rich in content, and involves a wide range; it has been focusing on emerging topics, such as those concerning low-carbon, innovation forms, and epidemic environments, following the COVID-19 pandemic. This study contributes to the body of knowledge on innovation, and helps to understand the features and structures of innovation research in business and economics.

Ambient air pollutants concentration prediction during the COVID-19: A method based on transfer learning.

Research on the correlation analysis between COVID-19 and air pollution has attracted increasing attention since the COVID-19 pandemic. While many relevant issues have been widely studied, research into ambient air pollutant concentration prediction (APCP) during COVID-19 is still in its infancy. Most of the existing study on APCP is based on machine learning methods, which are not suitable for APCP during COVID-19 due to the different distribution of historical observations before and after the pandemic. Therefore, to fulfill the predictive task based on the historical observations with a different distribution, this paper proposes an improved transfer learning model combined with machine learning for APCP during COVID-19. Specifically, this paper employs the Gaussian mixture method and an optimization algorithm to obtain a new source domain similar to the target domain for further transfer learning. Then, several commonly used machine learning models are trained in the new source domain, and these well-trained models are transferred to the target domain to obtain APCP results. Based on the real-world dataset, the experimental results suggest that, by using the improved machine learning methods based on transfer learning, our method can achieve the prediction with significantly high accuracy. In terms of managerial insights, the effects of influential factors are analyzed according to the relationship between these influential factors and prediction results, while their importance is ranked through their average marginal contribution and partial dependence plots.

Consensus-Based Track Association with Multistatic Sensors under a Nested Probabilistic-Numerical Linguistic Environment.

Track association is an important technology in military and civilian fields. Due to the increasingly complex environment and the diversity of the sensors, it is a key factor to separate the corresponding track from multiple maneuvering targets by multisensors with a consensus. In this paper, we first transform the track association problem to multiattribute group decision making (MAGDM), and describe the MAGDM with nested probabilistic-numerical linguistic term sets (NPNLTSs). Then, a consensus model with NPNLTSs is constructed which has two key processes. One is a consensus checking process, and the other is a consensus modifying process. Based on which, a track association algorithm with automatic modification is put forward based on the consensus model. After that, the solution of a case study in practice is given to obtain the corresponding track by the proposed method, and it provides technical support for the track association problems. Finally, we make comparisons with other methods from three aspects, and the results show that the proposed method is effective, feasible, and applicable. Moreover, some discussions about the situation where there is only one echo point at a time are provided, and we give a discriminant analysis method.

Multi-attribute decision making based on VIKOR with probabilistic linguistic term sets: An application to the risk evaluation of foreign direct investment.

The multiple global environments have triggered changes in the international environment, leading to a sharp decline of foreign direct investment (FDI) compared to pre-pandemic level. To evaluate the investment risk of FDI and make optimal investment decision becomes the most important issue for investors. This paper focuses on the evaluation of investment risk for FDI. First, an index system for risk evaluation of FDI is constructed. Then, we introduce the probabilistic linguistic entropy and cross entropy measures, based on which, a programming model is developed to identify the objective attribute weights. A composite weight derivation method, which takes both the objective attribute weights and the subjective attribute weights into account, is further introduced. In view of attributes' uncertainty and fuzziness and the conflicting characteristics of some attributes, the VIKOR (the Serbian name: VlseKriterijumska Optimizacija I Kompromisno Resenje, means multi-criteria optimization and compromise solution) method is used to evaluate the risk of FDI under the probabilistic linguistic environment. Furthermore, a case study is presented to illustrate the proposed method. The comparative analysis and some further discussions verify the validity of the proposed method for the FDI risk evaluation.

Deep learning in economics: a systematic and critical review.

From the perspective of historical review, the methodology of economics develops from qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of the superiority in learning inherent law and representative level, deep learning models assist in realizing intelligent decision-making in economics. After presenting some statistical results of relevant researches, this paper systematically investigates deep learning in economics, including a survey of frequently-used deep learning models in economics, several applications of deep learning models used in economics. Then, some critical reviews of deep learning in economics are provided, including models and applications, why and how to implement deep learning in economics, research gap and future challenges, respectively. It is obvious that several deep learning models and their variants have been widely applied in different subfields of economics, e.g., financial economics, macroeconomics and monetary economics, agricultural and natural resource economics, industrial organization, urban, rural, regional, real estate and transportation economics, health, education and welfare, business administration and microeconomics, etc. We are very confident that decision-making in economics will be more intelligent with the development of deep learning, because the research of deep learning in economics has become a hot and important topic recently.

Regret-Theoretic Multiattribute Decision-Making Model Using Three-Way Framework in Multiscale Information Systems.

The existing multiattribute decision-making (MADM) methods on multiscale information systems (MSISs) are generally studied from the utility point of view, which may cause two problems: 1) the objects are strictly classified into good or bad, which may lead to misclassification and 2) the risk attitude and psychological behaviors of decision makers are difficult to be reflected. In light of this, this article proposes a wide three-way decision (3WD) model on an MSIS, which combines 3WD theory and regret theory and can precisely make up for these two shortcomings. First, by virtue of regret theory, an outranking relation on the comprehensive MSIS is constructed according to the regret-rejoicing index. Second, objects in the outranking relation are classified into three different domains by a clustering method. In each domain, the ranking of objects can be calculated by using the relative closeness coefficient. Finally, we use the cases in the database to simulate the experiment to verify the decision-making effect of the proposed model. Comparative analysis and experimental analysis also show the effectiveness, superiority, and stability of the proposed model.

A bibliometric analysis of carbon neutrality: Research hotspots and future directions.

Global attention has shifted in recent years to climate change and global warming. The international community has set the objective of carbon neutrality to address the climate crisis. Carbon neutrality has drawn significant attention as a crucial step in the fight against climate change, with individual nations having established their carbon neutrality targets. This paper aims to use bibliometric analysis to investigate research hotspots and trends in carbon neutrality research, and accesses the literature through the Web of Science (WoS) core database and undertakes an in-depth examination of 909 publications linked to carbon neutrality around the world using Vosviewer and Bibliometrix software. According to the findings, the number of carbon neutrality publications has increased dramatically in recent years. There are also notable differences in carbon neutrality research across countries and regions. China and the US are the primary drivers and leaders of carbon neutrality research, and developing countries have relatively little carbon neutrality research. Research has concentrated on carbon neutrality's practical, technical, policy, and economic aspects, as well as renewable energy sources, carbon conversion technologies, and carbon capture and storage technologies are also research hotspots. The paper also outlines opportunities for the advancement of carbon neutrality research in the future, including how it might be further integrated with Artificial intelligence (AI) and the metaverse, and how to attack the difficulties and uncertainties faced by the post-epidemic rebound. This study aids in understanding the current state of the field of carbon neutrality research and can be used to guide future studies.

Advantage matrix: two novel multi-attribute decision-making methods and their applications.

By comparing attributes of objects in an information system, the advantage matrix on the object set is established in this paper. The contributions can be identified as follows: (1) The advantage degree is proposed by the accumulation of the advantage matrix. (2) Based on the advantage matrix, the advantage (disadvantage) neighborhood approximation operator and the advantage (disadvantage) correlation approximation operator are defined and studied. Based on these two new operators, the neighborhood degree and the correlation degree are presented. The relationships between them are also investigated to demonstrate the value of the proposed method. (3) Finally, based on the above three degrees, new algorithms are designed, in which the effectiveness and robustness of the algorithms are analyzed by practical examples.

Predicting the effectiveness of interventions on population-level sodium reduction: A simulation modeling study.

Background and Aims: Interventions that significantly reduce dietary sodium intake are anticipated to decrease gastric cancer (GCa) burden. However, the optimal restriction strategies remain unknown at present. This study aims to understand where and to what extent policies modifying sodium consumption change the distribution of GCa burden, and the effects of potential salt reduction strategies in China. Methods: The synthetic population in this microscopic simulation study is close to reality. We incorporated estimates of dietary patterns and GCa risk into the model of excess salt consumption. These estimates and simulated population were obtained from the China Health and Nutrition Survey, Global Burden of Disease Project, and the sixth census of China's National Bureau of Statistics, respectively. Results: In the no intervention scenario, we estimated that disease burdens due to excess sodium intake would be at 472.9 million disability-adjusted life years (DALYs) nationally between 2010 and 2030 (95% credible interval [CrI]: 371.1-567.7). The GCa burden caused by high sodium is projected to have a disproportionate impact on the central and southern provinces of China (9.2 and 4.5 million DALYs, respectively). Implementing a cooking salt substitute strategy would be expected to avoid a larger portion of GCa burden (about 67.2%, 95% CrI: 66.8%-67.6%) than the salt-restriction spoon program (about 16.7%, 95% CrI: 16.1%-17.4%). Conclusion: Dietary salt reduction policy is very powerfully effective in reducing the GCa burden overall. It is expected that proposed salt substitutes are more effective than traditional salt-restriction spoons to avoid increased inequality.

Cross-platform hotel evaluation by aggregating multi-website consumer reviews with probabilistic linguistic term set and Choquet integral.

In order to adequately utilize and integrate both ratings and comments from multiple websites, this paper proposes a new hotel evaluation model with probabilistic linguistic information processing. Taking consumers' possible psychological activities when leaving their reviews into consideration, this paper adapts the Weber-Fechner Law with the linguistic scale function and develop a novel unbalanced linguistic scale function. This paper also attempts to develop a method that enables adjusting linguistic-term formations among different websites to make full use of information. Then, we learn the decision criteria and the corresponding weight of hotel evaluation based on analyzing rating rules and consumer comments. Moreover, considering the interrelationships among criteria, this paper extends the Choquet integral to the probabilistic linguistic term set (PLTS) environment and designs some novel fusion operators. Furthermore, considering the fact that different websites mostly focus on heterogeneous hotel criteria, this paper puts forward a weighted averaging linear assignment based ranking method with the aid of PLTS Choquet integral. Finally, a case study of hotel evaluation is given to illustrate the validity and applicability of our proposed approach.

Integrating prospect theory with variable reference point into the conversion-based framework for linear ordinal ranking aggregation.

Considering that converting linear ordinal ranking (LOR) information into interval utility values can not only improve the computability of LOR information but also explore the degree of preference for different alternatives for decision-makers hidden behind LOR information, this paper proposes a conversion-based LOR aggregation method to aggregate LOR information under risk. Given that the behaviours of decision-makers are influenced by risk, this paper adopts prospect theory to depict the decision-makers' behaviours under risk in the conversion-based aggregation process. To achieve this, the information energy for LOR is constructed firstly, and its features are analysed, which makes a basis for the conversion process. After that, the details about how to integrate the prospect theory with variable reference points into the conversion-based aggregation framework are presented. Finally, an example (exploring the financial product preferences of a group of respondents) evidences the practicality of the proposed method. Further, some analyses and discussions are conducted to verify the rationality and stability of the method.

Insights into financial technology (FinTech): a bibliometric and visual study.

This paper conducted a comprehensive analysis based on bibliometrics and science mapping analysis. First, 848 publications were obtained from Web of Science. Their fundamental characteristics were analyzed, including the types, annual publications, hot research directions, and foci (by theme analysis, co-occurrence analysis, and timeline analysis of author keywords). Next, the prolific objects (at the level of countries/regions, institutions, journals, and authors) and corresponding pivotal cooperative relationship networks were used to highlight who pays attention to FinTech. Furthermore, the citation structures of authors and journals were investigated, including citation and co-citation. Additionally, this paper presents the burst detection analysis of cited authors, journals, and references. Finally, combining the analysis results with the current financial environment, the challenges and future development opportunities are discussed further. Accordingly, a comprehensive study of the FinTech documents not only reviews the current research characteristics and trajectories but also helps scholars find the appropriate research entry point and conduct in-depth research.

Consensus Model Handling Minority Opinions and Noncooperative Behaviors in Large-Scale Group Decision-Making Under Double Hierarchy Linguistic Preference Relations.

With the rapid development of society and continual progress of science and technology, large-scale group decision-making (LSGDM) problems are very commonly encountered in real-life situations. Considering that the information required for decision-making and people's cognition processes is becoming more and more complex, double hierarchy linguistic preference relation (DHLPR) can be used to express complex linguistic information reasonably and intuitively. Sometimes experts in LSGDM unwillingly modify their preferences or even modify them on purpose in a contrary way to the other experts. Thus, differing opinions or minority preferences are often referred to as obstacles to decision-making. This article develops a consensus model to manage minority opinions and noncooperative behaviors in LSGDM with DHLPRs. In addition, to establish the consensus model, some basic tools, such as the clustering method, weights-determining method, and adjustment coefficients-determining method, are developed. Finally, a practical LSGDM problem is set up to prove that the proposed consensus model is feasible and effective, and some comparative analyses are made to highlight the advantages of these methods and models, as well as to analyze current deficiencies.

Green Supplier Selection Based on Green Practices Evaluated Using Fuzzy Approaches of TOPSIS and ELECTRE with a Case Study in a Chinese Internet Company.

The selection of appropriate green chain suppliers is a very critical decision for effective and efficient green supply chain management in today's increased awareness and significant environmental pressures from various stakeholders. The aim of this paper is to screen appropriate green chain suppliers based on a framework using fuzzy TOPSIS and ELECTRE for a Chinese internet company. The framework is proposed, grounded on a literature review on green supply chain management practices, after which an empirical analysis is made to be applied an integrated suppliers selection, based on green practices incorporating specifically data collected of the 12 criteria from a set of 12 available suppliers. We use a fuzzy TOPSIS and ELECTRE approach to rank the green chain suppliers, and the results of the proposed framework are compared with the ranks obtained by both the outranking degrees and the incomparability among the actions of fuzzy ELECTRE methodology. Finally, sensitivity analysis was conducted to test the feasibility of the best alternative. The results indicated that the best supplier was alternative 9, and there were four dominant criteria: management support for GSCM, used environmentally friendly materials, followed legal environmental requirements and policies, and reduced the use of harmful substances.

Additive Integrals of q -Rung Orthopair Fuzzy Functions.

The q -rung orthopair fuzzy set ( q -ROFS) is a powerful tool to deal with uncertainty and ambiguity in real life. The theoretical basis for processing the continuous q -rung orthopair fuzzy information is q -rung orthopair fuzzy calculus ( q -ROFC) and the main object is q -rung orthopair fuzzy functions ( q -ROFFs). Recently, the authors proposed derivatives and differentials of q -ROFFs in the framework of q -ROFC. In this paper, we aim to further study the q -rung orthopair fuzzy integral ( q -ROFI). It is the most important and fundamental part of the q -ROFC theoretical system with direct and powerful applications. Our contribution is the indefinite and definite integrals, and bridges the fuzzy calculus theoretical gap of the nonlinear q -ROFFs. In particular, we begin with the indefinite integral of q -ROFFs, which can be regarded as the anti-derivatives operations of our previous work. Some of their basic properties are discussed. Next, we give the accurate concept of definite integrals of q -ROFFs under additive operations, and obtain the explicit integral formula. Some properties of q -ROFIs, such as comparison, algebraic operations, and mean value theorem are analyzed. Finally, we generalize the q -ROFI to the case when membership and nonmembership functions are allowed to be correlated. After the theoretical results have been established, we present some numerical examples to demonstrate the rationality and effectiveness of integrating continuous q -rung orthopair fuzzy data with the q -ROFIs.

A Stochastic Multi-Attribute Method for Measuring Sustainability Performance of a Supplier Based on a Triple Bottom Line Approach in a Dual Hesitant Fuzzy Linguistic Environment.

China is a developing country and with the speeding up of its industrialization, the environmental problems are becoming more serious, environmental pollution is a major environmental health problem in China. In order to have a more effective management and control of the significant growth issues of environment pollution, green supply chain incentives have started, which is kind of market incentive aiming to moderate the adverse effects of environmental pollution. Proper green chain supply selection and evaluation of companies is becoming very essential in sustainable green supply chain management. Generally speaking, decision-makers (DMs) prefer to provide a set of feasible and quantitative information for making performance evaluation, which motivates us to propose a framework using dual hesitant fuzzy linguistic term set (DHFLTS) and hesitant fuzzy linguistic term set (HFLTS) to select green suppliers. In this paper, group satisfaction and the regret theory are adopted for elicitation of preference information. The DHFLTS and HFLTS provide qualitative preferences of the DMs as well as reflect their hesitancy, inconsistency, and vagueness. Further, two new group satisfaction degrees are defined called the group satisfaction of hesitant fuzzy linguistic term set and dual hesitant fuzzy linguistic term set. Some properties of group satisfaction with DHFLST and HFL are also discussed. Unknown attribute weights are obtained to construct a novel Lagrange function optimization model to maximize the group satisfaction degree, which is an extension of general group satisfaction degree. A novel methodological approach based on two group satisfaction degrees framework and regret theory is developed to rank and select green chain suppliers focusing on specific selection objectives. The proposed model and method of this paper allow the DM to execute different fuzzy scenarios by changing importance weights attached to the triple-bottom-line areas. In the final part, the advantage of the proposed group satisfaction degree under DHFL and HFL background over the existing group satisfaction degree using examples have been presented with different computational combinations.

Therapeutic Schedule Evaluation for Brain-Metastasized Non-Small Cell Lung Cancer with A Probabilistic Linguistic ELECTRE II Method.

With the rapid development of modern medicine, therapeutic schedules of brain-metastasized non-small cell lung cancer (NSCLC) are expanding. To assist a patient who suffers from brain-metastasized NSCLC to select the most suitable therapeutic schedule, firstly, we establish an indicator system for evaluating the therapeutic schedules; then, we propose a probabilistic linguistic ELECTRE II method to handle the corresponding evaluation problem for the following reasons: (1) probabilistic linguistic information is effective to depict the uncertainty of the therapeutic process and the fuzziness of an expert's cognition; (2) the ELECTRE II method can deal with evaluation indicators that do not meet a fully compensatory relationship. Simulation tests on the parameters in the proposed method are provided to discuss their impacts on the final rankings. Furthermore, we apply the proposed method to help a patient with brain-metastasized NSCLC at the Sichuan Cancer Hospital and Institute to choose the optimal therapeutic schedule, and we present some sensitive analyses and comparative analyses to demonstrate the stability and applicability of the proposed method.

Nature Disaster Risk Evaluation with a Group Decision Making Method Based on Incomplete Hesitant Fuzzy Linguistic Preference Relations.

Because the natural disaster system is a very comprehensive and large system, the disaster reduction scheme must rely on risk analysis. Experts' knowledge and experiences play a critical role in disaster risk assessment. The hesitant fuzzy linguistic preference relation is an effective tool to express experts' preference information when comparing pairwise alternatives. Owing to the lack of knowledge or a heavy workload, information may be missed in the hesitant fuzzy linguistic preference relation. Thus, an incomplete hesitant fuzzy linguistic preference relation is constructed. In this paper, we firstly discuss some properties of the additive consistent hesitant fuzzy linguistic preference relation. Next, the incomplete hesitant fuzzy linguistic preference relation, the normalized hesitant fuzzy linguistic preference relation, and the acceptable hesitant fuzzy linguistic preference relation are defined. Afterwards, three procedures to estimate the missing information are proposed. The first one deals with the situation in which there are only n-1 known judgments involving all the alternatives; the second one is used to estimate the missing information of the hesitant fuzzy linguistic preference relation with more known judgments; while the third procedure is used to deal with ignorance situations in which there is at least one alternative with totally missing information. Furthermore, an algorithm for group decision making with incomplete hesitant fuzzy linguistic preference relations is given. Finally, we illustrate our model with a case study about flood disaster risk evaluation. A comparative analysis is presented to testify the advantage of our method.