Shandong University of Finance and Economics

The q-rung orthopair fuzzy sets (q-ROFs) are an important way to express uncertain information, and they are superior to the intuitionistic fuzzy sets and the Pythagorean fuzzy sets. Their eminent characteristic is that the sum of the qth power of the membership degree and the qth power of the degrees of non-membership is equal to or less than 1, so the space of uncertain information they can describe is broader. Under these environments, we propose the q-rung orthopair fuzzy weighted averaging operator and the q-rung orthopair fuzzy weighted geometric operator to deal with the decision information, and their some properties are well proved. Further, based on these operators, we presented two new methods to deal with the multi-attribute decision making problems under the fuzzy environment. Finally, we used some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods.

With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.

The COVID-19 pandemic has created significant challenges for energy transition. Concerns about the overwhelming emphasis on economic recovery at the cost of energy transition progress have been raised worldwide. More voices are calling for "green" recovery scheme, which recovers the economy while not compromising on the environment. However, limited academic attention has been paid to comprehensively investigating the implications of COVID-19 for global energy transition. This study thus provides a comprehensive analysis of the dynamics between energy transition and COVID-19 around the world and proposes a low-carbon energy transition roadmap in the post-pandemic era. Using energy data from the International Energy Agency (IEA), we first summarized and reviewed the progress of energy transition prior to COVID-19. Building on prior progress, we identified the challenges for energy transition during the pandemic from the perspectives of government support, fossil fuel divestment, renewable energy production capacity, global supply chain, and energy poverty. However, the pandemic also generates opportunities for global energy transition. We hence also identified potential opportunities for energy transition presented by the pandemic from the perspectives of price competitiveness, policy implementation efficiency, and renewable energy strengths. We further provided an in-depth discussion on the impact of current worldwide economic recovery stimulus on energy transition. Based on the identified challenges and opportunities, we proposed the post-pandemic energy transition roadmap in terms of broadening green financing instruments, strengthening international cooperation, and enhancing green recovery plans. Our study sheds light on a global low-carbon energy transition framework and has practical implications for green recovery schemes in post-pandemic times.

When reporting the results of clinical studies, some researchers may choose the five-number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation (SD), particularly for skewed data. For these studies, when included in a meta-analysis, it is often desired to convert the five-number summary back to the sample mean and SD. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this article, we propose to further advance the literature by developing a smoothly weighted estimator for the sample SD that fully utilizes the sample size information. For ease of implementation, we also derive an approximation formula for the optimal weight, as well as a shortcut formula for the sample SD. Numerical results show that our new estimator provides a more accurate estimate for normal data and also performs favorably for non-normal data. Together with the optimal sample mean estimator in Luo et al., our new methods have dramatically improved the existing methods for data transformation, and they are capable to serve as "rules of thumb" in meta-analysis for studies reported with the five-number summary. Finally for practical use, an Excel spreadsheet and an online calculator are also provided for implementing our optimal estimators.

With respect to multiple attribute group decision-making problems in which attribute values take the form of the interval-valued intuitionistic fuzzy numbers, the group decision-making methods based on some Hamacher aggregation operators, which extended the algebraic aggregation operators and Einstein aggregation operators, are developed. First, an interval-valued intuitionistic fuzzy Hamacher weighted averaging operator, interval-valued intuitionistic fuzzy Hamacher-ordered weighted averaging operator, interval-valued intuitionistic fuzzy Hamacher hybrid weighted averaging operator, interval-valued intuitionistic fuzzy Hamacher geometric weighted averaging operator, interval-valued intuitionistic fuzzy Hamacher geometric-ordered weighted averaging operator, and interval-valued intuitionistic fuzzy Hamacher geometric hybrid weighted averaging operator are proposed. Some desirable properties of these operators, such as commutativity, idempotency, monotonicity, and boundedness, are studied, and some special cases in these operators are analyzed. Furthermore, two methods to multicriteria decision group making based on these operators are developed. Finally, an illustrative example is given to verify the proposed methods and to demonstrate their practicality and effectiveness.

Digital transformation is essential if enterprises are to obtain a competitive advantage in the digital economy. However, successful transformation is a problem for both academia and industry. The literature mainly focuses on the net effects of single factors. Instead, we develop a configurational framework and propose that digital transformation does not depend on a single condition, but on interactions between environmental uncertainty and resource orchestration. Based on a fuzzy-set qualitative comparative analysis of Chinese enterprises undergoing digital transformation, we identify and explore five conditions that influence digital transformation. We show that both high and not-high levels of digital maturity can be achieved through different configurations of antecedents. There also exists a significant effect of technical uncertainty as well as synergy between environmental uncertainty and resource orchestration, which can jointly promote digital transformation. These findings enrich the literature on digital transformation and provide implications for the transformation of enterprises.

This study aimed to evaluate the dynamic effects of globalization, renewable energy consumption, non-renewable energy consumption, and economic growth on carbon-dioxide emission levels in Argentina over the 1970–2018 period. The econometric methodology considered in this study involved applications of methods that are robust to handling structural break problems in the data. Among the major findings, the Maki cointegration, with multiple structural breaks, analysis revealed long-run associations between carbon-dioxide emissions, renewable and non-renewable energy consumption, globalization, and economic growth. The elasticity estimates from the Autoregressive Distributed Lag model analysis showed evidence of renewable energy consumption and globalization reducing the emissions while non-renewable energy consumption was found to boost the emissions, both in the short- and long-run. Besides, globalization and renewable energy consumption were found to jointly reduce the emissions while globalization and non-renewable energy consumption were found to jointly boost the emissions in the long-run only. Moreover, the environmental Kuznets curve hypothesis was also verified in this study. Based on these key findings, several critically important policies are recommended.

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

In the real multi-attribute group decision making (MAGDM), there will be a mutual relationship between different attributes. As we all know, the Bonferroni mean (BM) operator has the advantage of considering interrelationships between parameters. In addition, in describing uncertain information, the eminent characteristic of q-rung orthopair fuzzy sets (q-ROFs) is that the sum of the qth power of the membership degree and the qth power of the degrees of non-membership is equal to or less than 1, so the space of uncertain information they can describe is broader. In this paper, we combine the BM operator with q-rung orthopair fuzzy numbers (q-ROFNs) to propose the q-rung orthopair fuzzy BM (q-ROFBM) operator, the q-rung orthopair fuzzy weighted BM (q-ROFWBM) operator, the q-rung orthopair fuzzy geometric BM (q-ROFGBM) operator, and the q-rung orthopair fuzzy weighted geometric BM (q-ROFWGBM) operator, then the MAGDM methods are developed based on these operators. Finally, we use an example to illustrate the MAGDM process of the proposed methods. The proposed methods based on q-ROFWBM and q-ROFWGBM operators are very useful to deal with MAGDM problems.

The theory of q-rung orthopair fuzzy sets (q-ROFSs) proposed by Yager effectively describes fuzzy information in the real world. Because q-ROFSs contain the parameter q and can adjust the range of expressed fuzzy information, they are superior to both intuitionistic and Pythagorean fuzzy sets. Archimedean T-norm and T-conorm (ATT) is an important tool used to generate operational rules based on the q-rung orthopair fuzzy numbers (qROFNs). In comparison, the Bonferroni mean (BM) operator has an advantage because it considers the interrelationships between the different attributes. Therefore, it is an important and meaningful innovation to extend the BM operator to the q-ROFNs based upon the ATT. In this paper, we first discuss q-rung orthopair fuzzy operational rules by using ATT. Furthermore, we extend BM operator to the q-ROFNs and propose the q-rung orthopair fuzzy Archimedean BM (q-ROFABM) operator and the q-rung orthopair fuzzy weighted Archimedean BM (q-ROFWABM) operator and study their desirable properties. Then, a new multipleattribute decision-making (MADM) method is developed based on q-ROFWABM operator. Finally, we use a practical example to verify effectiveness and superiority by comparing to other existing methods.

Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD) and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial denoised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVD-based methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.

This study uses text mining technology to construct an index of digital transformation and discusses the impact of digital transformation on enterprise innovation and its mechanisms from theoretical and empirical perspectives. It also analyzes whether digital transformation can significantly enhance enterprises’ value through innovation. The findings are presented as follows: first, digital transformation has a positive and significant impact on enterprise innovation, and this finding holds true when we conduct robustness testing and endogeneity processing. Second, the influence of digital transformation on enterprise innovation varies significantly according to enterprise size, ownership, and industry. Third, risk-taking plays an intermediary role between digital transformation and innovation. Fourth, the innovation incentive effect of digital transformation has a value enhancement function with a two-year lag, while it lacks a value enhancement function in the current year, following year, or next three years. In the modern era of innovation-driven and cross-border integration, this study deepens the theoretical understanding of innovation-driven and digital transformation. It also promotes the deeper integration of real and digital economies in practice.

Archimedean t -conorm and t -norm provide the general operational rules for intuitionistic fuzzy numbers (IFNs). The aggregation operators based on them can generalize most of the existing aggregation operators. At the same time, the Heronian mean (HM) has a significant advantage of considering interrelationships between the attributes. Therefore, it is very necessary to extend the HM based on IFNs and to construct intuitionistic fuzzy HM operators based on the Archimedean t -conorm and t -norm. In this paper, we first discuss intuitionistic fuzzy operational rules based on the Archimedean t -conorm and t -norm. Then, we propose the intuitionistic fuzzy Archimedean Heronian aggregation (IFAHA) operator and the intuitionistic fuzzy weight Archimedean Heronian aggregation (IFWAHA) operator. We also further discuss some properties and some special cases of these new operators. Moreover, we also propose a new multiple attribute group decision making (MAGDM) method based on the proposed IFAHA operator and the proposed IFWAHA operator. Finally, we use an illustrative example to show the MAGDM processes and to illustrate the effectiveness of the developed method.

The Bonferroni mean (BM) operator has the advantage of considering interrelationships between parameters, but it only can deal with crisp values. In recent years, many extended BM operators have been proposed to deal with fuzzy information. Dombi Bonferroni mean operators are special cases of general T-conorm and T-norm, which have the advantage of good flexibility with a general parameter. In this paper, we extend the BM operator based on the Dombi operations to propose the intuitionistic fuzzy Dombi Bonferroni mean (IFDBM) operator, the intuitionistic fuzzy weighted Dombi Bonferroni mean (IFWDBM) operator, the intuitionistic fuzzy Dombi geometric Bonferroni mean (IFDGBM) operator and the intuitionistic fuzzy weighted Dombi geometric Bonferroni mean (IFWDGBM) operator for dealing with the aggregation of intuitionistic fuzzy numbers (IFNs) and propose some multi-attribute group decision-making (MAGDM) methods. Firstly, we introduce the concept, the characteristics, the score function, the accuracy function and the operational rules of IFNs. Then, we propose the IFDBM operator, the IFWDBM operator, the IFDGBM operator and the IFWDGBM operator for aggregating IFNs. Then, we propose two MAGDM methods based on the proposed IFWDBM operator and the proposed IFWDGBM operator for dealing with MAGDM problems. Finally, we use an example to illustrate the MAGDM process of the proposed MAGDM methods. The proposed intuitionistic fuzzy Dombi Bonferroni mean operators are very useful to deal with MAGDM problems.

To be able to describe more complex fuzzy uncertainty information effectively, the concept of q-rung orthopair fuzzy sets (q-ROFSs) was first proposed by Yager. The q-ROFSs can dynamically adjust the range of indication of decision information by changing a parameter q based on the different hesitation degree from the decision-makers, where q ≥ 1, so they outperform the traditional intuitionistic fuzzy sets and Pythagorean fuzzy sets. In real decision-making problems, there is often an interaction phenomenon between attributes. For aggregating these complex fuzzy information, the Maclaurin symmetric mean (MSM) operator is more superior by considering interrelationships among attributes. In addition, the power average (PA) operator can reduce the effects of extreme evaluating data from some experts with prejudice. In this paper, we introduce the PA operator and the MSM operator based on q-rung orthopair fuzzy numbers (q-ROFNs). Then, we put forward the q-rung orthopair fuzzy power MSM (q-ROFPMSM) operator and the q-rung orthopair fuzzy power weighed MSM (q-ROFPWMSM) operator of q-ROFNs and present some of their properties. Finally, we present a novel multiple-attribute group decision-making (MAGDM) method based on the q-ROFPWA and the q-ROFPWMSM operators. The experimental results show that the novel MAGDM method outperforms the existing MAGDM methods for dealing with MAGDM problems.

Abstract Environmental sustainability and energy transition, especially the renewable energy transition, have become critical concerns of nations throughout the world in recent decades. The sustainable and eco‐friendly technologies have led to more sustainable methodologies, substantial stewardship of our natural resources, and the conversion to renewable energy sources, all of which have been demonstrated to benefit the environment significantly. However, prior studies have overlooked the ecological sustainability and energy transition effects of green technology innovation. Therefore, this study endeavored to investigate the role of green innovation (lnGRN) and financial globalization (lnFIG) on the sustainability of the environment (lnEFT) and energy transition (lnENT) in the United Kingdom using quarterly data for the period from 1995 to 2020. The study applied the time‐varying (bootstrapping) rolling window technique, which can retrieve casual associations among variables at different periods of sub‐samples. Besides this, the method is advantageous for addressing the non‐consistency of parameters and eliminating the pre‐test distortion. The novel Bootstrap Rolling‐Window full‐sample causality technique results demonstrate that lnGRN and lnFIG have unidirectional causality toward lnEFT and lnENT. Furthermore, the bootstrap rolling‐window subsamples in the final stage indicate that lnGRN and lnFIG mitigate lnEFT, whereas lnGRN and lnGDP enhance energy transition. On the other hand, lnGDP and lnETX contribute to environmental deterioration, while lnFIG hinders the energy transition. Several important policy implications are derived from the results to encourage financial globalization, green innovation technologies, renewable energy resources consumption, and environmental taxes.

In this technical note, constructive control techniques have been proposed for controlling feedforward nonlinear time-delay systems. The nonlinear terms admit an incremental rate depending on the input or delayed input. Based on the Lyapunov-Razumikhin theorem and Lyapunov-Krasovskii theorem, the delay-independent feedback controllers are explicitly constructed such that the closed-loop systems are globally asymptotically stable. An example is given to demonstrate the effectiveness of the proposed design procedure.

In the era of the digital economy, digital transformation (DT) has become a new approach for firms to gain competitive advantages in a context of intense and dynamic market competition. Companies in almost all industries have undergone or are currently undergoing DT. Due to limited resources and capabilities, the digitalization process of small and medium-sized enterprises (SMEs) is relatively slow, so it is critical to ascertain the key factors and paths that affect the success of DT for SMEs to optimize the allocation of resources. However, there is very little research on the DT of SMEs. In response to this literature gap, the purpose of this study is to discover the key factors of the DT in SMEs and explore their interaction mechanisms. From a holistic perspective, this study has identified six key factors from three dimensions of technology, organization, and environment, and based on the resource-based view and resource-dependence theory, constructed an action mechanism model. Structural equation modeling was used to analyze the data collected from 180 SMEs in China. The results show that technological and environmental factors have a positive impact on organizational capabilities, and then promote the success of DT of SMEs. Organizational capabilities play an intermediary role in the influence of technological and environmental factors on DT. In addition, employee skills positively moderate the relationship between organizational capabilities and the success of DT. This study contributes to the conceptual framework and management implications in the DT field. Our study provides practitioners with profound insights into the enterprise’s DT and suggests that enterprises attach importance to the improvement of organizational capabilities, and use strategy and talents as important resources to promote the success of enterprise DT.

We study how sharing a hometown or college connection with an incumbent member of China’s Politburo affects a candidate’s likelihood of selection as a new member. In specifications that include fixed effects to absorb quality differences across cities and colleges, we find that hometown and college connections are each associated with 5–9 percentage point reductions in selection probability. This “connections penalty” is equally strong for retiring Politburo members, arguing against quota-based explanations, and it is much stronger for junior Politburo members, consistent with a role for intra-factional competition. Our findings differ from earlier work because of our emphasis on within-group variation, and our focus on shared hometown and college, rather than shared workplace, connections. (JEL D72, O17, P26, Z13)

The existing three-way decision (TWD)-making methods cannot effectively handle incomplete multiattribute decision-making (MADM) problems in real life; it is necessary to explore an effective three-way MADM model in incomplete fuzzy decision systems (IFDSs). First, we consider the preference of decision makers for each alternative and introduce the concept of predecisions, thus an IFDS is obtained. Then, the weighted conditional probabilities are calculated with the aid of the defined similarity relation. Subsequently, we introduce the notion of relative utility functions and then present an approach to determine the relative utility function values. Afterward, we construct a TWD model in IFDSs and apply it to the modeling of incomplete MADM (IMADM) problems. Our study not only enriches TWD and MADM theories but also provides a new perspective for realistic IMADM problems. At last, the results of comparative and experimental analyses demonstrate the validity, stability, and superiority of our proposed model.