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In this paper, we use the notion of cyclic representation of a nonempty set with respect to a pair of mappings to obtain coincidence points and common fixed points for a pair of self-mappings satisfying some generalized contraction- type conditions involving a control function in partial metric spaces. Moreover, we provide some examples to analyze and illustrate our main results.

Theoretical method.

We establish some coincidence points and common fixed point results in partial metric spaces.

Results of this study are new and interesting.

In transitional economies, government support (GS) is considered to influence the development of the economy and industries and, consequentially, firms' intellectual capital (IC). However, empirical research has yet to explore the micro-mechanisms through which GS operates. Hence, the purpose of this study is to conduct an empirical inquiry into the specific role of GS on IC, considering the mediating effect of firm operational performance (OP).

Combining the institution-based theory, the resource orchestration paradigm and a dynamic perspective on IC, a new framework is constructed to evaluate the direct and indirect relationships existing among GS policies, firms' operational performance and IC. These processes and their outcomes are evaluated using mediating models with three steps and a panel regression based on panel data from 3,211 high-tech companies operating in China from 2008 to 2015.

Empirical findings confirm the existence of a significant direct relation between GS and IC and also suggest a mediating effect through operational performance.

(1) GS can be considered an institutional signal that boosts the attractiveness of a firm, thus enabling it to hire talent (human capital), build a wide network of relationships in the ecosystem (structural capital) and enhance its current relationships with financial service institutions and other stakeholders (relational capital). (2) This study, which considers GS an external resource, is one of the first attempts to explore how external resources influence firms' IC development through institutional pressures and mechanisms. The study confirms that multiple strategies exist through which government authorities and policymakers can influence firms' IC and in particular a combination of institutional factors and firm's resources and capabilities.

In the data-driven era, the quality of the data exchanged between suppliers and buyer can enhance the buyer’s ability to appropriately cope with the risks and uncertainties associated with raw material purchasing. This paper aims to address the issue of supplier selection and purchasing planning considering the quality of data by benefiting from suppliers’ synergistic effects.

An approach is proposed to measure data visibility’s total value using a multi-stage algorithm. A multi-objective mathematical optimization model is then developed to determine the optimal integrated purchasing plan in a multi-product setting under risk. The model contemplates three essential objective functions, i.e. maximizing total data quality and quantity level, minimizing purchasing risks and minimizing total costs.

With emerging competitive areas, in the presence of industry 4.0, internet of things and big data, high data quality can improve the process of supply chain decision-making. This paper supports the managers for the procurement planning of modern organizations under risk and thus provides an in-depth understanding for the enterprises having the readiness for industry 4.0 transformation.

Various data quality attributes are comprehensively subjected to deeper analysis. An applicable procedure is proposed to determine the total value of data quality and quantity required for supplier selection. Besides, a novel multi-objective optimization model is developed to determine the purchasing plan under risk.

In this paper, the explicit multistep, explicit multistep-SP and implicit multistep iterative sequences are introduced in the context of modular function spaces and proven to converge to the fixed point of a multivalued map T such that PρT, an associate multivalued map, is a ρ-contractive-like mapping.

The concepts of relative ρ-stability and weak ρ-stability are introduced, and conditions in which these multistep iterations are relatively ρ-stable, weakly ρ-stable and ρ-stable are established for the newly introduced strong ρ-quasi-contractive-like class of maps.

Noor type, Ishikawa type and Mann type iterative sequences are deduced as corollaries in this study.

The results obtained in this work are complementary to those proved in normed and metric spaces in the literature.

Fractional order nonlinear evolution equations (FNLEEs) pertaining to conformable fractional derivative are considered to be revealed for well-furnished analytic solutions due to their importance in the nature of real world. In this article, the autors suggest a productive technique, called the rational fractional (DξαG/G)-expansion method, to unravel the nonlinear space-time fractional potential Kadomtsev–Petviashvili (PKP) equation, the nonlinear space-time fractional Sharma–Tasso–Olver (STO) equation and the nonlinear space-time fractional Kolmogorov–Petrovskii–Piskunov (KPP) equation. A fractional complex transformation technique is used to convert the considered equations into the fractional order ordinary differential equation. Then the method is employed to make available their solutions. The constructed solutions in terms of trigonometric function, hyperbolic function and rational function are claimed to be fresh and further general in closed form. These solutions might play important roles to depict the complex physical phenomena arise in physics, mathematical physics and engineering.

The rational fractional (DξαG/G)-expansion method shows high performance and might be used as a strong tool to unravel any other FNLEEs. This method is of the form U(ξ)=∑i=0nai(DξαG/G)i/∑i=0nbi(DξαG/G)i.

Achieved fresh and further abundant closed form traveling wave solutions to analyze the inner mechanisms of complex phenomenon in nature world which will bear a significant role in the of research and will be recorded in the literature.

The rational fractional (DξαG/G)-expansion method shows high performance and might be used as a strong tool to unravel any other FNLEEs. This method is newly established and productive.

To dilute the financial difficulties in agricultural production and operation, the Chinese government has actively explored and developed rural supply chain finance (RSCF) service systems. The purpose of this study is to evaluate and analyze the performance of RSCF systems in China.

To evaluate the performance of RSCF systems in China, this study proposes a two-stage data envelopment analysis model. Compared with other models, the model proposed in this study considers not only the technical gap between RSCF systems but also the maximization of intermediate output to conform to the practice of RSCF.

Based on the empirical analysis, this study draws the following four conclusions. First, the overall efficiency of China's RSCF systems is low, and there remains great potential for improvement. Second, the technology gap ratio index score and meta-frontier efficiency of RSCF systems in Central China are the lowest in all regions, indicating that the technical level of RSCF systems in Central China is the lowest. Third, the relationship between rural residents' disposable income and the efficiency of RSCF systems is U-shaped, and the efficiency of RSCF systems in the high-income group is far greater than that of other income groups. Finally, the main reason for the lack of efficiency in RSCF seems to lie in management and technology.

This study divides all RSCF systems into four types according to management potential and technical potential, and recommend corresponding improvement suggestions for different kinds of RSCF systems.

Smog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction.

This paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness.

In order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies.

The proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective.

Based on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model.

The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability.

Pattern structures allow one to approach the knowledge extraction problem in case of partially ordered descriptions. They provide a way to apply techniques based on closed descriptions to non-binary data. To provide scalability of the approach, the author introduced a lazy (query-based) classification algorithm.

The experiments support the hypothesis that closure-based classification and regression allow one to both achieve higher accuracy in scoring models as compared to results obtained with classical banking models and retain interpretability of model results, whereas black-box methods grant better accuracy for the cost of losing interpretability.

This is an original research showing the advantage of closure-based classification and regression models in the banking sphere.

This paper aims to evaluate hematological parameters, blood glutathione (GSH), serum glucose-6-phosphate dehydrogenase (G6-PD) and liver function in favism animals' models after oral intake with a mixture of pickled olives and Vicia faba (V. faba) seeds.

Favism is a life-threatening hemolytic crisis. It results from the ingestion of V. faba by the individuals. The hemoglobin (Hb), hematocrit (Hct), red blood cells (RBCs), white blood cells (WBCs), serum glucose, blood GSH, serum G6-PD, serum thiobarbaturic acid reactive substances (TBARS), liver protein and liver function were evaluated after oral administration with a mixture of pickled olives with V. faba seeds in favism animals' models.

The favism-, vicine- and convicine-treated animals showed a significant decrease (p < 0.01) in Hb (6.42, 7.23 and 5.96 g/dl), Hct (25.4, 26.4 and 25.1%), RBCs (2.56, 2.45 and 2.60 106 cells/mm³, WBCs (4.35, 4.25 and 4.30 103 cells/mm³), serum glucose (95.8, 97.1 and 96.5 mg/dl), blood GSH (24.7, 26.6 and 23.8 mg/dl), serum G6-PD (15.8, 15.9 and 15.7 U/L), serum aspartate aminotransferase (6.35, 6.59 and 5.97 U/L), alanine aminotransferase (4.49, 4.61 and 4.50 U/L), total protein (6.54, 6.59 and 6.40 g/dl), albumin (3.84, 3.91 and 3.75 g/dl), globulin (2.70, 2.48 and 2.65 g/dl) and liver protein (3.37, 3.10 and 3.42 g/g tissue) but a significant increase (p < 0.01) in serum TBARS (38.7, 38.9 and 39.4 nmol/dl), alkaline phosphatase (275, 271 and 281 U/L) and total bilirubin (0.93, 0.89 and 0.91 mg/dl) compared to Hb (16.3 g/dl), Hct (45.3%), RBCs (5.80 106 cells/mm³), WBCs (9.45 103 cells/mm³), serum glucose (96.5 mg/dl), blood GSH (39.7 mg/dl), serum G6-PD (36.1 U/L), serum TBARS (18.0 nmol/dl), serum aspartate aminotransferase (19.8 U/L), alanine aminotransferase (9.23 U/L), alkaline phosphatase (214 U/L), total bilirubin (0.57 mg/dl), total protein (8.76 g/dl), albumin (4.85 g/dl), globulin (3.91 g/dl) and liver protein (6.28 g/g tissue) in control group. The oral administration with pickled olives + V. faba, pickled olives + vicine and pickled olives + convicine into favism animals' models, vicine-treated animals and convicine-treated animals, respectively pushed all the above-mentioned parameters to near the control levels.

V. faba contains vicine and convicine glycosides. Vicine and convicine glycosides in V. faba are hydrolyzed by intestinal microflora to aglycones divicine and isouramil, respectively. Divicine and isouramil are highly reactive compounds generating free radicals where divicine and isouramil are the main factors of favism. The ß-glucosidase in pickled olives converts both vicine and convicine glycosides into aglycones divicine and isouramil, respectively, in aerobic condition outside the human body (in inactive forms) and prevent these glycosides to cause favism.