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In this paper we propose a methodology for the estimation of customer satisfaction conceived as a latent variable specified in the American Customer Satisfaction Index (ACSI) structural model. The current proposal puts forward: the approaches of structural equations, since it involves the dual problem of indeterminacy of the latent scores and the normality assumed; the PLS approach, because of its drawbacks (shown in depth in the paper). The ACSI model will be estimated in a reduced rank regression (RRR) framework, showing that under a non restrictive hypothesis, shared by PLS, the structural model can be viewed as a RRR model between two blocks of manifest variables. Finally, in the paper an application is shown to assess the students’ satisfaction in respect to the service of a big real estate agency, operating in the houses‐to‐let market, in Bologna (Italy) for 2002.

Influencer marketing mediated by social media is prevalent in social commerce. Micro-, meso- and macro-influencers all play an irreplaceable role in marketing. The purpose of this paper is to explore how companies with limited budgets choose influencers according to products' various levels of brand familiarity.

This study constructs an evolutionary game model of influencer marketing based on evolutionary game theory on complex networks. This model initiates various networks to demonstrate how influencers disseminate information and constructs update mechanisms to depict how individuals react to this information based on individuals' information utility and friends' strategies.

Simulation results suggest that companies should invest more in macro-influencers than in meso-influencers, however investing all in macro-influencers is not a good choice. The investment in meso-influencers will increase as brand familiarity decreases, whereas it will not exceed investment in macro-influencers. Furthermore, the accumulation of micro-influencers can accelerate the marketing process.

This study examines the combined effects of micro-influencers, meso-influencers and macro-influencers in marketing by simulating the marketing process initiated by influencers on social media.

Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In order to solve such problems, the purpose of this paper is to propose a novel image super-resolution algorithm based on improved generative adversarial networks (GANs) with Wasserstein distance and gradient penalty.

The proposed algorithm first introduces the conventional GANs architecture, the Wasserstein distance and the gradient penalty for the task of image super-resolution reconstruction (SRWGANs-GP). In addition, a novel perceptual loss function is designed for the SRWGANs-GP to meet the task of image super-resolution reconstruction. The content loss is extracted from the deep model’s feature maps, and such features are introduced to calculate mean square error (MSE) for the loss calculation of generators.

To validate the effectiveness and feasibility of the proposed algorithm, a lot of compared experiments are applied on three common data sets, i.e. Set5, Set14 and BSD100. Experimental results have shown that the proposed SRWGANs-GP architecture has a stable error gradient and iteratively convergence. Compared with the baseline deep models, the proposed GANs models have a significant improvement on performance and efficiency for image super-resolution reconstruction. The MSE calculated by the deep model’s feature maps gives more advantages for constructing contour and texture.

Compared with the state-of-the-art algorithms, the proposed algorithm obtains a better performance on image super-resolution and better reconstruction results on contour and texture.

The purpose of this paper is to investigate the effect of high-intensity pulsed ion beam (HIPIB) intensity on the structure and corrosive properties of microarc oxidation (MAO) films on AZ31 magnesium alloy and explore the mechanism for modified anti-corrosion properties of irradiated films.

The energy deposited on the coating surface influences the remelting process of the MAO coatings significantly, which was closely related to the intensity of HIPIB; therefore, HIPIB with various intensities of 100-350 A/cm², was selected to modify the MAO films on AZ31 magnesium alloy. The changes in film structure and phase structure of modified films were characterized by scanning electron microscopy and X-ray diffractometry (XRD) with CuKα, respectively. The corrosive behavior of the MAO films was featured with polarization curves and electrochemical impedance spectrum in 3.5 per cent NaCl solution on a PAR 2273 electrochemical workstation.

The results clearly show that a dense, continual and remelted layer with a few micrometers in thickness was obtained on the irradiated surface at 200 A/cm², which are mainly responsible for the modified and optimal anti-corrosion property of MAO films by suppressing/retarding the process of the corrosive electrolyte infiltration into magnesium substrate surface.

The paper reveals that HIPIB irradiation could modify the corrosion resistance by producing a remelted compact layer on the MAO film surface at a suitable irradiation parameter and explored the modified mechanism of MAO films.

The purpose of this paper is to introduce an effective method to discriminate seal inks with Raman microscopy.

Raman spectra could effectively avoid interference from the paper and give extra peak information in the inks discrimination and identification. Thus, a Renishaw invia confocal Raman microscope system was employed for ink analysis in this study. A total of 12 representative seal ink samples, widely used in seven Chinese provinces, were investigated using the latest model of Renishaw Raman microscope.

Four types of inks were identified and discriminated successfully. Popular pigments such as Pigment Scarlet Powder, Pigment Yellow 55, phthalocyanine blue, Bronze red C and PbCrO3 were all identified in these seal ink samples. The indicative peaks to identify and discriminate the inks were also summarised and tentatively interpreted.

More ink samples were needed to establish a useful library. Many other pigments used in inks were still unknown.

This method was proved to be fast, accurate and non-destructive, and it could be more easily applied in real cases than Fourier transform infrared spectroscopy.

This method can help scientists discriminate some inks, which can hardly be discriminated by other techniques. The results are useful for the ink analysis and discrimination in forensic (document examination and file source identification), polymer and pigment fields.

The purpose of this paper is to identify different automotive coatings using Confocal Raman microscope which could hardly be differentiated with Fourier transform infrared microscope (FTIR).

Raman spectroscopy was used to provide extra vibration information to infrared spectroscopy. Paints preparation was not necessary, and only 30 s was needed for each sample in an optimised method. Paints were first analysed by FTIR and then compared with Raman microscope.

Raman microscope was used to address the lack of ability of FTIR in discriminating four groups of paints in same colours. This study indicated that Raman microscopy is especially effective in sensing pigments and could successfully identify all pigments in the paints.

The two instruments in combination produce accurate results than when used individually, especially in complex and multi-layered paints analysis.

The method proved to be fast, accurate and non-destructive, and it could be easily applied to real cases.

With this method, scientists could discriminate some coating types which were hard to be discriminated by other techniques.

Magnesium-aluminum layered double hydroxides (LDH) with a platelet-like morphology were synthesized through a modified co-precipitation method. The purpose of this paper is to investigate calcined Mg-Al-CO₃ LDH (CLDH) as chloride ion traps.

The morphology and chemical composition of the synthesized materials were studied through UHR-SEM, EDS, FT-IR and XRD. The chloride ion adsorption was confirmed by XRD; the characteristic diffraction peaks of the reconstructed LDH structure were revealed, similar to the one before the thermal treatment process. The effect of varying the experimental conditions on the chloride ion adsorption, such as the initial target-ion concentration, the adsorbent material dosage, the solution temperature and the solution pH was also investigated.

The experimental data fitting revealed that the Langmuir equation is a better model on the basis of correlation coefficients (R²) and that the pseudo-second kinetic model can satisfactorily describe the chloride ion uptake.

The ability of Mg-Al CLDH to recover their layered structure upon exposure to aqueous sodium chloride solutions with concentrations up to 0.3 M (10,636 mg/L) through the chloride adsorption and the simultaneous rehydration process is clearly demonstrated.

It is predicted that the bitcoin system will enter many areas of our lives in the future. Although it has many benefits, the most criticized issue of this system is excessive energy consumption. Since an excessive amount of energy is used in this process, it is vital that the preferred energy is environmentally friendly. For example, mining bitcoin with fossil fuels is likely to release a significant amount of carbon gas into the atmosphere. This will cause serious environmental problems. Therefore, great attention should be paid to the type of energy that will be used in bitcoin mining. In this study, it was stated that it would be beneficial to prefer nuclear energy in bitcoin mining. As a result of the use of nuclear energy, no carbon gas is released into the atmosphere. This will help reduce environmental pollution to a significant extent. Another dimension of nuclear energy being preferred in bitcoin mining is that the energy produced is continuous. The main reason for this is that nuclear energy is not affected by climatic conditions and temperature differences. Thanks to the preference of nuclear energy in bitcoin mining, it will be possible to contribute to the uninterrupted progress of this process.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

It is seen that emotional intelligence, which is the subject of research in many disciplines, especially psychology and social sciences, is one of the prominent concepts. Emotional intelligence has become a sought-after feature especially for today's leaders. It is possible to say that emotional intelligence is an important feature for leaders in the energy sector as well as for leaders in other fields. This study aims to determine how and to what extent emotional intelligence has an effect on leadership characteristics in today's conditions where the need for leadership is increasing. In this study, it is aimed to reveal the effects of emotional intelligence levels of managers in the energy sector on leadership. For this purpose, hypotheses have been developed, and a research has been conducted on managerial employees in an enterprise operating in the energy sector in order to test the hypotheses. In the study, the effects of emotional intelligence dimensions such as optimism, use of emotions, and evaluation of emotions on leadership were measured by analyzing the data obtained as a result of the questionnaire application. SPSS package program was used for statistical analysis of the data. According to the results of the research, it was determined that effective leadership increased as optimism/mood regulation increased. Similarly, as the evaluation of emotions increased, effective leadership increased.