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Sulfate-reducing bacteria (SRB) are recognized by scholars as the most important class of bacteria leading to corrosion of metal materials. It is important to use the properties of microorganisms to inhibit the growth of SRB in the corrosion protection of metal materials and to protect the environment.

In this work, the behavior of anaerobic Thiobacillus denitrificans (TDN) intracellular enzyme inhibition of SRB corrosion of EH36 steel was investigated with electrochemical impedance spectroscopy, biological detection technology and X-ray photoelectron spectroscopy.

Results showed that the SRB crude intracellular enzyme affected the corrosion behavior of EH36 steel greatly and the purified TDN intracellular enzyme inhibits SRB intracellular enzyme corrosion to EH36 steel.

A perfect enzyme activity inhibition mechanism will provide theoretical guidance for the selection and application of anticorrosion microorganisms, which is of scientific significance in the field of microbial anticorrosion research.

Bdellovibrio bacteriovorus is a gram-negative predatory bacterium which can potentially inhibit microbiologically influenced corrosion by preying on sulfate-reducing bacteria (SRB). However, no researches about the inhibition are reported according to the authors’ knowledge. The purpose of this paper was to investigate the Inhibition effect of B. bacteriovorus on the corrosion of X70 pipeline steel induced by SRB.

The effect of B. bacteriovorus on the growth of SRB was studied by measuring the optical density at 600 nm (OD600) and sulfate concentration in culture medium. X70 pipeline steel was used as the test material to investigate the anti-corrosion effect of B. bacteriovorus on SRB by conducting electrochemical analysis (including Tafel polarization curves and electrochemical impendence spectroscopy) and weight loss measurement.

B. bacteriovorus could inhibit the growth of SRB in culture medium by its predation on SRB, which led to decrease of OD600 value and increase of sulfate concentration. The results of electrochemical analysis indicated that B. bacteriovorus had positive inhibition efficiencies on SRB-induced corrosion of X70 pipeline steel. Moreover, corrosion rate of X70 pipeline steel was declined from 19.17 to 3.75 mg·dm-2·day-1 by the presence of B. bacteriovorus.

This is the first report about using B. bacteriovorus to inhibit the corrosion induced by SRB. Compared to other anti-corrosion methods, the microbial inhibition methods exhibit more considerable application value due to its low cost, high efficiency and non-pollution.

Based on text content analysis using big data, this study aims to explore differences in guest perceptions of peer-to-peer accommodations before and after COVID-19 to provide suggestions for the development of these properties in China postpandemic.

A guest perception dictionary was established by collecting Ctrip customer reviews of peer-to-peer accommodations. After data cleaning, thematic word analysis and semantic association network analysis were used to explore perceptions and thematic differences before and after COVID-19.

This research constructed a multidimensional framework of guest-perceived values for peer-to-peer accommodation in the context of COVID-19. The findings showed that the emphasis on functionality in peer-to-peer accommodation changed; perceived emotional values associated with peer-to-peer stays were more complex; perceived social values decreased, host–guest interactions were reduced and online communication became a stronger trend; tourist preferences for types of experiences changed, and people changed their destination selections; perceived conditional value was reflected in perceived risks, and the perceptions of environmental health, service and physical risks increased.

This research has constructed a multidimensional framework of tourist perceived value on the basis of peer-to-peer accommodation context and epidemic background and has thus shown the changes in tourist perceived value of peer-to-peer accommodation before and after COVID-19.

To the best of authors’ knowledge, this research constitutes the first attempt to explore the perceptual differences for peer-to-peer accommodations before and after COVID-19 based on an extensive data set of online reviews from multiple provinces of China.

The purpose of this paper is to calculate the local guaranteed fiscal revenue with the local fiscal revenue of 31 provinces, and predict their guaranteed fiscal revenue in 2018 with the artificial neural network (ANN).

The principal components analysis (PCA), particle swarm optimization (PSO) and extreme learning machine (ELM) model was designed to produce the inputs of KMV model. Then the KMV model was used for obtaining the default probabilities under different issuance scales. Data were collected from Wind Database. MATLAB 2018b and SPSS 22 were used in the field of modeling and results analysis.

This study’s findings show that PCA–PSO–ELM proposed in this research has the highest accuracy in terms of the prediction compared with ELM, back propagation neural network and auto regression. And PCA–PSO–ELM–KMV model can calculate the secure issuance scale of local government bonds effectively.

The sustainability forecast in this study can help local governments effectively control the scale of debt issuance, strengthen the budget management of local debt and establish the corresponding risk warning mechanism, which could make local governments maintain good credit ratings.

This study sheds new light on helping local governments avoid financial risks effectively, and it is conducive to establish a debt repayment reserve system for local governments and the proper arrangement for stock debt.

Generation Z (Gen-Z), sometimes known as “digital natives”, represents the first generation to become immersed in digital communication. In a multicultural environment, this study aims to explore which types of factors are most beneficial in connection with Gen-Z’s impulsive purchase behaviour.

This study adopts an exploratory sequential mixed-method design, incorporating both qualitative and quantitative approaches. In Study 1, focus group discussions are conducted to address “why” and “how” questions, whereas Study 2 uses a quantitative method to test the hypothetical model. The model is assessed using structural equation modelling. This study used the stimulus–organism–response (SOR) framework in the context of Instagram.

Building on Mehrabian and Russell’s (1974) concept and focus group discussions, Study 1 introduces a novel SOR model tailored to Instagram. In Study 2, the model is tested, and results confirm most hypotheses, except for three. Factors such as aesthetic appeal, scarcity promotions and discounted prices stimulate impulse buying behaviour in Gen-Z. Positive emotional responses evoked by these factors also influence impulse buying, whereas the impact of negative emotional responses is found to be insignificant.

This mixed-methods study enhances the theoretical understanding of Gen-Zers’ impulse buying behaviour by highlighting the influence of diverse independent variables. By using the SOR framework, it reveals the intricate emotional aspects impacting impulsive purchase decisions. The research provides new insights into online impulsive buying behaviour, particularly relevant to consumer psychology and behavioural economics among young collectivist consumers.

The purpose of this study is to test the relationship between mall relevance dimensions [(functional relevance (FNR), symbolic relevance (SYR), social relevance (SOR) and environmental relevance (ENR)] and shoppers' well-being (SWB), which in turn influences mall loyalty (ML). In addition, this study aims to investigate the moderating effect of social media celebrities (SMCs) on the association between SWB and ML.

A mall intercept survey was used to collect responses from mall shoppers. The authors received 426 valid responses from mall shoppers in Pakistan's three metropolitan cities (i.e. Karachi, Lahore and Islamabad). To test the hypotheses, partial least squares structural equation modelling (PLS-SEM) was used.

Findings reveal that FNR, SYR, ENR and SOR significantly and positively influence SWB. Similarly, SWB significantly affects ML. Moreover, SMCs moderate the positive relationship between SWB and ML.

This study is one of the pioneer studies examining mall relevance dimensions on SWB. In addition, this study contributes to the retailing literature by testing the moderation effect of SMCs on the relationship between SWB and ML. Likewise, this study provides insights for mall administration to focus on mall relevance in terms of FNR, SYR, ENR and SOR to enhance the current and prospects' SWB. Next, SMCs play a key role in enhancing SWB and ML.

Here, diazo coupling reaction was imparted on chemically inert lignin isolated from natural resources. Activated lignin was coupled with the diazotised aniline, m-nitroaniline, p-nitroaniline-, and p-anisidine gives organic pigments.

The continuous increase in particle size of pigments confirms addition of diazotised salt to lignin by coupling reaction. Further, the dispersing ability of these coloured pigments were exploited in polymer matrix. Epoxy-polyamine cross linking system was doped with difference percentage of pigments and coated on mild steel metal surface. The morphology of these composites was understood by SEM, particle size, differential scanning calorimeter and thermo gravimetric analysis.

The synthesised organic pigments were characterised by FT-IR, ¹H NMR and UV-visible spectroscopy. It was observed that hiding power of aniline- and m-nitroaniline–based azo pigments was more than p-nitroaniline- and p-anisidine–based azo pigments. Thermal properties as well as water contact angles of coatings were improved with pigment concentration. The chemical resistivity of coating was observed to be improved with the increasing % of lignin-based azo pigment.

Lignin-based azo pigment has great potential to replace metal oxide pigment and provide strategy for utilisation of lingo-cellulosic biowaste material.

The aim of this paper is to study the association between health consciousness, health belief model and intention to engage in healthy activities in addition to use restaurants.

An electronic questionnaire was distributed through social media and university website including questions on demographic variables and Likert scaled aspects of health consciousness, health belief model and behavioral intention to practice healthy habits and use fast-food restaurants.

A total of 92 Palestinian adults participated in this study. Age 28.5 ± 9.7 years. Of the studied group, 28.6% were males, body mass index = 24.4 ± 4.1 kg/m². Average health consciousness was 12.3 ± 3.1, health belief model (susceptibility = 10.4 ± 6.8, severity = 12.7 ± 7.2, benefit = 28.1 ± 5.3 and barriers = 17.8 ± 6.8) and for behavioral intention = 21.1 ± 6.4. In final regression models, only benefit was significantly associated with health consciousness (B = 0.18 ± 0.07, p = 0.012) and behavioral intention B = 0.26 ± 0.13, p = 0.05). Only barrier and severity were associated significantly with number of using restaurants weekly (0.04 ± 0.02, p = 0.03) and (0.05 ± 0.02, p = 0.004), respectively.

Health belief model partially explained use of restaurants and healthy lifestyle among Palestinians. This is a cross-sectional design and future clinical trials are needed.

To the best of the authors’ knowledge, this is the first study to address the role of health belief model and health consciousness in improving dietary style and habits.

The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework.

The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines CM of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM.

This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance.

The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce CM data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous CM such as medicine.

The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growing in maintenance-related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional CM practices. In this paper, an eVCM approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data.

The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based CM of rotating machines positively impacts the society with regard to the possibility of reducing how much time is actually spent on the accurate detection and classification of faults.

Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework, these studies are limited in the scope of faults, severity and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realized using big data management and data combination approaches.

This study aims to investigate consumer decision-making styles (CDMS) in the context of street food. In addition to the original CDMS constructs, two additional constructs, namely food safety risks and environmental risks, were included based on relevant literature. Furthermore, the study explores the moderating role of social media celebrities (SMCs) in bridging the intention-behaviour gap in street food consumption behaviour.

The data were collected through an online survey, with 300 participants providing useable responses. Partial least squares (PLS) analysis was employed to analyse the data.

The findings indicate that out of the eight identified CDMS, six styles, specifically recreational (hedonistic shopping consciousness), price consciousness, novelty-seeking, impulsiveness, confusion due to over-choice and brand loyalty, significantly influence consumers' intention to consume street foods. Additionally, the results support the moderating role of SMCs. This suggests that the presence and influence of SMCs play a significant role in shaping consumers' intention and behaviours towards street food consumption.

This study contributes significantly to the literature by adding two additional constructs, namely safety risks and environmental risks in CDMS. Moreover, this study fulfils the intention-behaviour gap in street food literature by exploring the moderation effect of SMCs.