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The purpose of this paper is to set out a study of a Mannich base, which was synthesized and used as an acidizing corrosion inhibitor first, and to the corrosion inhibitor mechanism.

A Mannich base, 1-phenyl-3-(1-pyrrolidinyl)-propanone (PHPP), was synthesized with acetophenone, pyrrolidine and formaldehyde at pH = approximately 2-3. The structure of PHPP was characterized by elemental analysis and Fourier transform infrared spectroscopy (FTIR). The corrosion inhibition of PHPP on N80 steel in 15 per cent hydrochloric acid (HCl) was studied by weight loss method, scanning electron microscope (SEM) and energy dispersive X-ray analysis (EDAX), and the adsorption behavior of PHPP on the surface of N80 steel was discussed.

The results showed that the inhibition efficiency reached to 99.8 per cent and corrosion rate was 2.65 g·m-2·h-1 at 0.6 per cent of PHPP concentration in 15 per cent HCl, which indicated that PHPP presented excellent corrosion inhibition performance. The results of SEM and EDAX analysis showed that PHPP could be absorbed on the surface of N80 steel. The adsorption process of PHPP on the surface of N80 steel was chemisorption. This process was spontaneous and obeyed Langmuir adsorption isotherm.

It was found that PHPP presented excellent corrosion inhibition performance, and it is practicable to enhance oil production in oilfield development as a oil-well acidizing inhibitor. The study results can provide theoretical guidelines for the development of the inhibitor.

This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on developing countries like Nigeria. The research identifies barriers to this integration and suggests solutions, intending to provide practical insights for improving operational efficiency in the O&G sector.

The study commenced with an exhaustive review of extant literature to identify existing barriers to I4.0 implementation and contextualise the study. Subsequent to this foundational step, primary data are gathered through the administration of carefully constructed questionnaires targeted at professionals specialised in maintenance within the upstream O&G sector. A semi-structured interview was also conducted to elicit more nuanced, contextual insights from these professionals. Analytically, the collected data were subjected to descriptive statistical methods for summarisation and interpretation with a measurement model to define the relationships between observed variables and latent construct. Moreover, the Relative Importance Index was utilised to systematically prioritise and rank the key barriers to I4.0 integration to CBM within the upstream O&G upstream sector.

The most ranked obstacles in integrating I4.0 technologies to the CBM strategy in the O&G industry are lack of budget and finance, limited engineering and technological resources, lack of support from executives and leaders of the organisations and lack of competence. Even though the journey of digitalisation has commenced in the O&G industry, there are limited studies in this area.

The study serves as both an academic cornerstone and a practical guide for the operational integration of I4.0 technologies within Nigeria's O&G upstream sector. Specifically, it provides an exhaustive analysis of the obstacles impeding effective incorporation into CBM practices. Additionally, the study contributes actionable insights for industry stakeholders to enhance overall performance and achieve key performance indices (KPIs).

Laser navigation without a reflector does not require setup of reflector markers at the scene and thus has the advantages of free path setting and flexible change. This technology has attracted wide attention in recent years and shows great potential in the field of automatic logistics, including map building and locating in real-time according to the environment. This paper aims to focus on the application of feature matching for map building.

First, an improved linear binary relation algorithm was proposed to calculate the local similarity of the feature line segments, and the matching degree matrix of feature line segments between two adjacent maps was established. Further, rough matching for the two maps was performed, and both the initial rotation matrix and the translation vector for the adjacent map matching were obtained. Then, to improve the rotation matrix, a region search optimization algorithm was proposed, which took the initial rotation matrix as the starting point and searched gradually along a lower error-of-objective function until the error sequence was nonmonotonic. Finally, the random-walk method was proposed to optimize the translation vector by iterating until the error-objective function reached the minimum.

The experimental results show that the final matching error was controlled within 10 mm after both rotation and translation optimization. Also, the algorithm of map matching and optimization proposed in this paper can realize accurately the feature matching of a laser navigation map and basically meet the real-time navigation and positioning requirements for an automated-guided robot.

A linear binary relation algorithm was proposed, and the local similarity between line segments is calculated on the basis of the binary relation. The hill-climbing region search algorithm and the random-walk algorithm were proposed to optimize the rotation matrix and the translation vector, respectively. This algorithm has been applied to industrial production.

Most source recording device identification models for Web media forensics are based on a single feature to complete the identification task and often have the disadvantages of long time and poor accuracy. The purpose of this paper is to propose a new method for end-to-end network source identification of multi-feature fusion devices.

This paper proposes an efficient multi-feature fusion source recording device identification method based on end-to-end and attention mechanism, so as to achieve efficient and convenient identification of recording devices of Web media forensics.

The authors conducted sufficient experiments to prove the effectiveness of the models that they have proposed. The experiments show that the end-to-end system is improved by 7.1% compared to the baseline i-vector system, compared to the authors’ previous system, the accuracy is improved by 0.4%, and the training time is reduced by 50%.

With the development of Web media forensics and internet technology, the use of Web media as evidence is increasing. Among them, it is particularly important to study the authenticity and accuracy of Web media audio.

This paper aims to promote the development of source recording device identification and provide effective technology for Web media forensics and judicial record evidence that need to apply device source identification technology.

This paper aims to exploit shape-memory polymers as self-healable materials. The underlying mechanism involved the thermal transitions as well as the enrichment of the healing reagents and the closure of the crack surfaces due to shape recovery. The multi-stimuli-triggered shape memory composite was capable of self-healing under not only direct thermal but also electrical stimulations.

The shape memory epoxy polymer composites comprising the AgNWs and poly (ε-caprolactone) were fabricated by dry transfer process. The morphologies of the composites were investigated by the optical microscope and scanning electron microscopy (SEM). The electrical conduction and the Joule heating effect were measured. Furthermore, the healing efficiency under the different stimuli was calculated, whose dependence on the compositions was also discussed.

The AgNWs network maintained most of the pathways for the electrons transportation after the dry transfer process, leading to a superior conduction and flexibility. Consequently, the composites could trigger the healing within several minutes, as applied with relatively low voltages. It was found that the composites having more the AgNWs content had better electrically triggered performance, while 50 per cent poly (ε-caprolactone) content endowed the materials with max healing efficiency under thermal or electrical stimuli.

The findings may greatly benefit the application of the intelligent polymers in the fields of the multifunctional flexible electronics.

Most studies have by far emphasized on the direct thermal triggered cases. Herein, a novel, flexible and conductive shape memory-based composite, which was capable of self-healing under the thermal or electrical stimulations, has been proposed.

This paper aims to disclose the evolution of pendulum hardness of two-component acrylic polyurethane coatings during the cure process and attempts to describe the quantitative relationship between pendulum hardness and curing time. These findings are helpful for the study of fast curing acrylic polyurethane coatings.

The pendulum hardness method was used to monitor the hardness of two-component acrylic polyurethane coatings during curing. The quantitative relationship between pendulum hardness and curing time can be obtained with Avrami equation.

The evolution of coating pendulum hardness can be divided into three stages. By using the Avrami equation that explained the influence of both the acid value and the curing temperature on the drying speed of hydroxyl acrylic resin, the evolution of coating pendulum hardness during curing can also be accurately described.

It should be noted that the physical meaning of the Avrami exponent, n, is not yet clear.

The results are of great significance for the development of fast-curing hydroxyl-functional acrylic resins, with the potential to improve the drying speed of the coatings used in automotive refinish.

It is novel to divide the pendulum hardness into three stages, and, for the first time, the Avrami equation is utilized to describe the evolution of coating pendulum hardness during curing.

Greenhouse gas (GHG) emission has a detrimental impact on climate change. There is an increasing trend for firms to use disclosure to signal stakeholders about its environmental responsibilities and performance in dealing with climate change. China is one of the countries producing the most carbon emissions. Over the last decade, Chinese state-owned enterprises (SOEs) are becoming important players in international trade. However, the existing literature provides limited evidence on how Chinese SOEs influence GHG disclosure. Through the lens of stakeholder–agency theory, this chapter studies the top 300 listed firms to examine the relationship between Chinese SOEs and the likelihood of GHG disclosure. The result suggests a negative relationship between Chinese SOEs and the likelihood of GHG disclosure. This could be explained as a consequence of the managers' political self-interests, economic and policy-oriented decision-making process and the power differentials between the government and SOE managers. This research extends the GHG literature to Chinese SOEs context, providing direct evidence on how state ownership impacts on GHG disclosure.

The problem of privacy protection in digital libraries is causing people to have increasingly extensive concerns. This study aims to design an approach to protect the preference privacy behind users’ book browsing behaviors in a digital library.

This paper proposes a client-based approach, whose basic idea is to construct a group of plausible book browsing dummy behaviors, and submit them together with users’ true behaviors to the untrusted server, to cover up users’ sensitive preferences.

Both security analysis and evaluation experiment demonstrate the effectiveness of the approach, which can ensure the privacy security of users’ book browsing preferences on the untrusted digital library server, without compromising the usability, accuracy and efficiency of book services.

To the best of the authors’ knowledge, this paper provides the first attempt to the protection of users’ behavior privacy in digital libraries, which will have a positive influence on the development of privacy-preserving libraries in the new network era.

This paper aims to propose a method for finding the maximum rotational speed of an inclined turntable at which the stability of the bearing oil film is maintained.

The finite difference method was used to solve the Reynolds equation. Variation of bearing capacity of a tilted hydrostatic turret over time was determined. The combined effect of tilt and rotational speed of the turret on the oil film stability was also analyzed.

When the turntable is operated at low speeds with only small angle of tilt, stability of the oil film is maintained. At lower rotational speeds, a smaller angle of tilt improves the bearing capacity and ensures stability of the oil film. Whereas, higher rotational speeds can have a considerable influence on the bearing capacity.

The results demonstrate that the inclination or tilt of the turntable significantly affects the stability of the oil film.

As a common form of failure in industry, corrosion causes huge economic losses. At present, with the development of computational techniques, artificial intelligence (AI) is playing a more and more important role in the field of scientific research. This paper aims to review the application of AI in corrosion protection research.

In this paper, the role of AI in corrosion protection is systematically described in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction.

With efficient and in-depth data processing methods, AI can rapidly advance the research process in terms of anticorrosion materials and methods, corrosion image recognition and corrosion life prediction and save on costs.

This paper summarizes the application of AI in corrosion protection research and provides the basis for corrosion engineers to quickly and comprehensively understand the role of AI and improve production processes.