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The purpose of this study is to prepare thermal transfer ribbons with good alcohol resistance.

A variety of alcohol-resistant thermal transfer inks were prepared using different polyester resins. The printing temperature, printing effect, adhesion and alcohol resistance of the inks on the label were studied to determine the feasibility of using the ink for manufacturing thermal transfer ribbons. The ink formulations were prepared by a simple and stable grinding technology, and then use mature coating technology to make the ink into a thermal transfer ribbon.

The results show that the thermal transfer ink has good scratch resistance, good alcohol resistance and low printing temperature when the three resins coexist. Notably, the performance of the ribbon produced by 500 mesh anilox roller was better than that of other meshes. Specifically, the ink on the matte silver polyethylene terephthalate (PET) label surface was wiped with a cotton cloth soaked in isopropyl alcohol under 500 g of pressure. After 50 wiping cycles, the ink remained intact.

The proposed method not only ensures good alcohol resistance but also has lower printing temperature and wider label applicability. Therefore, it can effectively reduce the loss of printhead and reduce production costs, because of the low printing temperature.

The purpose of this paper is to investigate the effects of laser power on the electrochemical corrosion performance in 3.5% NaCl, 0.1 M H₂SO₄ and 0.1 M NaOH solutions, which provided an experimental basis for the application of Al–Ti–Ni amorphous coating in marine environment.

Amorphous Al–Ti–Ni coatings were fabricated on S355 structural steel by laser thermal spraying (LTS) at different laser powers. The surface and cross-section morphologies, chemical element distribution, phases and crystallization behaviors of obtained coatings were analyzed using a scanning electron microscope, energy-dispersive X-ray spectroscope, X-ray diffraction and differential scanning calorimetry, respectively. The effects of laser power on the electrochemical corrosion performances of Al–Ti–Ni coatings in 3.5% NaCl, 0.1 M H₂SO₄ and 0.1 M NaOH solutions were investigated using an electrochemical workstation.

The crystallization temperature of Al–Ti–Ni coatings fabricated at the laser power of 1,300 and 1,700 W is ∼520°C, whereas that fabricated at the laser power of 1,500 W is ∼310°C. The coatings display excellent corrosion resistance in 3.5% NaCl and 0.1 M NaOH solutions, while a faster dissolution rate in 0.1 M H₂SO₄ solution. The coatings fabricated at the laser power of 1,300 and 1,700 W present the better electrochemical corrosion resistance in 3.5% NaCl and 0.1 M NaOH solutions, whereas that fabricated at the laser power of 1,500 W exhibits the better electrochemical corrosion resistance in 0.1 M H₂SO₄ solution.

In this work, Al-wire-cored Ti–Ni powder was first on S355 steel with the laser power of 1,300, 1,500 and 1,700 W, and the effects of laser power on the electrochemical corrosion performance in 3.5% NaCl, 0.1 M H₂SO₄ and 0.1 M NaOH solutions were investigated using an electrochemical workstation.

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.