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Compares the corrosion behaviour of Ti6Al4V titanium alloy, a conventional duplex stainless steel (UNS 31803) and AISI 304 austenitic stainless steel in synthetic biofluids using electrochemical techniques and comments on the suitability of DSS for use in biomedical applications. Finds that the general corrosion resistance of duplex stainless steels is slightly inferior to that of austenitic stainless steel and titanium alloy; duplex stainless steel does not show any sign of pitting when exposed to synthetic biofluids and exhibits excellent resistance to localised corrosion on par with that of titanium alloy. Concludes that duplex stainless steels are one of the best alternates to titanium alloys.

The purpose of this paper is to determine the effectiveness of using fully homomorphic encryption (FHE) to preserve the privacy of biometric data in an authentication system. Biometrics offers higher accuracy for personal recognition than traditional methods because of its properties. Biometric data are permanently linked with an individual and cannot be revoked or cancelled, especially when biometric data are compromised, leading to privacy issues.

By reviewing current approaches, FHE is considered as a promising solution for the privacy issue because of its ability to perform computations in the encrypted domain. The authors studied the effectiveness of FHE in biometric authentication systems. In doing so, the authors undertake the study by implementing a protocol for biometric authentication system using iris.

The security analysis of the implementation scheme demonstrates the effectiveness of FHE to protect the privacy of biometric data, as unlimited operations can be performed in the encrypted domain, and the FHE secret key is not shared with any other party during the authentication protocol.

The use of malicious model in the design of the authentication protocol to improve the privacy, packing methods and use of low-level programming language to enhance performance of the system needs to be further investigated.

The main contributions of this paper are the implementation of a privacy-preserving iris biometric authentication protocol adapted to lattice-based FHE and a sound security analysis of authentication and privacy.

This paper aims to report the influence of hexamethylenetetramine (HMTA) on phosphate coatings formed on AZ31 magnesium alloys.

These phosphate coatings were obtained by immersing magnesium alloys in phosphate baths with HMTA. The morphology and composition of the phosphate coatings were investigated via scanning electron microscopy, energy dispersive spectrometry and X-ray diffraction.

The phosphate coatings were mainly composed of CaHPO₄·2H₂O. The HMTA concentration in the phosphate bath influenced the crystallization and corrosion resistance of the phosphate coating.

The polarization curve shows that the anti-corrosion qualities of the phosphate coating were optimal when the HMTA concentration was 1.0 g/L in the phosphate bath. Electrochemical impedance spectroscopy (EIS) shows that the electrochemical impedances increased gradually when the HMTA concentration varied from 1.0 to 3.0 g/L.

This paper aims to investigate the effects of deposition time on the structure and anti-corrosion properties of a micro-arc oxidation (MAO)/Al coating on AZ31B Mg alloy.

The study describes the fabrication of the coating via a combined process of MAO with multi-arc ion plating. The structure, composition and corrosion resistance of the coatings were evaluated using scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction and electrochemical methods.

The Al-layer is tightly deposited with a good mechanical interlock along the rough interface due to the Al diffusion. However, the Al layer reduces the anti-corrosion of MAO-coated Mg alloy because of structural defects such as droplets and cavities, which act as channels for corrosive media infiltration towards the substrate. Fortunately, the Al layer improves the substrate corrosion resistance owing to its passive behaviour, and the corrosion resistance can be enhanced with increasing deposition time. All results indicate that a buffer layer fabricated through the duplex process improves the interfacial compatibility between the hard coating and soft Mg alloys.

An MAO/Al duplex coating was fabricated via a combined process of MAO and physical vapour deposition. MAO/Al duplex coatings exhibit obviously passive behaviours on AZ31 Mg alloy. The structure and corrosion resistance of MAO/Al coatings were investigated.

The purpose of this paper is to evaluate the salt spray corrosion (SSC) and electrochemical corrosion performances of CrNi, TiAlN/NiCr and CrNi–Al₂O₃–TiO₂ coatings on H13 steel, which improved the corrosion resistance of H13 hot work mold.

CrNi, TiAlN/NiCr and CrNi–Al₂O₃–TiO₂ coatings were fabricated on H13 hot work mold steel using a laser cladding and cathodic arc ion plating. The SSC and electrochemical performances of obtained coatings were investigated using a corrosion test chamber and electrochemical workstation, respectively. The corrosion morphologies, microstructure and phases were analyzed using an electron scanning microscope, optical microscope and X-ray diffraction, respectively, and the mechanisms of corrosion resistance were also discussed.

The CrNi coating is penetrated by corrosion media, producing the oxide of Fe₃O₄ on the coating surface; and the TiAlN coating is corroded to enter into the CrNi coating, forming the oxides of TiO and NiO, the mechanism is pitting corrosion, whereas the CrNi–Al₂O₃–TiO₂ coating is not penetrated, with no oxides, showing the highest SSC resistance among the three kinds of coatings. The corrosion potential of CrNi coating, TiAlN/CrNi and CrNi–Al₂O₃–TiO₂ coatings was –0.444, –0.481 and –0.334 V, respectively, and the corresponding polarization resistances were 3,074, 2,425 and 86,648 cm², respectively. The electrochemical corrosion resistance of CrNi–Al₂O₃–TiO₂ coating is the highest, which is enhanced by the additions of Al₂O₃ and TiO₂.

The CrNi, TiAlN/CrNi and CrNi–Al₂O₃–TiO₂ coatings on H13 hot work mold were firstly evaluated by the SSC and electrochemical performances.

The purpose of this study is to increase the wear resistance of Mg alloy by adding hard ceramic particles to it. The inclusion of hard ceramic particles further strengthen the Mg alloy, resulting in higher wear resistance. Mg alloys containing Zn, rare earth and Zr exhibit high specific strength and excellent creep resistance, making them suitable for aerospace components such as aircraft gearboxes and generator housings.

In the present study, composites have been produced in situ by using RZ5 mg alloy as matrix and TiC as reinforcement by self-propagating high-temperature synthesis technique. The abrasive wear behavior of RZ5 Mg alloy matrix reinforced with TiC particulates has also been examined. The pin-on-disc apparatus has been used for the tests. The abrasive paper is used as a counter body, and the results are obtained by changing sliding distance and applied load.

A notable enhancement in the wear resistance and mechanical properties of tested composite has been observed as compared to the RZ5 Mg alloy as a matrix. There is a uniform increment in the change in weight loss of RZ5-TiC composite with increasing sliding distance and applied load, but it decreases with increasing TiC content. The coefficient of friction (µ) also decreases uniformly with an increase in the reinforcement of TiC, but it decreases with an increase in applied load and sliding distance. The investigation of the worn composite, which determines dominant wear mechanisms as abrasion and plowing grooves on tested samples, has been done using field emission scanning electron microscopy.

The current manuscript provides a detailed abrasive wear analysis of RZ5-TiC composite by using different wear parameters. Specifically, extensive experimental data have been provided for RZ5-TiC composite. The effects of parameters such as applied load, sliding distance and Wt.% of TiC on the weight loss and coefficient of friction of the composites have been analyzed and discussed thoroughly.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

To explore building blocks of corporate value creation that can be effectively assembled by practicing managers to deconstruct corporate value creation into distinctive models (customer value creation and shareholder value creation) and stages (resource assembly and capability leverage) in the Indian Information Technology enabled Service (ITeS) industry for exploring efficiency differentials between large Indian ITeS companies.

Data envelopment analysis (DEA) technique has been used to uncover efficiency differentials in large Indian ITeS companies that represent 90% of all the ITeS companies listed in the Indian stock market and 13.9% of all companies listed in the Indian stock market, across industries.

This paper documents a nuanced understanding of interrelationships among activities that influence corporate value creation and comprehensively highlight those dominant activities that contribute to corporate value creation in an ITeS industry setting. The study demonstrates as to how companies can become more efficient in such crucial value creating components that result in superior corporate value. The explicating methodology proposed in this study can be handy for managers and can be extrapolated to other industry and national settings as well.

Deconstructing corporate value creation into granular models, customer value creation and shareholder value creation and further into two stages, being assembling resources to create capabilities and leveraging such capabilities to deliver value, this study provides hands-on value for managers in ITeS companies to create value.

Fusing the value creation and appropriation (VCA) framework, the resource-based view (RBV) and its extensions, this paper builds a robust theoretical model specification that is empirically tested.

Many manufacturers are exploring adopting smart technologies in their operations, also referred to as the shift towards “Industry 4.0”. Employees' contribution to high-tech initiatives is key to successful Industry 4.0 technology adoption, but few studies have examined the determinants of employee acceptance. This study, therefore, aims to explore how managers affect employees' acceptance of Industry 4.0 technology, and, in turn, Industry 4.0 technology adoption.

Rooted in the unified theory of acceptance and use of technology model and social exchange theory, this inductive research follows an in-depth comparative case study approach. The two studied Dutch manufacturing firms engaged in the adoption of Industry 4.0 technologies in their primary processes, including cyber-physical systems and augmented reality. A mix of qualitative methods was used, consisting of field visits and 14 semi-structured interviews with managers and frontline employees engaged in Industry 4.0 technology adoption.

The cross-case comparison introduces the manager's need to adopt a transformational leadership style for employees to accept Industry 4.0 technology adoption as an organisational-level factor that extends existing Industry 4.0 technology user acceptance theorising. Secondly, manager's and employee's recognition and serving of their own and others' emotions through emotional intelligence are proposed as an additional individual-level factor impacting employees' acceptance and use of Industry 4.0 technologies.

Synthesising these insights with those from the domain of Organisational Behaviour, propositions were derived from theorising the social aspects of effective Industry 4.0 technology adoption.

The study aims to examine the green finance customers' post-usage continuance behavior based on an extended social support theory (SST). Remarkably, this study explores five indirect predictors of green finance continuance behavior (GFCB) (i.e. environmental consciousness (EC), green bank marketing initiatives (GBMI), informational supports, emotional supports (EST) and psychological reactance) and a mediator (i.e. customer satisfaction).

In this study data were collected from 362 respondents from Bangladesh using a purposive sampling method with a structured self-administrative questionnaire and analyzed by partial least square structural equation and modeling using SMART PLS 3.0 software.

The results depict that the five predictors, i.e. information supports (ß = 0.367, t-statistics = 2.463, p < 0.001), EST (ß = 0.206, t-statistics = 2.315, p < 0.000), EC (ß = 0.324, t-statistics = 3.484, p < 0.000), GBMI (ß = 0.288, t-statistics = 2.028, p < 0.000), psychological reactance (ß = 0.126, t-statistics = 0.969, p < 0.052) influence GFCB while satisfaction is predicted by four predictors except psychological reactance (ß = 0.126, t-statistics = 0.969, p < 0.052). In addition, customer satisfaction (ß = 0.638, t-statistics = 6.317, p < 0.005) also has an impact on continuance behavior. Besides, the study understood that four predictors indirectly influence GFCB through satisfaction except psychological reactance ((ß = 0.080, t-statistics = 964, z = 0.958p < 0.338). Finally, the coefficient of determinations (R2) indicates that the five predictors explained 65.3% of changes in satisfaction, whereas 72.8% of changes are described by the five predictors and customer satisfactions.

Finally, this study highlights the social and managerial implications for the implementers of the green finance industry. It is recommended to emphasize green finance practice as it plays a crucial role in promoting environmental protection, ensuring social equity and driving economic growth. The green banking service providers, industry analysts, green consumers and respective government authorities can generalize green finance activities as an essential aspect of sustainable development to equalize the economic growth with a view to protecting environmental collapse and promoting renewable energy, energy efficiency, sustainable agriculture and other environmentally friendly activities.

The study will enormously contribute to the existing literature validating the proposed holistic framework applying SST along with EC, GBMI and psychological reactance in green finance continuance behavior.