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The purpose of this study is to examine the impact of informal and formal learning processes on protection behavior with a focus on the role of resilience as a potential mediator.

Using web-based survey questionnaires on informal and formal learning, protection behavior, and resilience the survey collected information from 329 employees working in various organizations in India. Structural equation modeling was used to analyze employees’ protection behavior in protecting organization resources.

Findings indicate that informal learning has a significant positive effect on employees’ protection behavior, in protecting organization information. Further, resilience is found to be having a small mediation effect on informal learning and protection behavior relationships. Formal learning has no impact on employees’ protection behavior.

One of the scarce empirical research works that have substantiated the informal learning effect on organization protection behavior among employees.

Informal learning increasing the protection behavior has implications for the organizations in promoting social interactions. Formal learning not impacting protection behavior implies that organizations may reduce the expenses of sending their employees to formal training on information protection behavior.

Sharing patient health information (PHI) among hospitals has been much slower than the adoption of health record systems. This paper aims to investigate if privacy regulation (PR) or security measures (SMs) influence hospitals’ use of health information exchange (HIE) to share PHI with other providers (e.g. physicians, labs, hospitals). The study specifically focuses on how multiple PRs can impede and a strong national security infrastructure (NSI) can support HIE.

The study uses secondary data from a multi-national and multi-hospital survey administered by the European Union. The multi-level structure of the cross-sectional panel data is used to test the influence of both hospital-level (e.g. PR) and national-level variables (e.g. NSI) on HIE. A total of nine types of HIE, three types of PRs, nine SMs and other relevant control variables are considered. This study uses a two-level random intercept generalized linear model to test the hypothesis proposed in the study.

The study finds that national-level PRs (NLPR) have the strongest positive influence on HIE in comparison to regional (RLPR) and hospital-level (HLPR) PRs. Moreover, the study finds evidence that the presence of RLPR and HLPR, on average, decreases the positive impact of NLPR by 264%. The SMs also have a significant and positive impact on HIE. Adoption of an additional SM can increase the odds of engaging in a certain type of HIE between 21% and 61%. On the other hand, a strong NSI can also amplify the positive impact of SM on certain types of HIE.

This study extends prior research on the role of PRs in enabling HIE by considering the complexities brought up by adopting multiple PRs. NLPRs have the strongest impact on HIE in comparison to RLPRs or HLPRs. Moreover, public infrastructure initiatives such as those related to secure communications can also complement SMs adopted by the providers by encouraging HIE.

Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.

In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.

The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.

In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.

The purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor (IGBT) faults has become an essential topic of study. Demand for sustainable energy sources has been prompted by rising environmental pollution and energy requirements. Renewable energy has been identified as a viable substitute for conventional fossil fuel energy generation. Because of its rapid installation time and adaptable expenditure for construction scale, wind energy has emerged as a great energy resource. Power converter failure is particularly significant for the reliable operation of wind power conversion systems because it not only has a high yearly fault rate but also a prolonged downtime. The power converters will continue to operate even after the failure, especially the open-circuit fault, endangering their other parts and impairing their functionality.

The most widely used signal processing methods for locating open-switch faults in power devices are the short-time Fourier transform and wavelet transform (WT) – based on time–frequency analysis. To increase their effectiveness, these methods necessitate the intensive use of computational resources. This study suggests a fault detection technique using empirical mode decomposition (EMD) that examines the phase currents from a power inverter. Furthermore, the intrinsic mode function’s relative energy entropy (REE) and simple logical operations are used to locate IGBT open switch failures.

The presented scheme successfully locates and detects 21 various classes of IGBT faults that could arise in a two-level three-phase voltage source inverter (VSI). To verify the efficacy of the proposed fault diagnosis (FD) scheme, the test is performed under various operating conditions of the power converter and induction motor load. The proposed method outperforms existing FD schemes in the literature in terms of fault coverage and robustness.

This study introduces an EMD–IMF–REE-based FD method for VSIs in wind turbine systems, which enhances the effectiveness and robustness of the FD method.

The Regulatory Technology (RegTech) is said to be the use of information technology for regulatory monitoring, reporting and compliance. It is used to solve regulatory and compliance issues more effectively and efficiently. Regulators with the digitization of regulation and datafication of processes would get empowered to manage volumes of data. RegTech would assist them in understanding innovative products, transactions, risks, reporting and any market manipulation activities in real time. For successful use of RegTech, the regulatory framework of a country should be comprehensive to address issues that may arise in the use of RegTech. Thus, the purpose of this article is to analyze the adequacy of the Saudi Arabian legal framework to address RegTech adoption.

The researcher using logical analysis method analyzed the available laws and interpreted the law to see the applicability and the adequacy of laws to regulate the use of RegTech in Saudi Arabia. The content analysis was also used in this research to analyze the literature. This analysis helped to explain the available literature on the research topic and its relevancy and the gap in literature.

The analysis using the logical and content methodologies shows that Saudi Arabia has general law to address some of the issues that might arise in the adoption of RegTech. Nonetheless, amending some of the existing laws or introducing guidelines could help better uplift of RegTech and similar technologies in Saudi Arabia.

As businesses and regulatory authorities embrace technology for better and efficient delivery of services and products in Saudi Arabia, the research is timely to analyze the adequacy of the laws in Saudi Arabia for adoption of RegTech. In the use of RegTech, issues related to privacy, due diligence, accountability and transparency could arise, however, there is a dearth of literature in these areas relating to technology adoption.

International climate politics are gradually changing in terms of new and ground-breaking policies and decision-making spearheaded by national governments. The growing global demand to combat climate change reflects the current challenges the world is facing. India’s negotiations at United Nations Conference on Climate Change are based on “equity,” “historical responsibility” and the “polluter pays” agenda, until a shift in the voluntary reduction of carbon emissions takes place. The purpose of this study is to understand why India, a “deal breaker”, is seen as a “deal maker” in climate governance?

For a state like India, domestic preferences are equally important in introducing climate policies alongside its concerns over poverty reduction and economic development, which also stand with its sustainable development goals. This paper explains India’s decision-making using a two-level approach focusing on “domestic preferences.” This rationale is based on India’s historical background as well as new upcoming challenges.

This paper shows that India has both the domestic needs and long-term benefits of combating climate change to cut carbon emissions, which gives the responsibility primarily to domestic audiences and international societies.

This paper uses an international political lens to critically analyze India’s climate positions and politics from both domestic and international levels, demonstrating the importance of considering both short- and long-term goals. The outcome benefits not only the policymakers in India but also stakeholders in the Asia-Pacific and beyond.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

Conflicts between herders and farmers in Nigeria in the last five years have been destructive to the corporate existence of Nigerian society and the Nigerian State, especially in Northcentral, Northwestern and Southern Nigeria. This paper aims to investigate the relationships between state responses and peace-building in rural grazing communities in Nigeria using a National Survey on Peace-building in Nigeria conducted by this team using a cross-sectional survey of 1,711 farmers and herders.

The study adopted the political economy of the herder–farmers conflict in Africa to contextualise the problem. Data generated from the study were analysed using chi-square test and binary logistic regression model.

The results showed that protection of victims of herder–farmers conflict (P = 0.024), blockage of sources of illicit weapons (P = 0.000), arrest of leaders (P = 0.043), provision of shelter (P = 0.030), provision of food (P = 0.037), protection of women from sexual exploitation and abuse (P = 0.019) and use of the media were positively related to peace-building in the rural grazing areas. The study further found that when the Federal Government (ß= 0.452, P = 0.018), State Government (ß= 0.522, P = 0.018), private individuals (ß = 0.855, P = 0.000) and cooperative societies (ß = 0.744, P = 0.021) established ranches, peace was likely to be guaranteed as opposed to where herders (ß= –0.355, P= 0.029) were allowed to establish ranches in the rural grazing communities in Nigeria implying that the Federal and State Government must be cautious in the implementation of the Livestock Transformation Plans not to create an impression that it is designed to favour the herders.

To the best of the authors’ knowledge, this study is original and the paper has not been submitted to any journal.

The purpose of variable loading conditions (392 N-785N-392N-785N) with break-in period were used to study interactions between zinc dialkyl dithiophosphate (ZDDP) 0.1 P% (phosphorus) and fine-grade molybdenum disulfide (MoS₂) 3%, in different mixtures of NLGI 2 lithium stearate grease. Four-ball wear tests were used to evaluate the tribological properties of different grease mixtures such as coefficient of friction and wear. ASTM 2266 as reported by earlier studies is useful, but it is not representative of real-life applications where variable loads and speeds and different break-in periods play a role and could change the results and the nature of tribofilms.

In this study, chemical and mechanical properties of tribofilms were examined. Moreover, design of experiment was used to examine the data and shorten experimentation time. Research described here is investigating variable loading conditions for real-life applications by using a break-in period of 2 min at the start to minimize asperities and establish a clean surface. Design expert (DOE) analyzes responses to reveal those variables that are single factor and those that are multifactor whether synergistically or antagonistically.

The results indicated that spectrum loading with break-in period showed reduction in wear when tested in greases with ZDDP/MoS₂ combinations. Ramping up or down the load every 7.5 min for a rotational speed of 1,200 rpm and a total of 36,000 revolutions or 30-min time slowed the wear properties of lithium-based grease under different MoS₂ and ZDDP concentrations. Experiments indicated that wear was largely dependent on the loading condition and ZDDP additives during specific break-in period at 1,200 rotational speed. It is believed that MoS₂ greases perform better under spectrum loading and under constant loading when mixed with ZDDP phosphorus.

This research indicates that there is a synergistic interaction between ZDDP, MoS₂ and variable loading especially when a break-in period is applied. The results indicated that wear was largely dependent on the specific speed used with spectrum loading as presented in the energy dispersive spectroscopy and the Auger electron spectroscopy analysis, and thus a 3% MoS₂ grease with ZDDP (phosphorus: 0.1 Wt.%) are needed to improve the wear resistance and improve the friction characteristics.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0016/