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This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

Based on the theoretical predictions of media equation theory and the computers-are-social-actors (CASA) perspective, this study aims to examine the effects of performance error type (i.e. logical, semantic or syntactic), task type and personality presentation (i.e. dominant/submissive and/or friendly/unfriendly) on users’ level of trust in their personal digital assistant (PDA), Siri.

An experimental study of human–PDA interactions was performed with two types of tasks (social vs functional) randomly assigned to participants (N = 163). While interacting with Siri in 15 task inquiries, the participants recorded Siri’s answers for each inquiry and self-rated their trust in the PDA. The answers were coded and rated by the researchers for personality presentation and error type.

Logical errors were the most detrimental to user trust. Users’ trust of Siri was significantly higher after functional tasks compared to social tasks when the effects of general usage (e.g. proficiency, length and frequency of usage) were controlled for. The perception of a friendly personality from Siri had an opposite effect on social and functional tasks in the perceived reliability dimension of trust and increased intensity of the presented personality reduced perceived reliability in functional tasks.

The research findings contradict predictions from media equation theory and the CASA perspective while contributing to a theoretical refinement of machine errors and their impact on user trust.

This study aims to use regression Least-Square Support Vector Machine (LS-SVM) as a probabilistic model to determine the factor of safety (FS) and probability of failure (PF) of anisotropic soil slopes.

This research uses machine learning (ML) techniques to predict soil slope failure. Due to the lack of analytical solutions for measuring FS and PF, it is more convenient to use surrogate models like probabilistic modeling, which is suitable for performing repetitive calculations to compute the effect of uncertainty on the anisotropic soil slope stability. The study first uses the Limit Equilibrium Method (LEM) based on a probabilistic evaluation over the Latin Hypercube Sampling (LHS) technique for two anisotropic soil slope profiles to assess FS and PF. Then, using one of the supervised methods of ML named LS-SVM, the outcomes (FS and PF) were compared to evaluate the efficiency of the LS-SVM method in predicting the stability of such complex soil slope profiles.

This method increases the computational performance of low-probability analysis significantly. The compared results by FS-PF plots show that the proposed method is valuable for analyzing complex slopes under different probabilistic distributions. Accordingly, to obtain a precise estimate of slope stability, all layers must be included in the probabilistic modeling in the LS-SVM method.

Combining LS-SVM and LEM offers a unique and innovative approach to address the anisotropic behavior of soil slope stability analysis. The initiative part of this paper is to evaluate the stability of an anisotropic soil slope based on one ML method, the Least-Square Support Vector Machine (LS-SVM). The soil slope is defined as complex because there are uncertainties in the slope profile characteristics transformed to LS-SVM. Consequently, several input parameters are effective in finding FS and PF as output parameters.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

The paper aims to examine the impacts and ethics of utilizing Artificial Intelligence (AI) in Indian policing. It explores both the positive and negative consequences of using AI, as well as the ethical considerations that have be taken into account.

This study is based on secondary sources of information, such as national and international reports, journal articles, and institutional websites that discuss the use of AI technology by the police in India.

AI has proven to be effective in policing, from preventing crime to identifying criminals, by detecting potential crimes in advance with fewer resources and in more areas. In India, the police use AI technology not only for facial recognition but also for crime mapping, analysis, and building blocks. However, factors such as caste, religion, language, and gender continue to cause conflict. India has shown a strong interest in using AI technology for policing, and wishes to accelerate its implementation in various policing contexts, including law and order. This paper calls for an assessment of the complexities and uncertainties brought about by new technologies in policing with ethical considerations.

This paper can provide valuable insights for policy-makers, academics, and practitioners engaged in discussions and debates concerning the ethical considerations associated with the adoption of AI tools in policing practices.

This study aims to examine the challenges in the implementation of energy management systems in residential buildings to lower the running cost and achieve a better energy-efficient building.

This study adopted a mixed research method. Quantitative data was gathered by issuing a research questionnaire to 20 Delphi experts, while qualitative data was acquired through a Systematic Literature Review. Data received was analyzed using the descriptive analysis method.

The findings revealed that the main barriers to incorporating energy management systems (EMSs) in residential buildings consist of a lack of awareness of energy management systems, lack of management commitment to energy management, lack of knowledge about energy management systems, lack of funds for energy management systems, resistance to energy management technology by the property owners and property managers, distrust and resistance to energy management technology by the property owners, high initial cost of energy management technologies, shortage of technicians for energy management technologies, the nonexistence of local manufacturers of energy management equipment, lack of incentives for efficient energy management and high repair costs of energy management technologies.

The specific focus on residential buildings may limit the applicability of findings to commercial or industrial sectors. Further research is warranted to accommodate other energy-consuming sectors.

People’s perceptions, either wrong or correct, affect their ability to make an informed decision to adopt energy management systems, denying them the opportunity to reap the associated benefits. Therefore, there is an urgent need for the residential industry stakeholders and the government to increase educational opportunities for property owners, managers and property tenants on the importance of energy management systems.

This research presents the potential obstacles and problematic areas that residents may encounter while using these energy management systems. Consequently, they will be able to make a well-informed choice when installing energy management systems. Moreover, the research elucidates the identification of novel perspectives and also unexamined obstacles that impede the widespread use of energy management systems in residential buildings.

Achieving an appropriate indoor environment quality (IEQ) is crucial to a green office environment. Whilst much research has been carried out across the globe on the ideal IEQ for green offices, little is known about which indoor environment New Zealand office workers prefer and regard as most appropriate. This study investigated New Zealand office workers' preference for a green environment.

Workers were conveniently selected for a questionnaire survey study from two major cities in the country – Wellington and Auckland. The perception of 149 workers was analysed and discussed based on the workers' demographics. The responses to each question were analysed based on the mean, standard deviation, frequency of responses and difference in opinion.

The results showed that workers' preferences for an ideal IEQ in green work environments depend largely on demographics. New Zealand office workers prefer work environments to have more fresh air and rely on mixed-mode ventilation and lighting systems. Also New Zealand office workers like to have better acoustic quality with less distraction and background noise. Regarding temperature, workers prefer workspaces to be neither cooler nor warmer. Unique to New Zealand workers, the workers prefer to have some (not complete) individual control over the IEQ in offices.

This study was conducted in the summer season, which could have impacted the responses received. Also the sample size was limited to two major cities in the country. Further studies should be conducted in other regions and during different seasons.

This study provides the opportunity for more studies in this area of research and highlights significant findings worthy of critical investigations. The results of this study benefit various stakeholders, such as facilities managers and workplace designers, and support proactive response approaches to achieving building occupants' preferences for an ideal work environment.

This study is the first research in New Zealand to explore worker preferences of IEQ that is not limited to a particular building, expanding the body of knowledge on workers' perception of the ideal work environment in the country.

Responding to calls for deeper analysis of the conceptual foundations of service infusion in manufacturing, this paper examines the underlying assumptions that: (i) manufacturing firms incorporating services follow a pathway, moving from pure-product to pure-service offerings, and (ii) profits increase linearly with this process. We propose that these assumptions are inconsistent with the premises of behavioural and learning theories.

Machine learning algorithms are applied to test whether a successive process, from a basic to a more advanced offering, creates optimal performance. The data were gathered through two surveys administered to USA manufacturing firms in 2021 and 2023. The first included a training sample comprising 225 firms, whilst the second encompassed a testing sample of 105 firms.

Analysis shows that following the base-intermediate-advanced services pathway is not the best predictor of optimal performance. Developing advanced services and then later adding less complex offerings supports better performance.

Manufacturing firms follow heterogeneous pathways in their service development journey. Non-servitised firms need to carefully consider their contextual conditions when selecting their initial service offering. Starting with a single service offering appears to be a superior strategy over providing multiple services.

The machine learning approach is novel to the field and captures the key conditions for manufacturers to successfully servitise. Insight is derived from the adoption and implementation year datasets for 17 types of services described in previous qualitative studies. The methods proposed can be extended to assess other process-based models in related management fields (e.g., sand cone).

This research demonstrates the theoretical merit of a reference architecture-based approach to life cycle cost (LCC) analysis system provision in the built environment. LCC insight is considered fundamental to sustainable decision making by asset managers; however, the current capabilities in practice do not align with the political ambition and the scale of competencies required to realise sectoral emissions–reduction targets.

In pursuing practical outcomes, the study employs a custom design science research-inspired methodology. Domain requirements are gathered via literature research as an initial top-down software reference architecture which is refined, bottom-up, through testing and implementation in a representative case study. A prototype IT system and reference architecture artefact are developed and used to evaluate the concept qualitatively through broad practitioner focus groups.

Sentiment analysis of the expert opinions is broadly positive and helps to substantiate the proposal’s theoretical suitability in addressing the scalability challenge. Additionally, constructive feedback provides guidance towards this trajectory, highlighting the importance of aligning with existing communities and standards, broadening future research scope to consider further scenarios and prioritisation of efforts to build trust around contracts and data quality.

The novelty of the work is the provision of the reusable LCC reference architecture development methodology.

The concept has the potential to provide LCC capabilities to industry at scale while the artefacts developed herein can be appended to existing LCC standards as implementation guidance to support IT system developers. Furthermore, the developed methodology can be employed in harmonisation efforts between policy and practice.