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Temperature anomalies in the upper troposphere have become a reality as a result of global warming, which has a noticeable impact on aircraft performance. The purpose of this study is to investigate the total air temperature (TAT) anomaly observed during the cruise level and its impact on engine parameter variations.

Empirical methodology is used in this study, and it is based on measurements and observations of anomalous phenomena on the tropopause. The primary data were taken from the Boeing 747-8F's enhanced flight data recorder, which refers to the quantitative method, while the qualitative method is based on a literature review and interviews. The GEnx Integrated Vehicle Health Management system was used for the study's evaluation of engine performance to support the complete range of operational priorities throughout the entire engine lifecycle.

The study's findings indicate that TAT and SAT anomalies, which occur between 270- and 320-feet flight level, have a substantial impact on aircraft performance at cruise altitude and, as a result, on engine parameters, specifically an increase in fuel consumption and engine exhaust gas temperature values. The TAT and Ram Rise anomalies were the focus of the atmospheric deviations, which were assessed as major departures from the International Civil Aviation Organizations–defined International Standard Atmosphere, which is obvious on a positive tendency and so goes against the norms.

Necessary fixed flight parameters gathered from the aircraft's enhanced airborne flight recorder (EAFR) via Aeronautical Radio Incorporated (ARINC) 664 Part 7 at a certain velocity and altitude interfacing with the diagnostic program direct parameter display (DPD), allow for analysis of aircraft performance in a real-time frame. Thus, processed data transmits to the ground maintenance infrastructure for future evaluation and for proper maintenance solutions.

A real-time analysis of aircraft performance is possible using the diagnostic program DPD in conjunction with necessary fixed flight parameters obtained from the aircraft's EAFR via ARINC 664 Part 7 at a specific speed and altitude. Thus, processed data is transmitted to the ground infrastructure for maintenance to be evaluated in the future and to find the best maintenance fixes.

Construction industry faces challenges in making objective decisions due to monetary value attached to quality. Among various quality management techniques available, cost of quality (COQ) is one such method used to address the concern. However, the absence of measurable COQ factors to monitor quality costs hampers the implementation of COQ framework in the construction industry. Therefore, this study aims to identify COQ factors focused on visible factors (VF) and hidden factors (HF) and the current requirements to achieve it.

This study is based on Preferred Reporting Items for Systematic Review and Meta-Analyses protocol guidelines. The present study identified 57 articles published between 1992 and 2023 in peer-reviewed journals.

The findings reveal 22 factors, which are grouped into four categories based on COQ. Through systematic review, the authors observed limited methodological and theoretical diversity. In fact, there are no quantitative frameworks to calculate COQ. The study, therefore, developed a framework comprising four major routes/paths of COQ factors within the framework.

The COQ routes developed through this study will enable the practitioners to meticulously categorise VF and HF, facilitating quantifying of quality throughout the lifecycle of project, which is currently absent from the existing quality assurance/quality control (QA/QC) approach. In addition, these COQ routes stand as essential construction strategies, significantly enhancing outcomes related to time, cost, quality, sustainability and fostering closer relationships within project frameworks.

The current study contributes significantly to the existing body of knowledge by developing various COQ routes and proposing future research directions to address gaps in the literature.

This study aims to assess the effectiveness of active teaching methodologies, namely, problem-oriented learning and the case method, to develop sustainability competencies. It also analyses the advantages and challenges for teachers when implementing the sustainable development goals (SDGs) in eight undergraduate and postgraduate degrees within the framework of a cross-departmental collaboration.

A mixed research methodology was used: a quantitative study to assess the levels of acquisition of sustainability and research competencies and the potential correlation between them, as well as a mixed study of the advantages and challenges for the teachers participating in the cross-departmental initiative. Curriculum content linked to the SDGs was worked on. Active teaching methodologies and a competency assessment rubric were used as curriculum implementation strategies in the eight courses involved.

Active teaching methodologies are suitable to implement the SDGs in university teaching and to develop both sustainability and research competencies. A synergic effect is observed between them. Coordinated work between teachers of different subjects in several degrees contributes to developing a culture of sustainability at the university.

Although the collaboration between teachers from different disciplines was successful, this study did not promote interdisciplinary projects among students from different degrees. This promises to be highly valuable for future research.

Students can become present and future leaders in achieving the SDGs. This approach can be replicated in other educational institutions.

This study bridges the gap between theoretical recommendations and the practical implementation of the SDGs in undergraduate and postgraduate degrees.

Coordinated work between teachers of different subjects in different degrees contributes to the development of a culture of sustainability at the university.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

This research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.

A feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.

The findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.

Based on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.

The present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

A major obstacle regarding the measurement of an organization's sustainability and accountability in the space economy is defining the context and boundaries of commercial activity in outer space. Here, we introduce an ecosystem framework to address this obstacle. We utilize this framework to analyze the space mining sector. Our ecosystem framework sets the space mining sector's boundaries and helps a firm identify key stakeholders, activities, policies, norms and common pool resources in that sector and the interactions between them; a significant step in structuring how to measure space sustainability and accountability.

Borrowing theories and perspectives from a wide range of academic fields, this paper conducts a comprehensive context analysis of the space mining ecosystem.

Using our ecosystem framework to define the context and set boundaries for the space mining sector allowed us to identify sustainability-related issues in the sector and offer roadmaps to develop sustainability measures and standards.

To the best of the authors’ knowledge, this is one of the first papers to introduce a framework to define boundaries in the global space economy and provides a tool to understand, measure and evaluate the space mining sector's environmental, social and economic issues.

The purpose of this paper is to determine if companies in the modular and offsite construction (MOC) industry are agile or not and its level of application for agility principles, which allows for quick responses to the increasingly dynamic nature of industry environments.

This paper proposes an agility assessment framework for MOC that uses 48 assessment attributes organized into four categories: metrics, drivers, enablers and capabilities. A questionnaire approach was used to disseminate the framework globally in 19 countries and synthesize its relevance to the MOC industry. The questionnaire had 55 complete responses, majority of respondents work in managerial positions for MOC manufacturing facilities and onsite general contractors.

It was found that the lowest metric score for adapting to change was for cost since controlling cost would be difficult for any changes required after the design freeze stage. The top agility driver was found to be the need to respond to the wide variety of customer expectations, while the lowest driver was the existence of competing priorities. The top agility enabler was vendor partnership, which can be related to current postpandemic supply chain disruptions. Regarding technological capabilities, Europe and the USA acquired better scores compared to Asia, Latin America and Africa.

This study contributes to the MOC body of knowledge by creating an agility assessment tool for MOC firms to analyze their agile approach and environment, identifying the preliminary importance of agility assessment attributes and determining significant agile differences between the main MOC industry groups.

This study aims to investigate the structural characteristics, spatial evolution paths and internal driving mechanisms of the knowledge transfer (KT) network in China’s patent-intensive industries (PIIs). The authors' goal is to provide valuable insights to inform policy-making that fosters the development of relevant industries. The authors also aim to offer a fresh perspective for future spatiotemporal studies on industrial KT and innovation networks.

In this study, the authors analyze the patent transfer (PT) data of listed companies in China’s information and communication technology (ICT) industry, spanning from 2010 to 2021. The authors use social network analysis and the quadratic assignment procedure (QAP) method to explore the problem of China’s PIIs KT from the perspectives of technical characteristics evolution, network and spatial evolution and internal driving mechanisms.

The results indicate that the knowledge fields involved in the PT of China’s ICT industry primarily focus on digital information transmission technology. From 2010 to 2021, the scale of the ICT industry’s KT network expanded rapidly. However, the polarization of industrial knowledge distribution is becoming more serious. QAP regression analysis shows that economic proximity and geographical proximity do not affect KT activities. The similarity of knowledge application capacity, innovation capacity and technology demand categories in various regions has a certain degree of impact on KT in the ICT industry.

The current research on PIIs mainly focuses on measuring economic contributions and innovation efficiency, but less on KT in PIIs. This study explores KT in PIIs from the perspectives of technological characteristics, network and spatial evolution. The authors propose a theoretical framework to understand the internal driving mechanisms of industrial KT networks.

Following the introduction of user fee for interpreting in Danish health care, a considerable decrease in interpreter services has been shown. This study aims to explore the experiences of language minority patients with health-care encounters when an interpreter was needed but not present.

Semistructured, in-depth interviews were conducted with 13 language minority patients with limited Danish proficiency. All interviews were conducted with interpreters in the participants’ native language. Data were analyzed using an inductive thematic approach.

Most participants experienced communication difficulties and difficulties participating actively in their own health care. The experience of unresolved language barriers led to a high degree of uncertainty and left the participants with unanswered health concerns. Participants expressed a reluctance to seek health care, which consequently limited the utilization of health care services.

Although the findings only represent a small sample of patients, the results still reveal major challenges that minority-language patients encounter when seeking health care. Future studies should explore, if the intention of the law is met through the user fees.

Despite having the same entitlements as native Danish-speaking patients, minority-language patients experienced difficulties accessing and using health care services due to the user fee and unresolved language barriers. The study elucidates patient perspectives and points to important ways of improving the quality of health care.

To the best of the authors’ knowledge, no investigation into the communicative consequences of the introduction of the user fee for interpreting services exists. Thus, this study seeks to address that gap.

This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.