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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.

This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective.

This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.

The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.

This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.

Artificial intelligence (AI) applications are increasingly used for day-to-day operations in healthcare. Each has a relatively limited scope or task, and several find application in managerial and organizational processes. More and more, AI and machine learning (ML) devices have received US FDA approval in the last decade. This chapter covers the main AI applications in healthcare, with a focus on organizational AI solutions (administrative AI), the main AI developers, their investment and real-world data and case studies in healthcare and other sectors. AI can be applied in resource management and procurement, resource allocation, clinical case management, staff work shift scheduling and handling of emergencies. AI applications are becoming ubiquitous in hospital (e.g. emergency room and operating theatre) and outpatient settings (e.g. ambulatory care and dentistry clinics). Their implementation is expected to bring direct benefits for patient care and satisfaction. This chapter gives a broad definition of AI in healthcare settings, with a focus on administrative applications and their use in case study data.

Multinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.

The key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.

A best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO₂) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.

The research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO₂ emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

In the context of modern urbanization, optimizing resources such as energy, materials, water and labour is no longer solely an environmental concern but a strategic economic necessity. This chapter underscores the vital connection between smart cities and resource efficiency, highlighting sustainable practices as crucial amidst the ever-expanding urban landscape. This chapter commences by demystifying key terms like ‘smart city,’ ‘data analytics,’ ‘artificial intelligence’ and ‘resource efficiency.’ It illuminates how these concepts intertwine and emphasizes their pivotal roles in shaping urban sustainability. Furthermore, this chapter unravels the multifaceted components of smart cities, showcasing their real-world use cases and the techniques of data analytics and artificial intelligence (AI) driving transformative changes. It draws from an extensive body of research, exemplifying how various data analytics techniques have been leveraged in the realm of smart cities. Towards its conclusion, this chapter provides a comprehensive overview of these techniques and their applications, shedding light on their potential to revolutionize resource management in urban environments. In essence, this chapter serves as a valuable compendium of knowledge, offering insights into the critical synergy between smart cities, data analytics, AI and resource efficiency. It underscores the imperative for cities to harness data-driven insights and technological advancements to achieve sustainable and prosperous urban futures.

The study aims to explore the moderation effect of renewable energy consumption (REC) on the relationship between foreign direct investment (FDI) inflows and carbon emission (CO₂). Furthermore, the study investigates the prevalence of rebound effect in energy efficiency for the top five FDI inbound destinations in the Asia-Pacific region.

The study uses a balanced panel data set spanning from 1995 to 2020 obtained from the World Bank Database. This paper used feasible generalized least squares (FGLS) as the primary method, and to ensure the robustness of the findings, this paper used the panels corrected standard errors (PCSE) model.

The findings reveal a negative relationship between FDI and CO₂ emissions and REC and CO₂ emissions. However, the moderation effect of REC on the relationship between FDI inflows and CO₂ emissions is positive, suggesting that when both FDI and REC increase simultaneously, carbon emissions also increase. This study attributes the observed positive moderation effect to the phenomenon known as the rebound effect.

FDI fosters environmental sustainability. Regions’ FDI policies can be guidelines for other nations aiming for similar outcomes. REC reduces CO₂ emissions, underlining renewable energy’s efficacy. However, positive moderation effect of REC on the relationship between FDI and CO₂ emissions highlights the necessity for balanced policies to prevent unintended consequences like the rebound effect.

The originality of this study lies in examining the prevalence of rebound effect in energy efficiency. Prior empirical studies have explored the relationship between REC and carbon emission and established that increased efficiency in renewable energy creates positive environmental and climate externalities. However, it is constrained by rebound effects and this has been ignored by previous studies.

The recent meeting of G-20 world leaders, held in New Delhi, in 2023, highlighted that the physical effect of climate change has considerable macro-economic costs at the national and global levels and they have also pledged to accelerate the clean, sustainable and inclusive energy transition along a variety of pathways. Climate change could pose various emerging risks to the firm’s operational and financial activities, specifically for those which are belonging to the energy sector. Thus, this study aims to investigate the impact of climate risks on the financial performance of select energy companies from G-20 countries.

The study considered 48 energy companies from G-20 countries as the sample for the period of 2017 to 2021. To measure the climate change-related physical risks, the study has considered the ND-GAIN climate vulnerability score and the firm’s financial performance has been measured by return on assets, return on equity, return on capital used and price-to-book ratio. To examine the impact of climate risks on the financial performance of the sample companies, the authors have used pooled ordinary least squares (OLS) and fixed/random effect regression analysis and required data diagnosis tests are also performed.

The empirical results suggested that climate risks negatively impacted the financial performance of the sample companies. The market performances of the firms are also being impacted by the physical climate change. The results of panel data regression analysis also confirmed the robustness of the empirical results derived from the pooled OLS analysis suggesting that firms that operated in a less climate-risky country, financially performed better than the firms that operated in a more climate-risky country.

The paper has significant practical implications like it could be helpful for the policymakers, investors, suppliers, researchers and other stakeholders in developing deeper insights about the impact of climate risks on the energy sectors from an international perspective. This study may also help the policymakers in developing policies for the management of climate risk for the energy sector.

This study adds insights to the existing literature in the area of climate risks and firm’s financial performance. Moreover, this may be the first study that attempts to evaluate the impact of climate risks on the financial performance of select energy companies from the G-20’s perspective.

The positive illiquidity–return relationship (so-called liquidity premium) is a well-established pattern in international developed stock markets. The magnitude of liquidity premium should increase with market illiquidity. Existing studies, however, do not confirm this conjecture with regard to frontier markets. This may result from applying different approaches to the investors' holding period. The paper aims to identify the role of the holding period in shaping the illiquidity–return relationship in emerging and frontier stock markets, which are arguably considered illiquid.

The authors utilise the data on stocks listed on fourteen exchanges in Central and Eastern Europe. The authors regress stock returns on liquidity measures variously transformed to reflect the clientele effect in a liquidity–return relationship.

The authors show that the investors' holding period moderates the illiquidity–return relationship in CEE markets and also show that the liquidity premium in these markets is statistically and economically relevant.

The findings may be of great interest to investors, companies and regulators. Investors and companies should take liquidity into account when making decisions; regulators should employ liquidity-enhancing actions to decrease companies' cost of capital and expand firms' investment opportunities, which will improve growth perspectives for the entire economy.

These findings enrich the understanding of the role that the investors' holding period plays in the illiquidity–return relationship in CEE markets. To the best knowledge, this is the first study which investigates the effect of holding period on liquidity premium in emerging and frontier markets.

There are two main objectives in this study. First, we aim to develop a set of constructs for health task management support (HTMS) features to evaluate which health-related tasks are supported by mobile health application (mHealth app) functions. Second, drawing on innovation resistance theory (IRT), we examine the impacts of the newly developed HTMS dimensions on perceived usefulness, alongside other barrier factors contributing to technology anxiety.

Using a mixed-method research design, this research seeks to develop new measurement scales that reflect how mHealth apps support older adults’ health-related needs based on interviews. Subsequently, data were collected from older adults and exploratory factor analysis was used to confirm the validity of the new scales. Partial least squares structural equation modeling (PLS-SEM) was used to analyze survey data from 602 older adults.

The PLS-SEM results indicated that medical management task support, dietary task support, and exercise task support were positively associated with perceived usefulness, while perceived complexity and dispositional resistance to change were identified as antecedents of technology anxiety. Perceived usefulness and technology anxiety were found to positively and negatively influence adoption intention, respectively.

This study enriches the information systems literature by developing a multidimensional construct that delineates how older adults’ health-related needs can be supported by features of mHealth apps. Drawing on IRT, we complement the existing literature on resistance to innovation by systematically examining the impact of five types of barriers on technology anxiety.

This paper aims to investigate the impact of geopolitical events of the Russia–Ukraine conflict on the stock market volatility of G20 countries. Furthermore, the paper also investigates the possible reasons for any similarities or differences in the results of the three sectors.

This paper measures the impact of the stock market sectoral index price (SIP) by using the daily closing price as a dependent variable. In addition, this study uses three independent variables: geopolitical risk (GPR), commodity price (CP) and foreign exchange rate (FER). Seventeen countries from the G20 are analyzed using a daily timeframe from September 2021 to August 2022 (before and during the Russian invasion).

The results revealed that FER, CP and GPR all affect SIP, but the level of significance and positive/negative signs vary in all three sectors. The positive FER affects SIP in all sectors, while the negative CP and GPR significantly impact SIP in the energy and transportation sectors.

This study’s research model is more suited for transportation and energy than consumer goods. Future researchers can enhance the research model for the consumer goods sector by incorporating additional variables to understand their relationship with SIP better.

This study explores the impact of the Russia–Ukraine conflict on the stock market in G20 countries, focusing on the top three most affected sectors.