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The Women in Leadership Development (WLD) Initiative is a year-long, multifaceted co-curricular leadership development opportunity created to support the unique developmental needs of emerging women leaders. WLD was intentionally designed around the context of second-generation gender bias with a firm grounding of research and theory on gender and leadership.

Organized around three leadership pathways – leadership training, leadership coaching, and leadership support networks – WLD brings together the best practices of leadership development in combination with feminist pedagogy and critical perspectives to foster meaningful and impactful development of women leaders.

This paper describes the design of the initiative and how each leadership pathway supports the leadership development journey for emerging women leaders. It provides a model that is impactful as well as foundational, for undergraduate women’s leadership development.

Women leaders, in particular, can benefit from leadership development that takes gender into account (DeFrank-Cole & Tan, 2022a; Ely et al., 2011).

This study aims to present a diagnosis method to inspect the structure health for aging transport aircraft based on the postflight data in severe clear-air turbulence at transonic flight. The purpose of this method development is to assist certificate holder of aircraft maintenance factory as a complementary tool for the structural maintenance program to ensure that the transport aircraft fits airworthiness standards.

In this study, the numerical approach to analyze the characteristics of flight dynamic and static aeroelasticity for two four-jet transport aircraft will be presented. One of these two four-jet transport aircraft is an aging one. Another one is used to demonstrate the order of magnitude of the static aeroelastic behaviors. The nonlinear unsteady aerodynamic models are established through flight data mining and the fuzzy-logic modeling technique based on postflight data. The first and second derivatives of flight dynamic and static aeroelastic behaviors, respectively, are then estimated by using these aerodynamic models.

Although the highest dynamic pressure of aging aircraft is lower, the highest absolute value of static aeroelastic effects response to the wing of aging aircraft is about 3.05 times larger than normal one; the magnitude variations of angles of attack are similar for both aircrafts; the highest absolute value of the static aeroelastic effects response to the empennage of aging aircraft is about 29.67 times larger than normal one in severe clear-air turbulence. The stabilizer of aging aircraft has irregular deviations with obvious jackscrew assembly problems, as found in this study.

A lack of the measurement data of vertical wind speed sensor on board to verify the estimated values of damping term is one of the research limitations of this study. This research involved potential problem monitoring of structure health for transport aircraft in different weights, different sizes and different service years. In the future research, one can consider more structural integrity issues for other types of aircraft.

It can be realized from this study that the structure of aging transport aircraft may have potential safety threat. Therefore, when the airline managed aging transport aircraft, it ought to be conducted comprehensive and in-depth inspections to reduce such safety risks and establish a complete set of safety early warning measures to deal with the potential problem of aircraft aging.

It can be realized that the structure of aging transport aircraft has potential safety threat. The airline managed aging transport aircraft; it should conduct comprehensive and in-depth inspections to reduce safety risks and establish a complete set of safety early warning measures.

This method can be used to assist airlines to monitor aging transport aircraft as a complementary tool of structural maintenance program to improve aviation safety, operation and operational efficiency.

For decades, the fast population growth worldwide was interrelated with the adopted rapid lifestyle behavior that relies on the extensive use of fossil fuels. This primary energy source has caused various urban and environmental impacts, such as global warming, air pollution, and so forth. Consequently, the identified circumstance issues have caused many health, social, and economic hindering effects for global citizens. It poses an existential threat to humanity and the global earth's ecosystem. The alarming levels of urban pollution emissions are putting enormous challenges to the related stakeholders (governments, businesses, investors, automakers, and citizens) to admit the need to decarbonize the global economy and reach sustainable development goals (SDGs) for cleaner and smarter cities across borders. As such, a vital part of a smart city is the transport sector. The road transport sector, precisely, is one of the primary consumers of fossil fuels that contribute to high carbon dioxide emissions. Accordingly, it is essential to adopt novel and sustainable urban transport solutions and promote the achievement of the SDG's eleventh goal for sustainable cities and communities. This chapter provides insight into the present global energy situation with particular attention to the road transport sector. Indeed, it highlights different emerging technologies for a sustainable and smart urban mobility future that will mitigate the environmental situation thanks to the development of drive and internet telecommunication technologies. Furthermore, we aim in this chapter to study the international progress of the transition project using the Political, Economic, Social, Technological, Environmental, and Legal (PESTEL) analysis methodology. This study is to pinpoint opportunities for project development and the challenges that set back its evolution.

This study aims to examine the relationships between functional value (quality and price), social value (extrinsic and intrinsic), emotional value and attitude toward a brand, as well as the direct relationship between attitude toward a brand and the purchase intention of tires. This research also explores the moderating effect of social influence on the relationship between attitude toward a brand and purchase intention.

A conceptual model based on literature is developed and tested using an online survey, with a sample of 760 active drivers gathered through purposive sample judgment. The data were analyzed using structural equation modeling with AMOS 28 and Hayes Process Macro 4.

The results demonstrate that extrinsic social value has a positive direct relationship with attitude toward brands. The findings also indicate that intrinsic social value has a positive influence on attitudes toward brands. Attitude toward a brand is found to have a positive direct relationship with purchase intention.

This research extends the existing literature on consumption values and offers insights into the specific values that influence attitudes toward tire brands as well as purchase intention. The findings provide insights to tire businesses in values that they could focus on when developing strategies to increase positive brand attitude and purchase intention.

The purpose of this study is to identify the consumption intention of Baba Nyonya Peranakan ethnic food among the youth generation by examining the effect of media influence and curiosity.

A causal study through the quantitative method is applied. This study focused on the youth generation, and a total of 219 valid responses were used for hypothesis testing using Statistical Package for Social Sciences and SmartPLS software. Purposive sampling, under non-probability sampling, was used. The data were collected through an online survey. The survey link was disseminated mainly on social media groups.

All of the hypotheses were found to be statistically significant. Media influence was found to directly and indirectly influence the consumption intention of Baba Nyonya ethnic food through attitude and curiosity. The involved stakeholders could also be more alert and improve the strategic planning based on the research finding that can stimulate more interest among consumers, benefiting the national gastronomy heritage tourism development.

The integration of biculturalism is rooted in knowledge about food, media coverage about food and food for social events that influence the formation of national food identity. Baba Nyonya food brings a unique identity with mixed heritage and culture, combined ingredients and recipes between two majority races of Malay and Chinese. However, exposure to ethnic food is lacking and causes the youth generation to unaware and not interested to try ethnic food. This study initiates to examine the effect of media influence and curiosity as food promotion via social media potentially to influence and lead to increased consumption of ethnic food.

Pervasive analytics act as a prominent role in computer-aided prediction of non-communicating diseases. In the early stage, arrhythmia diagnosis detection helps prevent the cause of death suddenly owing to heart failure or heart stroke. The arrhythmia scope can be identified by electrocardiogram (ECG) report.

The ECG report has been used extensively by several clinical experts. However, diagnosis accuracy has been dependent on clinical experience. For the prediction methods of computer-aided heart disease, both accuracy and sensitivity metrics play a remarkable part. Hence, the existing research contributions have optimized the machine-learning approaches to have a great significance in computer-aided methods, which perform predictive analysis of arrhythmia detection.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

This paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.

With daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.

The empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.

The MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.

A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

Engineering composite laminates/structures are usually subjected to complex and variable loads, which result in interlayer delamination damage. However, damaged laminate may cause the whole structure to fail before reaching the design level. Therefore, the purpose of this paper is to develop an equivalent model to effectively evaluate compressive residual strength.

In this paper, taking carbon fiber reinforced composite T300/69 specimens as the study object, first, the compressive residual strength under different impact energy is obtained. Then, zero-thickness cohesive elements, Hashin failure criteria and Camanho nonlinear degradation scheme are used to simulate the full-process simulation for compression after edge impact (CAEI). Lastly, based on an improved Whitney–Nuismer criterion, the equation of edge hole stress distribution, characteristic length and compressive residual strength is used to verify the correctness of the equivalent model.

An equivalent relationship between the compressive residual strength of damaged laminates and laminates with edge hole is established. For T300/69 laminates with a thickness of 2.4 mm, the compressive residual strength after damage under an impact energy of 3 J is equivalent to that when the hole aperture R = 2.25 mm and the hole aperture R = 9.18 mm when impact energy is 6 J. Besides, the relationship under the same size and different thickness is obtained.

The value of this study is to provide a reference for the equivalent behavior of damaged laminates. An equivalent model proposed in this paper will contribute to the research of compressive residual strength and provide a theoretical basis for practical engineering application.