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Magnetic hysteresis holds significant technical and physical importance in the design of electromagnetic components. Despite extensive research in this area, modeling magnetic hysteresis remains a challenging task that is yet to be fully resolved. The purpose of this paper is to study vector hysteresis play models for anisotropic ferromagnetic materials in a physical, thermodynamical approach.

In this work, hysteresis play models are implemented to interpret magnetic properties, drawing upon classical rate-independent plasticity principles derived from continuum mechanics theory. By conducting qualitative and quantitative verification and validation, various aspects of ferromagnetic vector hysteresis were thoroughly examined. By directly incorporating the hysteresis play models into the primal formulations using fixed point method, the proposed model is validated with measurements in a finite element (FE) environments.

The proposed vector hysteresis play model is verified with fundamental properties of hysteresis effects. Numerical analysis is performed in an FE environment. Measured data from a rotational single sheet tester (RSST) are validated to the simulated results.

The results of this work demonstrates that the essential properties of the hysteresis effects by electrical steel sheets can be represented by the proposed vector hysteresis play models. By incorporation of hysteresis play models into the weak formulations of the magnetostatic problem in the h-based magnetic scalar potential form, magnetic properties of electrical steel sheets can be locally analyzed and represented.

While most West European nations were formed around pre-existing entities that could be called “countries” before the modern age, this was not the case in the Middle East. Some entities, like Egypt, did have a clear political and cultural identity before colonialism, others, like Algeria, did not. This chapter discusses the four states of the Maghreb: Morocco, Algeria, Tunisia and Libya, through the perspective of “country creation” going into and coming out of colonial rule. We can see here two “models” of fairly similar types of historical development, one showing a gradual process through a protectorate period to relatively stable modern nations, another through violent conquest and direct colonization ending in violent liberation and military and wealthy but fragile states. The article asks whether these models for the history of country creation and the presence or absence of pre-colonial identities can help explain the modern history and nature of these states in the Arab Spring and the years thereafter. Then, a more tentative attempt is made to apply these models to two countries of the Arab east, Syria and Iraq. While local variations ensure that no model can be transferred directly, it can show the importance of studying the historical factors that go into the transition from geographical region to a country with people that can form the basis of a nation.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

Effective leadership is critical in driving innovation and success in organisations, particularly in today’s rapidly changing environment. However, achieving effective leadership at all levels of the organisation can be challenging. This chapter argues that understanding how the brain functions is essential for innovation leaders to achieve positive results and higher rates of success in their projects. By analysing relevant research on neuroscientific functioning patterns and developing interventions based on these foundations, this chapter establishes that the brain’s self-organising ability and cognitive processing systems offer valuable insights for effective innovation leadership. Based on neuroscientific evidence this chapter concludes that effective innovation leadership should focus on inviting others to engaged co-creation, rather than directing others to perform specific tasks as if they were ‘a prolonged arm’. Additionally, effective innovation leadership integrates insights from information processing in the brain by providing behavioural-oriented impulses that activate the brain, enabling individuals to maintain focus, restore motivation or emotional stability, enhance mood and confidence, and increase cognitive flexibility. Evidence-based interventions range from structured breaks to powernapping and walking. The importance of self-leadership is stressed throughout the chapter. By deriving solutions from an understanding of how the brain functions, interventions that may have been known for a long time can become evidence-based and optimised for use in organisations. Future research could explore the intersection of neuro- and behavioural science with leadership to further innovate organisational principles.

This chapter examines the University of Connecticut (UConn) Neag School of Education's efforts to improve the recruitment of students of colour through an Early College Experience (ECE) Programme. During the pandemic, the School of Education and the ECE Programme collaborated to train and certify high school teachers to instruct the UConn's lower level undergraduate courses. The programme exposed many students of colour to teaching as a career.

Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours.

This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed.

Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk.

The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.

Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

The case was written using information and data from secondary sources. It describes real people and the situations experienced by them. It does not use any fictitious names, scenarios or organizations.

The case study “Melanie Perkins: Poised to Redesign Canva from Tech Unicorn to Tech Giant?” describes the entrepreneurship journey of Melanie Perkins (she) (Perkins), the CEO of Australia-based tech unicorn and graphic design company, Canva Pty Ltd. (Canva). The case starts with a brief look into Perkins’ background and documents her entrepreneurial spirit, which, at the age of 19, led her to identify a hitherto unserved market (yearbooks) in the graphic design industry and offer an online design system through her venture, Fusion Books (Fusion). Fusion was completely bootstrapped and became a runaway success within five years. That encouraged her to envision setting up a one-stop-shop design site that would make design accessible to everyone.

However, when she tried to raise funds, Perkins encountered multiple rejections from venture capitalists. She persevered and continually refined her strategy. Eventually, she managed to raise venture capital funding and establish her design startup, Canva, in 2013. Canva then went on to disrupt the graphic design industry. The case describes in detail the reasons for Canva’s success, which went on to be one of the few profitable unicorn start-ups. The case also throws light on how Perkins used Canva as a tool to change society with her two-step plan. Despite its market success, Canva faced heavy competition in the design and publishing space from well-established players. Can Perkins challenge the competition and ultimately make Canva a software giant in the future?

The case is intended for use in teaching the subjects “Entrepreneurship Development,” “Business Strategy,” “Leadership Skills and Change Management” and “Positive Psychology for Managers” in both graduate and post-graduate programs.

This study aims to explore the determinants of the green purchase behaviour (GPB) of environment-friendly and energy-efficient electronic products market. It specifically examines the moderating effect of consumers’ moral identity on the relationships between the consumption values and GPB of environment-friendly and energy-efficient electronic products market. It also examines the direct relationship between consumption values and GPB.

In this study, the theory of consumption values is combined with the moral identity of consumers. A structured questionnaire mall-intercept survey was used to collect data from 396 respondents, which was subsequently processed using the smart PLS software for partial least square structural equation modelling analysis.

Findings reveal that functional value, social value, conditional value and epistemic value are the significant predictors of GPB of the environment-friendly and energy-efficient electronic products market. The moral identity of consumers also appears to positively moderate the relationships between functional, emotional and conditional values and the GPB.

The energy efficiency of electronic products is included in this study as an additional feature of functional value, while government support and business promotional initiatives are incorporated as the new elements of conditional value. Therefore, the inclusion and evaluation of the moral identity of consumers, alongside new elements of functional and conditional values in the theory of consumption values, could be considered a significant theoretical addition. The study uncovered certain customer insights that could help accelerate the adoption of green electronic products, which may result in better energy savings, reduced carbon emissions and environmental safety.