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Necessary conditions represent the factors that cannot be compensated but must be present to aim the desired outcome; if a necessary condition is absent, the outcome will not exist. This logic of necessity causality differs from the conventional logic that has been evaluated by the methods drawing the lines “through the middle of the data” (e.g., regression and SEM). The authors argue that the empirical investigation of necessity causality has been largely ignored in hospitality and tourism literature although the notion of necessary causes for achieving certain outcomes is widespread throughout the studies. Thus, the authors introduce “necessary condition analysis” (NCA) as a suitable analytical method to identify necessary conditions in hospitality and tourism research. This chapter provides details on the underlying logic, key advantages, and an illustrative example of NCA. The chapter concludes by offering a few recommendations for future NCA applications in hospitality and tourism research.

The purpose of this work is to study the capability of heuristic algorithms like genetic algorithm to estimate the electron transport parameters of the Gallium Arsenide (GaAs). Also, the paper provides a simple but complete electron mobility model for the GaAs based on the genetic algorithm that can be suitable for use in simulation, optimization and design of GaAs‐based electronic and optoelectronic devices.

The genetic algorithm as a powerful heuristic optimization technique is used to approximate the electron transport parameters during the model development.

The capability of the model to approximate the electron transport properties of Gallium Arsenide is tested using experimental and Monte Carlo data. Results show that the genetic algorithm based model can provide a reliable estimate of the electron mobility in Gallium Arsenide for a wide range of temperatures, concentrations and electric fields. Based on the obtained results, this paper shows that the genetic algorithm can be a useful tool for the estimation of the transport parameters of semiconductors.

For the first time, the genetic algorithm is used to calculate the electron transport parameters in Gallium Arsenide. A complete electron mobility model for a wide range of temperatures, doping concentrations, compensation ratios and electric fields is developed.

Due to the limitations of conventional urban planning, it is essential to develop novel techniques of urban futruing. This paper aimed to use the scenario technique to create four plausible narratives of the future of Isfahan. Also, the authors described the problems of city foresight in the Global South.

This paper chronicles the Schwartzian steps taken to build explorative scenarios of Isfahan City in Iran in 2040. After using a STEEPV (Social, Technological, Environmental, Economic, Political, Value) analysis, the authors prioritized the collected variables by combining influence diagrams, the iceberg metaphor and an expert-based survey. Once the key uncertainties were derived, four scenarios were developed and discussed.

Through thematic analysis of the official visions of Isfahan’s future and the juxtaposition of these narratives with insight yielded in the scenario-development process, the paper concludes that the Northernness of the prevailing urban imaginaries, uncritical mimetic benchmarking, depoliticization of urban futures and the decorative reductionistic visions colonize urban futures in Isfahan/Iran. Critical/deconstructive city foresight and application of discomfort/ignorance criteria in the generation of scenarios can improve the rigor and quality of city foresight in the Global South.

The application of city foresight in the Global South has been limited. The paper is a step toward bridging this gap and providing some recommendations on how city foresight in the Global South might differ from its counterparts in the Global North.

Numerous scales have been developed and utilized in the tourism and hospitality field, yet, their psychometric properties have not been systematically reviewed and evaluated. This gap compromises researchers' ability to develop better measures and improve measurement decisions. In this current study, 56 scales were identified and evaluated in terms of their psychometric properties. It was found that most scales were imperfect in measuring tourism and hospitality domains, and most scales did not provide explicit information about the scale development procedures that were adopted. The scale development procedure and psychometric properties of the reviewed scales are summarized, evaluated, and recommendations are made for future tourism and hospitality scale development.

The purpose of the research was to explore the critical success factors (CSFs) that enable knowledge-sharing behaviors (KSB) among students and to determine if there are differences in the CSFs that enable KSB among undergraduate, graduate or professional course students.

The study is based on primary data collection, surveying 281 students using a multiple-choice Likert-style scale.

Researchers found that factors such as knowledge culture (KC), perceived rewards (PR), quality of technology (QT), trust, networking ties (NT) and competitiveness were critical for KSB. In contrast, collective culture, subjective norms (SN) and the ability to share did not significantly impact KSB. The CSFs common among students at different educational levels were KC, PR, trust and competitiveness. KC was more important for undergraduates and professional course students but less significant for graduate students. Additionally, NT and the QT did not impact KSB among undergraduates.

While several studies have explored CSFs for knowledge-sharing behaviors among students at specific educational levels, there is a lack of comparative empirical research examining KSB and the CSFs affecting students at different educational stages. This study fills a gap in the literature by adopting a comparative approach and presenting common success factors for knowledge sharing among students at various educational levels.

To prevent the coil from burning or getting damaged, it is necessary to estimate the duration of its operation as long as its temperature does not exceed the permissible limit. This paper aims to investigate the effect of switching on the thermal behavior of impregnated and nonimpregnated windings. Also, the safe operating time for each winding is determined.

The power loss of the winding is expressed as a function of the winding specifications. Using homogenization techniques, the equivalent thermal properties for the homogenized winding are calculated and used in a proposed thermal equivalent circuit for winding modeling and analysis. The validity and accuracy of the proposed model are determined by comparing its analysis results and simulation and measurement results.

The results show that copper windings have better thermal behavior and lower temperature compared to aluminum windings. On the other hand, by impregnating or increasing the packing factor of the winding, the thermal behavior is improved. Also, by choosing the right duty cycle for the winding current source, it is possible to prevent the burning or damage of the winding and increase its lifespan. Comparing the measurement results with the analysis results shows that the proposed equivalent circuit has an error of less than 4% in the calculation of the winding center temperature.

In this paper, the effect of temperature on the electrical resistance of the coil is ignored. Also, rectangular wires were not investigated. Research in these topics are considered as future work.

By calculating the thermal time constant of the winding, its safe operation time can be calculated so that its temperature does not exceed the tolerable value (150 °C). The proposed method analyzes both impregnated and nonimpregnated windings with various schemes. It investigates the effects of switching on their thermal behavior. Additionally, it determines the safe operating time for each type of winding.

In recent years, the use of structural equation modeling (SEM) has become widespread in tourism and hospitality research. Because there are two different approaches to SEM (i.e., covariance-based SEM and variance-based, partial least squares SEM), this brings challenges for researchers about which SEM to use and what to report in each SEM approach. Therefore, the purpose of this chapter is to discuss the differences between CB-SEM and PLS-SEM and to provide comprehensive guidelines for researchers on how to apply each SEM. Within this context, the authors first briefly summarize the fundamentals and advantages of using SEM. Then, the authors explain in detail the major issues that should be considered when selecting between CB-SEM and PLS-SEM. Finally, to ensure rigorous research practices, the authors provide step-by-step guidelines for the application of both CB-SEM and PLS-SEM.

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.