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Health risks associated with coronavirus disease 2019 (COVID-19) have severely affected the financial stability of airline companies globally. Recapturing financial stability following this crisis depends heavily on these companies’ ability to attain efficient and productive operations. This study uses several empirical approaches to examine key factors contributing to carriers sustaining high productivity prior to, during and after a major recession. Findings suggest, regardless of economic conditions, that social distancing which requires airline companies in the Asia Pacific region to fly with a significant percentage of unfilled seats weakens the performance of those companies. Furthermore, efficient operations do not guarantee the avoidance of productivity declines, especially during a recession.

The years following the 9/11/2001 terrorists attacks saw a marked increase in community and hospital emergency preparedness, from communications across community networks, development of policies and procedures, to attainment and training in the use of biological warfare resources. Regular drills ensured emergency and health care personnel were trained and prepared to address the next large-scale crisis, especially from terrorist and bioterrorist attacks. This chapter looks at some of the more familiar global health issues over the past two decades and the lessons learned from hospital responses to inform hospital management in preparation for future incidents.

This study is a narrative review of the literature related to lessons learned from four major events in the time period from 2002 to 2023 – SARS, MERS, Ebola, and COVID-19.

The initial search yielded 25,913 articles; 57 articles were selected for inclusion in the study.

Comparison of key issues and lessons learned among the four major events described in this article – SARS, MERS, Ebola, and COVID-19 – highlight that several lessons are “relearned” with each event. Other key issues, such as supply shortages, staffing availability, and hospital capacity to simultaneously provide care to noninfectious patients came to the forefront during the COVID-19 pandemic. A primary, ongoing concern for hospitals is how to maintain their preparedness given competing priorities, resources, and staff time. This concern remains post-COVID-19.

We theorize the critical role of angel investors' affective experiences and first impressions in the context of entrepreneurial finance. We develop a model and propositions to illustrate why angel investors make the decision to continue screening, thus explaining why certain investment proposals make it, while others do not.

Drawing on affective events theory and the literature on affective experiences, we theorize how the perceptions of pitches that trigger positive or/and negative physiological arousal, short-lived emotions, and associated thoughts are different, thus allowing us to build new theory of how these different experiences can influence the outcome of the evaluation process in the initial screening stage.

Our model suggests that the initial evaluation unfolds in five stages: perception of an entrepreneurial pitch, physiological arousal, emotions, first impression, and a decision to continue screening. When different manifestations of physiological arousal and subsequent emotions set the tone of first impressions, they can be either a positive, negative, or mixed experience. While positive and mixed first impression can lead to selection, negative first impression can lead to rejection.

We illustrate what is of value for angel investors when they look for new investments, and why certain entrepreneurial pitches lead to the decision to continue screening, while others do not. We propose that what angel investors feel is particularly important in situations where they are not yet making the ultimate decision to invest money but are involved in decisions about whether to continue to spend time to investigate the investment proposal.

This paper aims to explore the relationship between prudence in risk attitudes and patience of time preference of Chinese apple growers regarding off-farm cold storage of production and marketing in non-harvest seasons. The authors also consider the effect of farmer participation in cooperative-like organizations known as Farm Bases (FBs).

The authors use multiple list methods and elicitation strategies to measure Chinese apple farmers' risk attitudes and time preferences. Because these farmers can either sell their apples immediately to supermarkets or intermediaries or place them in storage, the authors assess correlations between their storage decisions and their preferences regarding risk and time. The authors also differentiate risks involving gains and losses and empirically examine individual risk attitudes in different scenarios.

Marketing decisions are moderately associated with risk attitudes but not time preference. Farmers with memberships in local farmer cooperatives are likely to speculate more in cold storage. Thus, risk aversion behavioral and psychological motives affect farmers' decision-making of cold storage and intertemporal marketing activities. However, membership in cooperatives does not always result in improved income and welfare for farmers.

The research confirms that behavioral factors may strongly drive vulnerable smallholder farmers to speculate into storage even under seasonal and uncertain marketing volatility. There is the need to think deeper about the rationale of promoting cooperatives and other agricultural forms, because imposing these without careful consideration can have negative impacts.

Do risk and time preferences affect the decision of farmers to utilize storage facilities? This question is important because it is not clear if and how risk preferences affect the tradeoff between consuming today and saving for tomorrow, especially for farmers in developing countries.

This study examined the factors influencing willingness-to-repurchase for each class of airline service, and integrate the constructs of service quality, satisfaction and willingness-to-repurchase which were rooted on Engel-Kollat-Blackwell (EKB) model. The study focuses on the domestic and international arrival of passengers at Murtala Muhammed International Airport in Lagos and Nnamdi Azikwe International Airport in Abuja. Information was gathered from domestic and foreign passengers who had post-purchase experience and had used the airline's services more than once. The survey data were obtained concurrently from arrival passengers at two major international airports using an electronic questionnaire through random and purposive sampling techniques. The data was analysed using the ordinal logit model and structural equation model. From the 606 respondents, 524 responses were received but 489 responses were valid for data analysis and reporting and were obtained mostly from economy and business class passengers. The study found that the quality of seat pitch, allowance of 30 kg luggage permission, availability of online check-in 24 hours before the departing flight, quality of space for legroom between seats, and the quality of seats that can be converted into a fully flatbed are the major service factors influencing willingness-to-repurchase economy and business class tickets. Also, it was found that passengers' willingness to repurchase is influenced majorly by service quality, but not necessarily influenced by satisfaction. These results reflect the passengers' consciousness of COVID-19 because the study was conducted during the heat of COVID-19 pandemic. Recommendations were suggested for airline management based on each class.

This paper aims to present a literature review of remote monitoring systems for water infrastructure in the Global South.

Following initial scoping searches, further examination was made of key remote monitoring technologies for water infrastructure in the Global South. A standard literature search methodology was adopted to examine these monitoring technologies and their respective deployments. This hierarchical approach prioritised “peer-reviewed” articles, followed by “scholarly” publications, then “credible” information sources and, finally, “other” relevant materials. The first two search phases were conducted using academic search services (e.g. Scopus and Google Scholar). In the third and fourth phases, Web searches were carried out on various stakeholders, including manufacturers, governmental agencies and non-governmental organisations/charities associated with Water, Sanitation and Hygiene (WASH) in the Global South.

This exercise expands the number of monitoring technologies considered in comparison to earlier review publications. Similarly, preceding reviews have largely focused upon monitoring applications in sub-Saharan Africa (SSA). This paper explores opportunities in other geographical regions and highlights India as a significant potential market for these tools.

This review predominantly focuses upon information/data currently available in the public domain.

Remote monitoring technologies enable the rapid detection of broken water pumps. Broken water infrastructure significantly impacts many vulnerable communities, often leading to the use of less protected water sources and increased exposure to water-related diseases. Further to these public health impacts, there are additional economic disadvantages for these user communities.

This literature review has sought to address some key technological omissions and to widen the geographical scope associated with previous investigations.

This paper explores the experiences of Lebanese and Ukrainian diasporans on the Canadian island of Newfoundland by using the concepts of host-region and reflective domestic ethnicity. ¹ It is based on fieldwork among the descendants of Lebanese immigrants who settled at the turn of the twentieth century (Hirschfeld Shibley) and recent immigrants from Ukraine (Lesiv). Many studies of diasporas explore the notion of ethnicity. These explorations often take place in large and representative diasporic settings that, in turn, offer platforms for diverse public expressions of ethnic identities reinforced by vibrant diasporic institutions. Newfoundland is a comparatively small territory and has historically been an unpopular destination for immigrants. Furthermore, in Canada, the island is known for its distinct regional sense of identity. The generalised framework of ‘hostland’ frequently used to embrace entire countries or continents is unsuitable for the present study. Via a narrower regional prism, our findings show that, albeit for dissimilar reasons, Lebanese and Ukrainian diasporans engage with their ethnic heritages predominantly in domestic spheres. We explore domestic ethnicity formation processes focusing on select creative expressions, including material objects.

The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices.

The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al. (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model.

The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy.

The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation.

Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.