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The COVID-19 pandemic created a broad array of challenges for hospitals. These challenges included restrictions on admissions and procedures, patient surges, rising costs of labor and supplies, and a disparate impact on already disadvantaged populations. Many of these intersecting challenges put pressure on hospitals' finances. There was concern that financial pressure would be particularly acute for hospitals serving vulnerable populations, including safety-net (SN) hospitals and critical access hospitals (CAHs). Using data from hospitals in Washington State, we examined changes in operating margins for SN hospitals, CAHs, and other acute care hospitals in 2020 and 2021. We found that the operating margins for all three categories of hospitals fell from 2019 to 2020, with SNs and CAHs sustaining the largest declines. During 2021, operating margins improved for all three hospital categories but SN operating margins still remained negative. Both changes in revenue and changes in expenses contributed to observed changes in operating margins. Our study is one of the first to describe how the financial effects of COVID-19 differed for SNs, CAHs, and other acute care hospitals over the first two years of the pandemic. Our results highlight the continuing financial vulnerability of SNs and demonstrate how the factors that contribute to profitability can shift over time.

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.

Financial inclusion in Peru has been addressed through coverage, quality of financial services, movement of transactions, and service points. The purpose of this chapter is to evaluate for the department of Ancash, Peru, the link between financial inclusion and its socioeconomic factors. Socioeconomic variables and financial inclusion of the Ancash department of the National Household Survey are taken as indicators, later contrasted through the logit model, with the financial inclusion variable being the explained variable.

There is evidence of positive and negative relationships between financial inclusion and socioeconomic variables; these are important components for planning financial inclusion. Raising the levels of formal employment, the educational level and considering the area of residence would be a strategy to generate a dynamic of inclusion in the department of Ancash.

Machine learning is an algorithmic-based auto-learning mechanism that improves from its experiences. It makes use of a statistical learning method that trains and develops on its own without the assistance of a person. Data, characteristics deduced from the data, and the model make up the three primary parts of a machine learning solution. Machine learning generates an algorithm from subsets of data that can utilise combinations of features and weights different from those obtained from basic principles. In this paper, an analysis of customer behaviour is predicted using different machine learning algorithms. The results of the algorithms are validated using python programming.

This study investigates the impacts of operational risks and corporate governance on bond yield spreads, examining their impacts on bond yield spreads during the COVID-19 pandemic. The results indicate that operational risks significantly raise yield spreads, especially for high-leverage firms. Moreover, a higher independent director percentage reduces debt costs. Furthermore, the results reveal more pronounced effects of operational risks on yield spreads during the COVID-19 pandemic, with these risks increasing the financing costs for large firms. When the effect of the independent director percentage on the yield spreads increases, this consequently raises the debt costs for large firms.

Corporate misconduct carries significant social and economic costs, and therefore regulators and other stakeholders seek to deter it. Despite the significant costs and deterrence efforts, corporate misconduct is widespread and our understanding of it is limited. As argued in this chapter, one key reason for this is the lack of understanding of the benefits and penalties of misconduct for the companies and individuals involved, as well as the detection of such behavior. This chapter seeks to advance our understanding of corporate misconduct and builds on the rational choice model (RCM) – where the decision to engage in misconduct hinges on a calculation of the expected costs and benefit – and links it to research in organization theory and strategy. Specifically, it sets a research agenda at the intersection of organizational and strategic perspectives, to deepen our understanding of corporate misconduct and shed light on opportunities for empirical and theoretical research which can potentially aid in developing effective deterrence strategies.

The protective aspects of relationships for health have been extensively studied. Here, we assess whether different dimensions of partnership status at the time of a child’s birth are associated with better self-assessed health later in mid-life. Data are from three countries with different social welfare policies relating to union status and parenting: the US, the UK, and Norway. Results indicate that women who were partnered at first birth had better health at midlife in all three countries than women who were unpartnered. The analysis indicates no differences in the mid-life health of Norwegian women who were married or cohabiting at birth, whereas for US and UK women, being married at the birth of a first child is more beneficial for mid-life health than bearing the child in a cohabiting union. In the US, women who are least likely to marry do not demonstrate better mid-life health if they had wed relative to cohabiting. In the UK, in contrast, the women least likely to be married at the birth experience better returns if they marry. These findings highlight the importance of paying closer attention to heterogeneous treatment effects as they relate to childbearing, relationship status, and mid-life health.

Pierre Nelis joined a small group of artists working for a creative entrepreneur who had invented software to produce movies. He brought a great deal of marketing expertise to a team of technology creators, and it was this that ultimately allowed the firm to sell its software to movie industry leaders throughout the world. The firm – Softimage – was bought by Microsoft, which hired Pierre Nelis to oversee the integration process, and later to develop new communications products. Nelis has an outstanding ability to identify the elements needed by a firm to become more effective, and this led him to set up a one-of-a-kind external facilitation programme that went on to become a model for many business growth support organizations throughout the world, but especially in North America and Europe.