Patients with end-stage renal disease (ESRD) on hemodialysis (HD) are at a high risk of acquiring SARS-CoV-2 and of developing severe COVID-19 and death. The possibility of being reinfected with this virus is poorly understood. To date, there are a small number of reports of reinfections in COVID-19 patients, especially in HD patients, with only four cases described so far. The aim was to show the possibility of reinfection and developing severe acute respiratory syndrome in HD patients. We describe a 69-year-old ESRD patient who had been on HD treatment for three years, with diabetes mellitus and a history of ischemic cardiomyopathy. The patient was tested for SARS-CoV-2 by a nasopharyngeal polymerase chain reaction (PCR) test because of a positive cluster at his dialysis unit and initially diagnosed with COVID-19 in July 2020. In this period, he had mild symptoms for a few days and remained asymptomatic afterwards. Four months later, he presented to the hospital with fatigue, high fever and shortness of breath, and was COVID-19 positive again. This case points to the possibility of reinfection, lack of immune response after an asymptomatic or mild infection, or even the possibility of the fi rst false-positive PCR test. Future longitudinal studies are needed to evaluate the potential reinfections, recurrence, and duration of antibody detection.

Key account management (KAM) in theory is described as a strategic approach distinguishable from account management or key account selling that should be used to endure long-term development and retention of strategic customers. This article presents the importance of key account management orientation in today's business and how it affects the non-financial performance of companies in Bosnia and Herzegovina. Also, we will present the results of ongoing research that aims to identify the influence of key account management orientation on company non-financial performance in different industry sectors. Data were collected from several companies in different industries considering a company as a unit of analysis. Research instrument - questionnaire compromised scales that had been validated and found reliable in previous research. Item total reliability and confirmatory factor analysis will be used to test the reliability and validity of the constructs. Furthermore, the structural equation modelling (SEM) technique will be employed to analyze the effects of key account management orientation on a company's non-financial performance. It is to be expected that the results of the conducted research show a statistically significant impact of key account management orientation on a company's non-financial performance in Bosnia and Herzegovina.

This study examines student engagement in an online environment concerning the perception regarding the course and the technology used. A research model was developed from the principal tenets of the expectancy-value theory to which values and expectations are assumed to influence how students build engagement. The model conjoins student perception related to course factors (content and rigor), technology factor (technology convenience), and student engagement (psychological, cognitive, emotional, and behavioral). The model was tested using a sample composed of 328 business undergraduate students taking the courses online using the BigBlueButton e-learning system due to the global emergency caused by the COVID-19 pandemic. Hence, respondents did not voluntarily choose the online teaching delivery method. The results imply that both course content and perceived technology convenience predict overall student engagement, while course rigor influences student cognitive, emotional, and behavioral commitment, but not psychological engagement.

High penetration of renewable energy sources brings both opportunities and challenges for Smart Grid operation. Due to their high contribution to energy consumption, aggregated load flexibility of small residential and service sector consumers has a potential to address the intermittency challenge of distributed generation. Predicting aggregated load flexibility of this consumer sector involves access to sensitive smart meter data, raising data collection and sharing concerns. Federated Learning, a decentralized machine learning technique that uses data distributed on user devices to construct an aggregated, global model, offers potential solutions to tackling this challenge. This paper explores the potential of using Federated Learning for flexibility prediction in Smart Grids through an analysis of its opportunities and implications for different stakeholders involved, as well as the challenges faced. The analysis shows that Federated Learning is a promising approach for building privacy-preserving energy portfolios of aggregated demand data.