American School of Milan

Background/Objectives: Healthcare organizations worldwide face challenges in retaining talented employees, with the phenomenon of quiet quitting increasingly recognized as a contemporary issue. Rather than leaving their jobs, employees remain at work but exert minimal effort and exhibit reduced engagement, which can ultimately undermine the performance of healthcare organizations. The aim of this research was to examine the impact of servant leadership on work engagement within healthcare organizations, to determine whether this leadership style can help mitigate the effects of quiet quitting. Methods: The study employed a quantitative approach, utilizing validated instruments to measure servant leadership and work engagement. A cross-sectional study design was utilized, employing a convenience sampling method. A total of 362 valid surveys were collected from healthcare professionals in Serbia participating in the study from January to March 2025. The partial least squares structural equation modeling (PLS-SEM) method was used to examine the relationship between servant leadership and work engagement among healthcare professionals. Results: The results indicate that servant leadership has a positive and statistically significant impact on all dimensions of engagement: vigor, dedication, and absorption. Conclusions: Based on these findings, it is concluded that servant leadership can serve as an effective strategy for enhancing work engagement and reducing negative employee behaviors, such as quiet quitting, which may, in turn, improve organizational efficiency in the healthcare industry.

Biofuels derived from biomass feedstocks produced following the implementation of measures that avoid indirect land used change (ILUC) have the potential of reducing dependency on fossil-based fuels without competing with food value chain. This work develops a model- and optimisation-based methodologies that address challenges related to the production of low-ILUC biomass feedstocks. Case studies investigated include planning of Iow-ILUC biomass feedstock production, biomethane production using low-ILUC biomass feedstocks, integrated production of first generation (1G) and second generation (2G) bioethanol using Miscanthus, and production of hydrotreated vegetable oil (HVO) using castor seeds. Analysis of results show that farmers interested in these models are recommended to sell low-ILUC biomass such as soybean, wheat and brassica above the breakeven price to avoid losses. The estimated selling price for the three crops are 362 €/t, 321 €/t and 381 €/t respectively. To meet the demand of 40,000 t/yr of 2G bioethanol in the UK, approximately 17,094 ha of underutilised land is required. Policy makers should consider options to support alternatives such as retrofitting, and inter-cropping to avoid or mitigate ILUC.

<bold>INTRODUCTION:</bold> Despite decades of inhaler use, inhalation device errors are prevalent, compromising treatment efficacy and substantial healthcare costs. Digital dry powder inhalers (DPIs) can offer real-time monitoring and remote support, potentially enhancing treatment adherence and patient technique. However, the impact of digitalization on the in-vitro performance and the bioavailable dose delivered remains understudied. METHOD 28 study participants received a DPI together and a training session from a licenced pharcacist. Participants in the "intervention" group utilized a smartphone application providing real-time feedback on inhalation technique, while no monitoring was available to the control group. Adherence was monitored and the impact of various improper inhalation techniques observed among participants on the device performance were evaluated in-vitro. <bold>Results:</bold> The intervention group showed significantly better inhalation technique and adherence (figure 1). <fig><object-id>erj;64/suppl_68/PA2324/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig> Figure 1 Inhaler technique comparison between Control (A) and Intervention (B) group. In-vitro simulations has shown that the various improper inhalation techniques observed among participants in the control group could lead to up to 65% of loss of bioavailable dose delivered from the device. <bold>CONCLUSION:</bold> Participants using a digitalized DPI with a companion app significantly improved adherence and demonstrated inhalation technique comparable to theoretical perfect inhalation.

This article provides a combined geospatial artificial intelligence-machine learning, geoAI-ML, agent-based, data-driven, technology-rich, bottom-up approach and datasets for capturing the human dimension in climate-energy-economy models. Seven stages were required to conduct this study and build thirteen datasets to characterise and parametrise geospatial agents in 28 regions, globally. Fundamentally, the methodology starts collecting and handling data, ending with the application of the ModUlar energy system Simulation Environment (MUSE), ResidentiAl Spatially-resolved and temporal-explicit Agents (RASA) model. MUSE-RASA uses AI-ML-based geospatial big data analytics to define eight scenarios to explore long-term transition pathways towards net-zero emission targets by mid-century. The framework and datasets are key for climate-energy-economy models considering consumer behaviour and bounded rationality in more realistic decision-making processes beyond traditional approaches. This approach defines energy economic agents as heterogeneous and diverse entities that evolve in space and time, making decisions under exogenous constraints. This framework is based on the Theory of Bounded Rationality, the Theory of Real Competition, the theoretical foundations of agent-based modelling and the progress on the combination of GIS-ABM.

Oral-facial-digital syndromes (OFDS) constitute a heterogeneous group of entities whose clinical manifestations are often overlapping. We report on a 23-week-old aborted fetus who showed a transitional phenotype between OFD II and OFD VI syndromes.