St. Francis College (St. Francis of Brooklyn or SFC) is a private college in Brooklyn Heights, New York. It was founded in 1859 by the Franciscan Brothers of Brooklyn, a Franciscan order, as the St. Francis Academy and was the first private school in the Roman Catholic Diocese of Brooklyn. St. Francis College began as a parochial all-boys academy in the City of Brooklyn and has become a small liberal arts college that has 19 academic departments which offer 72 majors and minors.St. Francis College is a predominantly undergraduate institution with graduate programs in accounting, project management, psychology and creative writing. St. Francis is set in an urban environment and is considered a commuter college. As of 2019, there were 2,323 undergraduates (6% part-time) and 90 graduates (51% part-time).St. Francis College has 21 athletic teams that compete in Division I of the NCAA and are known as the Terriers. SFC's teams participate in the Northeast Conference, with the exception of the men's and women's water polo teams which compete in the CWPA and the MAAC, respectively.St.St.St.St. St. St. St.St.St.
The growing magnitude, diversity, and speed of data produced across contemporary cyber-physical, enterprise, and cloud-edge systems has revealed inherent constraints with the inflexible artificial intelligence (AI) models regarding providing real-time choices. Self-adaptive artificial intelligence systems have become an attractive paradigm to overcome these challenges through enabling continuous learning, dynamic model reconfiguration and context-specific optimization in the changing data distributions and operational constraints. In this paper, a detailed self-adaptive AI architecture to serve large-scale decision making with data is provided, which involves online learning, feedback-based adjustment, and automatic policy optimization. The suggested architecture takes advantage of the hybrid intelligence methods merging deep learning, reinforcement learning, and meta-learning to reach strong performance in non-stationary and uncertain environments. Adaptive control loops are incorporated to check data drift, system performance and the consumption of resources and recalibration of a model can be done in real time without involving human intervention. The comprehensive test on large-scale datasets indicates that the proposed solution is much more accurate in making decisions, flexible and resilient to systems against the traditional non-adaptable and semi-adaptive AI models. The outcomes confirm the effectiveness of self-adaptive intelligence in aiding scalable, dependable, and explainable decision-making of dynamic data-intensive applications including smart infrastructure, autonomous systems, and intelligent business analytics.
BACKGROUND:Artificial intelligence (AI) is increasingly embedded in healthcare businesses, promoted for its ability to enhance efficiency, reduce costs and optimize workflows. However, the intersection of profit-driven priorities with patient-centred values presents significant ethical and professional challenges for nurses, who serve as the frontline mediators between technology and patients. AIM:This study aimed to explore nurses lived experiences of AI integration in healthcare businesses, focusing on how they navigate tensions between institutional efficiency and their professional commitment to patient-centred care. METHODS:An interpretive phenomenological design was employed to capture the depth of nurses' perspectives. Data were collected between May and June 2025 through 26 semi-structured interviews and 1 focus group with 7 nurses, yielding a total of 33 participants from AI-integrated private hospitals. Transcripts were analyzed thematically, with trustworthiness ensured through member validation, audit trails and reflexive journaling. RESULTS:Four overarching themes emerged. Nurses reported emotional and ethical conflicts when AI recommendations contradicted clinical judgement, often leading to moral distress. Business imperatives were perceived to prioritize efficiency over individualized care, with nurses excluded from decision-making about AI adoption. Many participants expressed anxiety over role displacement and a diminishing sense of autonomy, although some redefined their professional identity as technology navigators. Inadequate training and lack of institutional support further amplified challenges, leaving nurses underprepared to manage AI tools effectively. CONCLUSION:While AI offers organizational advantages, its integration without inclusive planning and adequate training risks undermining holistic nursing practice. Strengthening institutional support, valuing nurses' input and balancing efficiency with empathy are essential to align technological innovation with compassionate, patient-centred care.
We obtain an orthogonality space by endowing an implicative-ortholattice (i-OL) with a suitable orthogonality relation; for such spaces, we also investigate the particular case of implicative-orthomodular lattices (i-OMLs). Moreover, we define the commutativity relation between two elements of an i-OL, as well as the Sasaki projections on this structure. Furthermore, we characterize the i-OMLs and implicative-Boolean algebras (i-Boolean algebras), showing that the center of an i-OML is an i-Boolean algebra. We prove that an i-OL is an i-OML if and only if it admits a full Sasaki set of projections. Finally, based on Sasaki maps on implicative-ortholattices, we introduce the notion of Sasaki spaces, proving that when a complete i-OL admits a full Sasaki set of projections, it is a Sasaki space. We also provide a characterization of Dacey spaces arising from i-OLs.
Gene expression profiles can tell one cancer from another, but a profile has tens of thousands of genes, and most of them add little to a diagnosis. A large panel is also hard to read and expensive to run in a clinic. This paper asks a simple question. How few genes are enough to separate five common tumor types with high accuracy. We use the public TCGA pan-cancer RNA-Seq dataset, which has 801 samples across breast, kidney, colon, lung, and prostate tumors and more than 20000 genes. We rank genes with a univariate filter and train three standard classifiers, a logistic regression, a linear support vector machine, and a random forest, on panels of growing size. To keep the results honest, the ranking is redone inside every cross-validation fold, so no gene is chosen using the test data. Accuracy climbs fast and then flattens. A panel of only 20 genes reaches about 99% accuracy, and a panel of 10 genes is already above 90%. A linear support vector machine on the $\mathbf{2 0}$-gene panel reaches $\mathbf{9 9. 4 \%}$ accuracy with a macro F1 of 0.994. We also compare three ways of ranking genes and report the selected panel. The result is a small and reproducible biomarker panel that keeps the accuracy of the full profile while using a tiny fraction of the genes.
