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 digital transformation of the U.S. economy has intensified the need for robust financial forecasting and proactive cybersecurity solutions. This study integrates advanced machine learning techniques—Long Short-Term Memory (LSTM), Prophet, and Random Forest Regressor—for financial time-series forecasting across twelve leading U.S. companies. In parallel, a cybersecurity framework was developed using the CICIDS2017 dataset and trained with a Random Forest Classifier to detect cyber intrusions. The forecasting models demonstrated high accuracy, with Random Forest achieving the best performance across most companies, while LSTM showed strong consistency in sequential trend learning. The cybersecurity model achieved an accuracy of 99.89% and an F1-score of 99.72%, effectively identifying attack patterns. Finally, a conceptual “AI Risk Intelligence Decision System” is proposed, fusing financial risk indicators with cyber threat intelligence to support early decision-making in digital financial operations. The proposed framework enhances predictive accuracy and cyber resilience, demonstrating strong potential for integration into real-world financial and cyber risk monitoring systems.
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.
This paper proposes a novel Physics-Informed Self-Supervised Diagnosis (PI-SSD) framework for rotating machinery fault detection, combining physical modeling, self-supervised representation learning, and uncertainty-aware classification. The architecture integrates a multi-resolution convolutional encoder, a windowed Transformer for temporal context modeling, and a latent neural ordinary differential equation (ODE) module that embeds mechanical priors, such as Jeffcott rotor dynamics, directly into the learning process. A masked segment reconstruction objective enables self-supervised pretraining using unlabeled healthy signals, while an evidential classifier head produces fault probabilities with calibrated uncertainty. We evaluate PI-SSD on two publicly available datasets, the NASA PHM'09 Gearbox dataset and the Aalto Rotor dataset, covering 6 fault types and over 5,500 multichannel vibration recordings. Compared to seven strong baselines, PI-SSD achieves the highest Macro-F1 score (0.91) and lowest Expected Calibration Error (ECE = 0.022) on the NASA dataset, while maintaining strong domain transfer performance on Aalto (Macro-F1 = 0.81, PR-MSE = 0.067) without fine-tuning. Ablation studies confirm the contribution of each component, and physics consistency analysis demonstrates low violation rates under varying speeds. These results highlight the potential of embedding physics knowledge into self-supervised neural systems for robust, interpretable, and transferable fault diagnosis in industrial applications.
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.
