American Public University System

Producing stable 58Ni in Type Ia supernovae (SNe Ia) requires sufficiently high-density conditions that are not predicted for all origin scenarios, so examining the distribution of 58Ni using the near-infrared (NIR) [Ni II] 1.939 mu m line may observationally distinguish between possible progenitors and explosion mechanisms. We present 79 telluric-corrected NIR spectra of 22 low-redshift SNe Ia from the Carnegie Supernova Project-II, ranging from +50 to +505 days, including 31 previously unpublished spectra. We introduce the Gaussian Peak Ratio, a detection parameter that confirms the presence of the NIR [Ni II] 1.939 mu m line in eight SNe in our sample. Nondetections occur at earlier phases (<=+100 days) when the NIR Ni line has not emerged yet or in low signal-to-noise spectra, yielding inconclusive results. Subluminous 86G-like SNe Ia show the earliest NIR Ni features around similar to+50 days, whereas normal-bright SNe Ia do not exhibit NIR Ni until similar to+150 days. NIR Ni features detected in our sample have low peak velocities (v similar to 1200 km s-1) and narrow line widths (<= 3500 km s-1), indicating stable 58Ni is centrally located. This implies high-density burning conditions in the innermost regions of SNe Ia and could be due to higher mass progenitors (i.e., near-Mch). NIR spectra of the nearly two dozen SNe Ia in our sample are compared to various model predictions and paired with early-time properties to identify ideal observation windows for future SNe Ia discovered by upcoming surveys with Rubin-LSST or the Roman Space Telescope.

Background/Objectives: The global aging population has placed escalating demands on long-term care systems, with nursing homes facing persistent challenges including chronic understaffing, high staff turnover, complex resident acuity, and elevated risk of adverse events. Artificial intelligence (AI)—encompassing machine learning, natural language processing, and computer vision—presents a transformative opportunity to address these systemic pressures by enabling proactive, data-driven care delivery. This rapid review aims to systematically map the existing literature on AI applications in nursing facilities, categorize how these technologies contribute to improvements in quality of care, and identify gaps warranting further investigation. Methods: Following Arksey and O’Malley’s framework and PRISMA-ScR guidelines, we conducted a comprehensive search of academic literature using a predefined Boolean string. The extracted data were organized and analyzed thematically. Results: The synthesized literature (n = 28 studies) revealed seven primary themes: (1) Clinical management, risk prediction, and monitoring; (2) Pressure injuries, wound management, and diagnostics; (3) Objective assessment, mental health, and end-of-life care; (4) Nutrition and personalized daily support; (5) Operational efficiency and staffing; (6) Technical, infrastructure, and economic barriers; and (7) Social, ethical, and demographic considerations. Conclusions: AI holds considerable promise for enhancing the quality of care in nursing homes across clinical, operational, and social domains. However, widespread adoption remains constrained by prohibitive infrastructure costs, data privacy regulations, algorithmic bias, staff resistance, and limited generalizability of findings across diverse populations. Successful integration requires evidence-based implementation frameworks and standardized and interoperable platforms.

BACKGROUND:Artificial intelligence (AI) is increasingly available to faculty and students, yet adoption remains uneven. Faculty report uncertainty about capabilities, limitations, and ethical boundaries, and lack time for integration, while institutions grapple with policy, privacy, and equity concerns. The purpose of this article is to provide a concise, nursing faculty-centered AI development checklist for responsible AI integration in teaching and assessment. METHOD:AI implementation guidance was synthesized into a five-step checklist for faculty AI competency development. RESULTS:The checklist converts exploratory evidence into a logical faculty development plan, emphasizing practical steps that reduce cognitive load, standardize expectations, and support equitable access to enable rapid adoption and consistent implementation for individuals, programs, and institutions. CONCLUSION:With a concise evidence-based checklist, nurses can achieve the benefits of AI while advancing equity and preserving the human-centered core of the nursing profession. This checklist simplifies steps for understanding new technologies, adds to research on competencies, and improves instructional efficiency.

Industrial Control Systems (ICS) form the backbone of modern critical infrastructure but are increasingly exposed to cyber-physical attacks due to their integration with networked control and supervisory systems. This study develops a Reservoir Computing (RC) framework for real-time anomaly detection in a simulated microgrid of ten PLC nodes. The model uses a fixed recurrent reservoir with a lightweight readout layer to capture nonlinear dynamics while remaining efficient for edge hardware. Dynamic residuals in voltage, current, and frequency signals define an adaptive anomaly score based on evolving statistics. Tests under multiple attack types-drift, replay, freeze, and injection-achieved precision 0.94, recall 0.91, and F1-score 0.92 with inference times under 1.5 ms. Visual analyses of reservoir trajectories, Bayesian decision surfaces, and ROC mappings show clear separability between nominal and attack states. Results confirm that RC provides a fast, interpretable, and resource-efficient solution for securing modern ICS and microgrid environments.

BackgroundBlood lactate accumulation during exercise has traditionally been associated with reduced oxygen availability and an increased reliance on glycolytic metabolism. However, lactate production is influenced by multiple processes beyond oxygen availability. Previous pilot work demonstrated that a standardized controlled breathing intervention produced intermittent reductions in peripheral oxygen saturation comparable to intermittent hypoxic training, but its effects on blood lactate had not been investigated. This crossover study compared normobaric hypoxia (NH) and controlled breathing (CB). We hypothesized that both interventions would increase blood lactate accumulation relative to control.MethodsFifteen healthy adults completed three laboratory visits using a within-subject crossover design. Participants performed an identical 30-minute functional exercise protocol during control (CON), NH (15% inspired oxygen), and CB. Blood lactate was measured at rest, after 10, 20, and 30 minutes of exercise, and after 10 minutes of recovery. Blood lactate AUC, maximum blood lactate, average HR, peripheral oxygen saturation (SpO2), and ratings of perceived exertion (RPE) were assessed. Data were analyzed using linear mixed-effects models with Tukey-adjusted pairwise comparisons.ResultsContrary to hypothesis, NH and CB produced different metabolic responses despite reducing peripheral oxygen saturation. Blood lactate differed among conditions over time (condition × time interaction, p = 0.007), with CB demonstrating lower blood lactate than CON at 10, 20, 30 minutes, and recovery, and lower blood lactate than NH at 20 and 30 minutes. Maximum blood lactate (p = 0.002), blood lactate AUC (p < 0.001), and average HR (p < 0.001) were significantly lower during CB. NH produced greater reductions in peripheral oxygen saturation than CB, although both interventions reduced SpO2 relative to CON. RPE did not differ among conditions (p = 0.371).ConclusionsA standardized controlled breathing intervention was associated with lower blood lactate accumulation, cumulative lactate exposure, peak blood lactate concentration, and average HR despite significant reductions in peripheral oxygen saturation during moderate-intensity exercise. These exploratory findings suggest pulse oximetry-measured peripheral oxygen saturation may not fully predict metabolic responses to exercise. Because underlying mechanisms were not directly assessed, future mechanistic studies using direct measurements of skeletal muscle oxygenation, gas exchange, and metabolic regulation are warranted.