Princeton University is a private Ivy League research university in Princeton, New Jersey. Founded in 1746 in Elizabeth as the College of New Jersey, Princeton is the fourth-oldest institution of higher education in the United States and one of the nine colonial colleges chartered before the American Revolution.[a] The institution moved to Newark in 1747, and then to the current site nine years later. It officially became a university in 1896 and was subsequently renamed Princeton University. Princeton is often ranked among the best and most prestigious universities in the world.The university is governed by the Trustees of Princeton University and has an endowment of $37.7 billion, the largest endowment per student in the United States. Princeton provides undergraduate and graduate instruction in the humanities, social sciences, natural sciences, and engineering to approximately 8,500 students on its 600 acres (2.4 km2) main campus. It offers postgraduate degrees through the Princeton School of Public and International Affairs, the School of Engineering and Applied Science, the School of Architecture and the Bendheim Center for Finance. The university also manages the Department of Energy's Princeton Plasma Physics Laboratory and is home to the NOAA's Geophysical Fluid Dynamics Laboratory. It is classified among "R1: Doctoral Universities – Very high research activity" and has one of the largest university libraries in the world.Princeton uses a residential college system and is known for its upperclassmen eating clubs. The university has over 500 student organizations. Princeton students embrace a wide variety of traditions from both the past and present. The university is a NCAA Division I school and competes in the Ivy League. The school's athletic team, the Princeton Tigers, has won the most titles in its conference and has sent many students and alumni to the Olympics.As of October 2021, 75 Nobel laureates, 16 Fields Medalists and 16 Turing Award laureates have been affiliated with Princeton University as alumni, faculty members, or researchers. In addition, Princeton has been associated with 21 National Medal of Science awardees, 5 Abel Prize awardees, 11 National Humanities Medal recipients, 215 Rhodes Scholars and 137 Marshall Scholars. Two U.S. Presidents, twelve U.S. Supreme Court Justices (three of whom currently serve on the court) and numerous living industry and media tycoons and foreign heads of state are all counted among Princeton's alumni body. Princeton has graduated many members of the U.S. Congress and the U.S. Cabinet, including eight Secretaries of State, three Secretaries of Defense and two Chairmen of the Joint Chiefs of Staff.S.S.S.S.S.S.S.S.S.S.S.S.S.S.
Accurate modeling of compressible premixed flames, encompassing both deflagrations and detonations, remains a significant challenge for predictive Large Eddy Simulation (LES) due to the strong coupling between the thermochemical state and the local thermodynamic state. This work presents a manifold-based turbulent combustion model that ensures a fully consistent thermodynamic state between model and flow solver through an iterative procedure. The framework reproduces critical quantities including temperature, radical species, and source term profiles, addressing limitations of existing approaches that rely on low-Mach perturbations or tabulated ZND detonations without thermodynamic consistency. Validation is performed against one-dimensional and high-fidelity RDE-like data, demonstrating that the thermodynamically consistent model consistently outperforms existing approaches across a broad range of compressible flame regimes — including both deflagration and detonation. The results highlight the importance of fully accounting for the thermodynamic state to achieve accurate predictions. By capturing both deflagrative and detonative behavior within a single framework, the model provides a unified, versatile tool for LES of high-speed reacting flows and offers a foundation for future studies of compressible reacting flows, including applications to rotating detonation engines and other supersonic combustion systems.Novelty and significance statementCurrent manifold-based models for compressible premixed flames are based on a partial coupling of the thermodynamic state with the thermochemical state. This leads to inaccurate predictions of key quantities such as chemical source terms and radical species, particularly in the presence of large thermodynamic fluctuations characteristic of high-speed compressible flows, due to their very strong sensitivity to the thermodynamic state. This work presents a novel manifold-based turbulent combustion model that fully incorporates the thermodynamic state and spans all regimes of premixed combustion from deflagration to detonation, for the first time in one unified model. The new model enables the study of the coupling between chemical kinetics, heat release, and compressible wave dynamics governing combustion processes featuring deflagration and detonation, including deflagration-to-detonation transition and rotating detonation engines, at reduced computational cost compared to detailed chemistry with higher physical accuracy compared to existing models.
Industrial robots are increasingly deployed in construction and manufacturing tasks, where the deployment of end-to-end visuomotor policies is challenged by the observation-execution gap induced by observation, inference, and execution latencies. This gap is often significant on industrial robotic arms due to highlevel control interfaces and slower closed-loop dynamics, making execution timing a dominant system-level concern. This paper presents a system-level, latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms. The framework integrates latency-calibrated multimodal sensing, data synchronization, a unified communication pipeline, and a teleoperation interface for collecting expert demonstrations. Within this framework, we formalize a latency-aware execution strategy that assigns timestamps to policy-predicted action sequences and schedules only temporally feasible actions according to their intended execution time, enabling asynchronous inference and execution without modifying policy architectures or training procedures. We evaluate the framework on a contact-rich assembly task while systematically varying inference latency and compare latency-aware execution against blocking and naive asynchronous baselines using identical policies and sensing modalities. Results show that latency-aware execution preserves smooth motion, compliant contact behavior, and task progression consistent with demonstrations across inference latencies of 100-500 ms. Latency-aware execution maintained task duration and motion smoothness within 13% and 9% of the demonstration reference, respectively, while avoiding the latency-dependent slowdown observed under blocking execution and the large contact-force overshoots produced by naive asynchronous execution. These findings demonstrate that explicit handling of the observation-execution gap is essential for reproducible, closed-loop deployment of visuomotor policies on industrial robotic platforms.
In this article we investigate the relationship of the 3D incompressible, primitive equations with wind-driven boundary conditions and Coriolis force and the Ekman spiral. We identify three distinct regimes. In the first one, we show that every solution converges exponentially fast to the Ekman spiral as t → + ∞. In particular, this implies that the Ekman spiral is the unique equilibrium of the system. In the second regime, we prove nonlinear stability of the Ekman spiral, and in the third one, we show nonlinear instability of the Ekman spiral.
Exposure to environmental stressors such as heat and air pollution is rising in urban areas, placing soft mobility users (e.g., pedestrians, cyclists, and scooter riders) at particular risk—a challenge that will only intensify with climate change. This study develops a comprehensive multi-dimensional exposure assessment framework — BREEZE (Bioclimatic Route Evaluation for Environmental haZard avoidancE) — for urban soft mobility routes that integrates environmental stressor modeling, dynamic population weighting, and citizen-oriented navigation tools. The methodology addresses dual objectives: supporting evidence-based urban planning decisions through exposure hotspot identification and enabling personalized route optimization for individual citizens. The BREEZE framework is designed to be transferable across different urban contexts while accommodating variations in data availability and local environmental conditions. The Brussels-Capital Region (BCR) case study demonstrates the framework’s application across different levels and time spans. At the city level and over the annual time span, incorporating dynamic population data — distinguishing between daytime and nighttime populations — enables more accurate identification of exposure hotspots compared to static census-based approaches, thereby supporting targeted intervention planning. At the individual level and over a daily time span, integrating environmental modeling into route optimization shows that personalized path selection based on hazard avoidance criteria can reduce strong heat or air pollution exposure during daytime or nighttime travel by up to 20% and 40%, respectively. The framework demonstrates how environmental modeling can be translated into actionable insights for both strategic urban planning and individual mobility decision-making, bridging the gap between scientific assessment and practical public health applications.
Background and objective This study presents a computationally and physiologically grounded framework for electromyography (EMG) simulation to examine how assumed cortical and subcortical inputs shape bilateral EMG and intermuscular coherence during bimanual coordination. Methods Simulated first dorsal interosseous (FDI) EMG was generated for a 1:2 force coordination task (approximately 40% maximum voluntary contraction) using motor unit models incorporating (i) variable cortical and subcortical drive weighting, (ii) narrowband versus broadband cortical drive (13–30 vs. 13–60 Hz), and (iii) asymmetric bilateral coupling. Intermuscular EMG–EMG wavelet coherence was computed across α, β, low-γ, and high-γ bands. Model outputs were validated against experimental human EMG at the signal level, comparing simulated and recorded bipolar first dorsal interosseous traces across eight amplitude and frequency-domain signal features. Results Coherence magnitude increased with greater modeled neural crosstalk and cortical weighting. The frequency band most sensitive to drive weighting depended on cortical bandwidth: β-band coherence (13–30 Hz) best differentiated cortical and subcortical influences under narrowband drive, whereas low-γ (30–60 Hz) was most informative under broadband conditions. α-band coherence showed minimal sensitivity to subcortical modulation in this context. Conclusions Within this simulation framework, intermuscular coherence was sensitive to modeled bilateral coupling and cortical-subcortical weighting, with diagnostic sensitivity depending on the spectral bandwidth of cortical drive. Thus, the model should be interpreted as a hypothesis generating, signal level framework rather than a direct neural measurement. This open-source framework bridges computational modeling and biomarker signal analysis, supporting reproducible, hypothesis driven biomarker development for neuromotor disorders such as Parkinson’s disease, spinal cord injury, and stroke.
