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(WP 2025-06) Ramsey and Keynes...and Sraffa and Wittgenstein: Change in Interwar Cambridge Economics and Policy
No full textThe Ramsey-Keynes exchange regarding the nature of probability is investigated in connection with the development of interwar Cambridge economics and Ramsey and Sraffa’s influences on Keynes and Wittgenstein. First discussed are Ramsey’s criticisms of Keynes and Wittgenstein; then Sraffa’s criticisms of Marshall and Wittgenstein. Ramsey was influenced by Peirce and pragmatism and Sraffa by Gramsci and the theory of cultural hegemony. Ramsey died in 1930 but Sraffa continued to interact with both Keynes and Wittgenstein. After his critique of Marshall he participated in the ‘Cambridge circus’ and turned to recovering the Classical economics of Ricardo. Keynes’s General Theory statement of what the essence of his theory was and Wittgenstein’s Philosophical Investigations distinction between rules of language and language-games both parallel Sraffa’s Classical outside forces operating in the economic field argument and Gramsci’s state power and non-state institutions view. Keynes and Wittgenstein determined the nature of interwar Cambridge economics and philosophy. Ramsey and Sraffa were instigators of this change
Quantum Simulation of Molecular Dynamics Processes─A Benchmark Study Using a Classical Simulator and Present-Day Quantum Hardware
No full textWe explore how the fundamental problems in quantum molecular dynamics can be modeled using classical simulators (emulators) of quantum computers and the actual quantum hardware available to us today. The list of problems we tackle includes propagation of a free wave packet, vibration of a harmonic oscillator, and tunneling through a barrier. Each of these problems starts with the initial wave packet setup. Although Qiskit provides a general method for initializing wave functions, in most cases it generates deep quantum circuits. While these circuits perform well on noiseless simulators, they suffer from excessive noise on quantum hardware. To overcome this issue, we designed a shallower quantum circuit for preparing a Gaussian-like initial wave packet, which improves the performance of real hardware. Next, quantum circuits are implemented to apply the kinetic and potential energy operators for the evolution of a wave function over time. The results of our modeling on classical emulators of quantum hardware agree perfectly with the results obtained using the traditional (classical) methods. This serves as a benchmark and demonstrates that the quantum algorithms and Qiskit codes we developed are accurate. However, the results obtained on the actual quantum hardware available today, such as IBM’s superconducting qubits and IonQ’s trapped ions, indicate large discrepancies due to hardware limitations. This work highlights both the potential and challenges of using quantum computers to solve fundamental quantum molecular dynamics problems
From the Podium to the Press: Coverage of Kamala Harris\u27s 2024 Convention Address
No full textKamala Harris\u27s nomination at the 2024 Democratic National Convention was both unusual and historic. Not only was she the first woman of color to be nominated for the presidency by a major party, but she also emerged as the party\u27s choice without having competed in any nominating contests, an outcome made possible by Joe Biden\u27s withdrawal from the candidacy. National nominating conventions are important opportunities for parties and candidates to draw media attention, and this coverage shapes the political impact of conventions. This was especially important in 2024, given the shortened nature of the Harris campaign. Using a sample of newspapers and TV news programs, we find that the media over emphasized controversial issues such as the war in Gaza and immigration while under emphasizing other substantive policy areas such as the economy. In addition, the coverage tended to amplify attention to so-called women\u27s issues and downplay Harris\u27s patriotic appeals. These findings highlight the divergence between what the candidate emphasized and what the media reported, with important implications for public perception of the campaign and Harris\u27s policy priorities
Use of Light Naphtha in Heavy-Duty Compression Ignition Engines – Analysis of Engine Performance, Emissions, and Economics
No full textThe most pragmatic and impactful way to reduce GHG emissions from heavy-duty engines in the near term is to use lower lifecycle GHG fuels which are compatible with existing infrastructure. Light naphtha requires very little refinery processing and thus its production and distribution (well-to-tank) CO2 emissions are ∼70 % lower than fossil ultra-low sulfur diesel fuel. Additionally, due to the higher hydrogen-to-carbon ratio, light naphtha has ∼10 % lower tailpipe (tank-to-wheels) CO2 emissions compared to diesel fuel. Based on economic modelling, light naphtha has the potential to be ∼5 to 25 % easier to produce than gasoline on an energy basis, providing a meaningful reduction in the total cost of ownership for fleet operators when compared to diesel fuel. Due to its fuel properties and economics, light naphtha also has potential to be an attractive alternative fuel for heavy-duty compression ignition engines to reduce criteria pollutants, GHG emissions, and operating costs. In this work, engine experiments were conducted to understand light naphtha’s performance and emissions compared to diesel fuel. It was determined that at light engine loads, due to the high volatility and low fuel reactivity of light naphtha, partially premixed combustion was achievable resulting in a ∼80 % to 99 % reduction in soot at equal NOx while observing improvements in efficiency compared to diesel combustion. The challenge at light load is developing an injection strategy that has acceptable levels of combustion noise with light naphtha. At higher engine loads, the fuel property differences are not as apparent and the combustion process between light naphtha and diesel fuel is more similar, which is predominately mixing-controlled. Regardless, due to the physical and chemical properties of light naphtha, soot emissions are reduced by ∼50 % to 80 % compared to diesel fuel, while achieving similar engine efficiency and combustion noise
Applying Large Language Models for Surgical Case Length Prediction
No full textImportance Accurate prediction of surgical case duration is critical for operating room (OR) management, as inefficient scheduling can lead to reduced patient and surgeon satisfaction while incurring considerable financial costs.
Objective To evaluate the feasibility and accuracy of large language models (LLMs) in predicting surgical case length using unstructured clinical data compared to existing estimation methods.
Design, Setting, and Participants This was a retrospective study analyzing elective surgical cases performed between January 2017 and December 2023 at a single academic medical center and affiliated community hospital ORs. Analysis included 125493 eligible surgical cases, with 1950 used for LLM fine-tuning and 2500 for evaluation. An additional 500 cases from a community site were used for external validation. Cases were randomly sampled using strata to ensure representation across surgical specialties.
Exposures Eleven LLMs, including base models (GPT-4, GPT-3.5, Mistral, Llama-3, Phi-3) and 2 fine-tuned variants (GPT-4 fine-tuned, GPT-3.5 fine-tuned), were used to predict surgical case length based on clinical notes.
Main Outcomes and Measures The primary outcome was average error between predicted and actual surgical case length (wheels-in to wheels-out time). The secondary outcome was prediction accuracy, defined as predicted length within 20% of actual duration.
Results Fine-tuned GPT-4 achieved the best performance with a mean absolute error (MAE) of 47.64 minutes (95% CI, 45.71-49.56) and R2 of 0.61, matching the performance of current OR scheduling (MAE, 49.34 minutes; 95% CI, 47.60-51.09; R2, 0.63; P = .10). Both GPT-4 fine-tuned and GPT-3.5 fine-tuned significantly outperformed current scheduling methods in accuracy (46.12% and 46.08% vs 40.92%, respectively; P \u3c .001). GPT-4 fine-tuned outperformed all other models during external validation with similar performance metrics (MAE, 48.66 minutes; 95% CI, 45.31-52.00; accuracy, 46.0%). Base models demonstrated variable performance, with GPT-4 showing the highest performance among non–fine-tuned models (MAE, 59.20 minutes; 95% CI, 56.88 - 61.52).
Conclusion and Relevance The findings in this study suggest that fine-tuned LLMs can predict surgical case length with accuracy comparable to or exceeding current institutional scheduling methods. This indicates potential for LLMs to enhance operating room efficiency through improved case length prediction using existing clinical documentation
AC-Induced Corrosion of Cathodically Protected Pipelines: Experimental Study and Probabilistic Modeling
No full textThis study investigated the effects of alternating current (AC) interference on pipeline steel under cathodic protection (CP). In a simulated solution, real-time electrochemical measurements and corrosion rate analysis were conducted on two steel types (C1018 and X60) under various levels of AC interference with CP. Due to the complexity of AC-induced corrosion, relying on the shift in DC potential alone cannot accurately demonstrate the corrosion behavior in the presence of AC interference. In fact, such an approach may mislead the predictions of corrosion performance. It is observed that AC interference reduced the effectiveness of CP and increased the corrosion rate of the steel, both in weight loss and Tafel Extrapolation (Tafel) measurements. The study concluded that conventional CP standards used in the field were inadequate in the presence of high AC-level interference. Furthermore, this study found that a more negative CP current density (−0.75 A/m2) could reduce the effect of AC interference by 46–93%. This is particularly shown in the case of low-level AC interference, where the reduction can reach up to 93%. Utilizing the experimental data obtained by the two measurement methods, probabilistic models to predict the corrosion rate were developed with consideration of the uncertainty in the measurements. The sensitivity analysis showed how AC interference impacts the corrosion rate for a given CP level
Student wellness: Interest and program ideas & pilot of a student wellness program
No full textObjective: To increase our knowledge related to student wellness programs, the current studies examined interest in wellness and wellness programs among university students and piloted a newly developed wellness program targeting students in higher education. Participants/Methods: In Study 1, 93 undergraduate students answered questions related to their wellness and mental health (e.g. psychological wellbeing, satisfaction with life, optimism, and stress) and to wellness programs (e.g. interest, barriers, duration, and topics). In Study 2, 13 undergraduate and graduate students participated in a 9-week pilot wellness program focused on specific wellness topics (e.g. relaxation, yoga, gratitude and self-compassion, and emotion regulation). Results/Conclusions: Study 1 results support a strong interest in wellness and wellness programs among undergraduate students. Study 2 results suggest that students who participated in an on-campus wellness program reported higher levels of overall psychological wellbeing and optimism and lower levels of mental health issues relative to baseline