Search for: All records

Navigating dilemmas involving conflicting values is challenging even for humans in high-stakes domains, let alone for AI, yet prior work has been limited to everyday scenarios. To close this gap, we introduce CLASH (Character perspective-based LLM Assessments in Situations with High-stakes), a meticulously curated dataset consisting of 345 high-impact dilemmas along with 3,795 individual perspectives of diverse values. CLASH enables the study of critical yet underexplored aspects of value-based decision-making processes, including understanding of decision ambivalence and psychological discomfort as well as capturing the temporal shifts of values in the perspectives of characters. By benchmarking 14 non-thinking and thinking models, we uncover several key findings. (1) Even strong proprietary models, such as GPT-5 and Claude-4-Sonnet, struggle with ambivalent decisions, achieving only 24.06 and 51.01 accuracy. (2) Although LLMs reasonably predict psychological discomfort, they do not adequately comprehend perspectives involving value shifts. (3) Cognitive behaviors that are effective in the math-solving and game strategy domains do not transfer to value reasoning. Instead, new failure patterns emerge, including early commitment and overcommitment. (4) The steerability of LLMs towards a given value is significantly correlated with their value preferences. (5) Finally, LLMs exhibit greater steerability when reasoning from a third-party perspective, although certain values (e.g., safety) benefit uniquely from first-person framing. 

Metal plasma has been widely applied in hard coatings or metallization of vias and trenches in semiconductor device fabrication. Ion irradiation plays a vital role in the film properties. Previous methods have focused on the Ti atom and Ti+ ion number densities; however, there is a lack of a practical optical emission spectroscopy method for measuring the Ti2+ ion number density. High-charge-state ions lead to high compressive stress, especially in high-power impulse magnetron sputtering (HiPIMS) and vacuum arc plasma devices with a high ionization fraction. In this work, we present a novel charge-state resolved OES method to obtain the time-resolved Ti+ and Ti2+ ion number densities. This method is based on the excited-state cycle mechanisms of Ti+(4p) and Ti2+(4p) emitting states, as determined by kinetic investigations using a collisional-radiative model. In the excited-state cycle mechanisms, the Ti+/Ti2+ line-ratio is found to be sensitive to the ion ratio, and the Ti2+ line-ratio is sensitive to the electron density. The latter can decouple the influence of the electron density on the Ti+/Ti2+ line ratio, allowing the Ti+ and Ti2+ ion number densities to be determined by combining the above line ratios. This method is verified in a vacuum arc titanium metal plasma source. 

Abstract MotivationAs the SARS-CoV-2 virus rapidly evolves, predicting the trajectory of viral mutations has become a critical yet complex task. A deep understanding of future mutation patterns, in particular the mutations that will prevail in the near future, is vital in steering diagnostics, therapeutics, and vaccine strategies for disease control. ResultsIn this study, we developed a model to forecast future SARS-CoV-2 mutation surges in real-time, using historical mutation frequency data from the USA. We transformed the temporal prediction problem into a supervised learning framework using a sliding window approach. This involved breaking the time series of mutation frequencies into very short segments. Considering the time-dependent nature of the data, we focused on modeling the first-order derivative of the mutation frequency. We predicted the final derivative in each segment based on the preceding derivatives, employing various machine learning methods, including random forest, XGBoost, support vector machine, and neural network models. Empowered by the novel transformation strategy and the high capacity of machine learning models, we observed low prediction error that is confined within 0.1% and 1% when making predictions of mutation rates for the future 30 and 80 days, respectively. In addition, the method also led to a notable increase in prediction accuracy compared to traditional time-series models, as evidenced by much lower MAE (Mean Absolute Error) and MSE (Mean Squared Error) for predictions made within different time horizons. To further assess the method’s effectiveness and robustness in predicting mutation patterns for unforeseen mutations, we first designed a synthetic case where we categorized all mutations into three major patterns. The model demonstrated its robustness by accurately predicting unseen mutation patterns when training on data from two pattern categories while testing on the third pattern category, showcasing its potential in forecasting a variety of mutation trajectories. We then applied our method to prediction for a recent time frame between 1 January 2025 and 10 June 2025, for both the USA and UK, where the model training was conducted using frequency sequence data collected between 12 December 2019 and 26 January 2023 in the USA. The model demonstrated superior performance for both datasets. Availability and implementationTo enhance accessibility and utility, we built our methodology into a GitHub package (https://github.com/ZhouXY199502/SWD). Our method has the potential applicability to study other infectious diseases or forecasting tasks, thus extending its relevance beyond the current COVID pandemic. 

Massachusetts Bay in the northeastern United States is highly vulnerable to ocean acidification (OA) due to reduced buffering capacity from significant freshwater inputs. We hypothesize that acidification varies across temporal and spatial scales, with short-term variability driven by seasonal biological respiration, precipitation–evaporation balance, and river discharge, and long-term changes linked to global warming and river flux shifts. These patterns arise from complex nonlinear interactions between physical and biogeochemical processes. To investigate OA variability, we applied the Northeast Biogeochemistry and Ecosystem Model (NeBEM), a fully coupled three-dimensional physical–biogeochemical system, to Massachusetts Bay and Boston Harbor. Numerical simulation was performed for 2016. Assimilating satellite-derived sea surface temperature and sea surface height improved NeBEM’s ability to reproduce observed seasonal and spatial variability in stratification, mixing, and circulation. The model accurately simulated seasonal changes in nutrients, chlorophyll-a, dissolved oxygen, and pH. The model results suggest that nearshore areas were consistently more susceptible to OA, especially during winter and spring. Mechanistic analysis revealed contrasting processes between shallow inner and deeper outer bay waters. In the inner bay, partial pressure of pCO2 (pCO2) and aragonite saturation (Ωa) were influenced by sea temperature, dissolved inorganic carbon (DIC), and total alkalinity (TA). TA variability was driven by nitrification and denitrification, while DIC was shaped by advection and net community production (NCP). In the outer bay, pCO2 was controlled by temperature and DIC, and Ωa was primarily determined by DIC variability. TA changes were linked to NCP and nitrification–denitrification, with DIC also influenced by air–sea gas exchange. 

The rapid development of adeno-associated viral vectors (AAV) to treat genetic disease has placed increased emphasis on the design of efficient downstream manufacturing processes. This study investigated the potential of using single pass tangential flow filtration (SPTFF) as a novel means of concentrating and purifying AAV clarified cell lysate (CCL). AAV stability studies revealed the shear-sensitive nature of the AAV capsids, with evidence of aggregation and fragmentation following repeated passages through a peristaltic pump (as would occur during batch ultrafiltration). SPTFF experiments focused on first identifying the membrane(s) that permitted high yield of AAV (negligible sieving into the permeate) along with substantial host cell protein (HCP) removal. Experiments were then performed at various permeate fluxes, which revealed that stable SPTFF processes can be achieved by operating below a critical flux for fouling (Jfoul). 300 kDa regenerated cellulose (RC) membranes were identified as optimal for this application, given their ability to provide complete AAV retention with high removal of HCP (>90%) when operated below Jfoul. The critical flux during SPTFF was increased by preconditioning the CCL through a positively-charged adsorptive filter, which reduced the concentration of foulants prior to SPTFF. These studies provide the first demonstration of SPTFF for the concentration and purification of AAV clarified cell lysate while minimizing shear exposure.