Application of optical single-sideband laser in Raman atom interferometry

A frequency doubled I/Q modulator based optical single-sideband (OSSB) laser system is demonstrated for atomic physics research, specifically for atom interferometry where the presence of additional sidebands causes parasitic transitions. The performance of the OSSB technique and the spectrum after second harmonic generation are measured and analyzed. The additional sidebands are removed with better than 20 dB suppression, and the influence of parasitic transitions upon stimulated Raman transitions at varying spatial positions is shown to be removed beneath experimental noise. This technique will facilitate the development of compact atom interferometry based sensors with improved accuracy and reduced complexity

Multiphase MCM-CAPRAM modeling of the formation and processing of secondary aerosol constituents observed during the Mt. Tai summer campaign in 2014

Despite the high abundance of secondary aerosols in the atmosphere, their formation mechanisms remain poorly understood. In this study, the Master Chemical Mechanism (MCM) and the Chemical Aqueous-Phase Radical Mechanism (CAPRAM) are used to investigate the multiphase formation and processing of secondary aerosol constituents during the advection of air masses towards the measurement site of Mt. Tai in northern China. Trajectories with and without chemical–cloud interaction are modeled. Modeled radical and non-radical concentrations demonstrate that the summit of Mt. Tai, with an altitude of ∼1.5 km a.m.s.l., is characterized by a suburban oxidants budget. The modeled maximum gas-phase concentrations of the OH radical are 3.2×106 and 3.5×106 molec. cm−3 in simulations with and without cloud passages in the air parcel, respectively. In contrast with previous studies at Mt. Tai, this study has modeled chemical formation processes of secondary aerosol constituents under day vs. night and cloud vs. non-cloud cases along the trajectories towards Mt. Tai in detail. The model studies show that sulfate is mainly produced in simulations where the air parcel is influenced by cloud chemistry. Under the simulated conditions, the aqueous reaction of HSO−3 with H2O2 is the major contributor to sulfate formation, contributing 67 % and 60 % in the simulations with cloud and non-cloud passages, respectively. The modeled nitrate formation is higher at nighttime than during daytime. The major pathway is aqueous-phase N2O5 hydrolysis, with a contribution of 72 % when cloud passages are considered and 70 % when they are not. Secondary organic aerosol (SOA) compounds, e.g., glyoxylic, oxalic, pyruvic and malonic acid, are found to be mostly produced from the aqueous oxidations of hydrated glyoxal, hydrated glyoxylic acid, nitro-2-oxopropanoate and hydrated 3-oxopropanoic acid, respectively. Sensitivity studies reveal that gaseous volatile organic compound (VOC) emissions have a huge impact on the concentrations of modeled secondary aerosol compounds. Increasing the VOC emissions by a factor of 2 leads to linearly increased concentrations of the corresponding SOA compounds. Studies using the relative incremental reactivity (RIR) method have identified isoprene, 1,3-butadiene and toluene as the key precursors for glyoxylic and oxalic acid, but only isoprene is found to be a key precursor for pyruvic acid. Additionally, the model investigations demonstrate that an increased aerosol partitioning of glyoxal can play an important role in the aqueous-phase formation of glyoxylic and oxalic acid. Overall, the present study is the first that provides more detailed insights in the formation pathways of secondary aerosol constituents at Mt. Tai and clearly emphasizes the importance of aqueous-phase chemical processes on the production of multifunctional carboxylic acids

Prediction of acute kidney injury in patients with femoral neck fracture utilizing machine learning

BackgroundAcute kidney injury (AKI) is a common complication associated with significant morbidity and mortality in high-energy trauma patients. Given the poor efficacy of interventions after AKI development, it is important to predict AKI before its diagnosis. Therefore, this study aimed to develop models using machine learning algorithms to predict the risk of AKI in patients with femoral neck fractures.MethodsWe developed machine-learning models using the Medical Information Mart from Intensive Care (MIMIC)-IV database. AKI was predicted using 10 predictive models in three-time windows, 24, 48, and 72 h. Three optimal models were selected according to the accuracy and area under the receiver operating characteristic curve (AUROC), and the hyperparameters were adjusted using a random search algorithm. The Shapley additive explanation (SHAP) analysis was used to determine the impact and importance of each feature on the prediction. Compact models were developed using important features chosen based on their SHAP values and clinical availability. Finally, we evaluated the models using metrics such as accuracy, precision, AUROC, recall, F1 scores, and kappa values on the test set after hyperparameter tuning.ResultsA total of 1,596 patients in MIMIC-IV were included in the final cohort, and 402 (25%) patients developed AKI after surgery. The light gradient boosting machine (LightGBM) model showed the best overall performance for predicting AKI before 24, 48, and 72 h. AUROCs were 0.929, 0.862, and 0.904. The SHAP value was used to interpret the prediction models. Renal function markers and perioperative blood transfusions are the most critical features for predicting AKI. In compact models, LightGBM still performs the best. AUROCs were 0.930, 0.859, and 0.901.ConclusionsIn our analysis, we discovered that LightGBM had the best metrics among all algorithms used. Our study identified the LightGBM as a solid first-choice algorithm for early AKI prediction in patients after femoral neck fracture surgery

UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language

We introduce UbiPhysio, a milestone framework that delivers fine-grained action description and feedback in natural language to support people's daily functioning, fitness, and rehabilitation activities. This expert-like capability assists users in properly executing actions and maintaining engagement in remote fitness and rehabilitation programs. Specifically, the proposed UbiPhysio framework comprises a fine-grained action descriptor and a knowledge retrieval-enhanced feedback module. The action descriptor translates action data, represented by a set of biomechanical movement features we designed based on clinical priors, into textual descriptions of action types and potential movement patterns. Building on physiotherapeutic domain knowledge, the feedback module provides clear and engaging expert feedback. We evaluated UbiPhysio's performance through extensive experiments with data from 104 diverse participants, collected in a home-like setting during 25 types of everyday activities and exercises. We assessed the quality of the language output under different tuning strategies using standard benchmarks. We conducted a user study to gather insights from clinical experts and potential users on our framework. Our initial tests show promise for deploying UbiPhysio in real-life settings without specialized devices.Comment: 27 pages, 14 figures, 5 table

A Dielectric Metasurface Optical Chip for the Generation of Cold Atoms

Compact and robust cold atom sources are increasingly important for quantum research, especially for transferring cutting-edge quantum science into practical applications. In this letter, we report on a novel scheme that utilizes a metasurface optical chip to replace the conventional bulky optical elements used to produce a cold atomic ensemble with a single incident laser beam, which is split by the metasurface into multiple beams of the desired polarization states. Atom numbers $~10^7$ and temperatures (about 35 ${\mu}$K) of relevance to quantum sensing are achieved in a compact and robust fashion. Our work highlights the substantial progress towards fully integrated cold atom quantum devices by exploiting metasurface optical chips, which may have great potential in quantum sensing, quantum computing and other areas

New evidence on housing wealth and consumption channels

This paper provides new evidence on the effect of housing wealth on consumption by focusing on the impact of home-equity extraction. We develop a household consumption decision model to illustrate the differential effect of home-equity extraction, relative to net home equity, on consumption. The home-equity extraction channel is also shown to vary with household-level borrowing constraints. Based on U.S. household survey data and an instrumental-variables approach, our empirical results validate model predictions. We find that the marginal propensity to consume is two times higher for the home-equity extraction channel relative to the conventional housing wealth effect. The consumption effect of home-equity extraction is more than 2.5 times greater for liquidity-constrained households than for unconstrained households. These results are even more pronounced in the case of durable goods consumption for constrained borrowers