Influences of Random Surface Waves on the Estimates of Wind Energy Input to the Ekman Layer in the Antarctic Circumpolar Current Region

Sea surface waves significantly affect the wind energy input to the Ekman layer in the upper ocean. In the study, we first incorporated the wave-induced Coriolis-Stokes forcing, the reduction of wind stress caused by wave generation, and wave dissipation into the classical Ekman model to investigate the kinetic energy balance in the wave-affected Ekman layer. Then, both the theoretical steady state solution for the idealized condition and the nonsteady state solution for the realistic ocean were derived. Total energy input to the wave-affected Ekman layer includes the wind stress energy input and the wave-induced energy input. Based on the WAVEWATCH III model, the wave spectrum was simulated to represent realistic random directional wave conditions. The wind stress energy input and the wave-induced energy input to the wave-affected Ekman layer in the Antarctic Circumpolar Current in the period from 1988 to 2010 were then calculated. The annual mean total energy input in the Antarctic Circumpolar Current region was 402.5 GW and the proportions of the wind stress energy input and the wave-induced energy input were, respectively, 85% and 15%. Particularly, total energy input in the Antarctic Circumpolar Current in the wave-affected Ekman layer model was 59.8% lower than that in the classical Ekman model. We conclude that surface waves play a significant role in the wind energy input to the Ekman layer

ODSum: New Benchmarks for Open Domain Multi-Document Summarization

Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries. With a more inter-related document set, there does not necessarily exist a correct answer for the retrieval, making it hard to measure the retrieving performance. We propose a rule-based method to process query-based document summarization datasets into ODMDS datasets. Based on this method, we introduce a novel dataset, ODSum, a sophisticated case with its document index interdependent and often interrelated. We tackle ODMDS with the \textit{retrieve-then-summarize} method, and the performance of a list of retrievers and summarizers is investigated. Through extensive experiments, we identify variances in evaluation metrics and provide insights into their reliability. We also found that LLMs suffer great performance loss from retrieving errors. We further experimented methods to improve the performance as well as investigate their robustness against imperfect retrieval. We will release our data and code at https://github.com/yale-nlp/ODSum

RGBGrasp: Image-based Object Grasping by Capturing Multiple Views during Robot Arm Movement with Neural Radiance Fields

Robotic research encounters a significant hurdle when it comes to the intricate task of grasping objects that come in various shapes, materials, and textures. Unlike many prior investigations that heavily leaned on specialized point-cloud cameras or abundant RGB visual data to gather 3D insights for object-grasping missions, this paper introduces a pioneering approach called RGBGrasp. This method depends on a limited set of RGB views to perceive the 3D surroundings containing transparent and specular objects and achieve accurate grasping. Our method utilizes pre-trained depth prediction models to establish geometry constraints, enabling precise 3D structure estimation, even under limited view conditions. Finally, we integrate hash encoding and a proposal sampler strategy to significantly accelerate the 3D reconstruction process. These innovations significantly enhance the adaptability and effectiveness of our algorithm in real-world scenarios. Through comprehensive experimental validations, we demonstrate that RGBGrasp achieves remarkable success across a wide spectrum of object-grasping scenarios, establishing it as a promising solution for real-world robotic manipulation tasks. The demonstrations of our method can be found on: https://sites.google.com/view/rgbgras

Crop Area Estimation from UAV Transect and MSR Image Data Using Spatial Sampling Method

AbstractUsing remote sensing data to estimate crop area is efficient to a wide range of end-users, including government agencies, farmers and researchers. Moderate spatial resolution (MSR) image data are widely used to estimate crop area. But its accuracy can’t meet the demands of precision. Spatial sampling techniques integrated the strengths of remote sensing and sampling survey are being widely used. This method need large sample size which is cannot be guaranteed by remote sensing due to weather. The Unmanned Aerial Vehicle (UAV) can be used as an effective way to guarantee enough sample size. This paper proposed a spatial sampling method using MSR image classification results and UAV transects, a stratified random sampling method was proposed, area-scale (from MSR image classification) was used as auxiliary variable to guide the distribution of UAV transects, which had proved that 2% sampling ratio can make the crop area estimation accuracy more than 95% with a 95% confidence interval

A systematic review and meta-analysis of herbal medicine on chronic obstructive pulmonary diseases

Herbal medicine (HM) as an adjunct therapy has been shown to be promising for the chronic obstructive pulmonary disease (COPD). However, the role of herbs in COPD remains largely unexplored. In this present study, we conducted the systematic review to evaluate the efficacy of herbs in COPD. 176 clinical studies with reporting pulmonary function were retrieved from English and Chinese database. Commonly used herbs for acute exacerbations stage (AECOPD) and stable COPD stage (SCOPD) were identified. A meta-analysis conducted from 15 high quality studies (18 publications) showed that HM as an adjunct therapy had no significant improvement in pulmonary function (FEV1, FEV%, FVC, and FEV1/FVC) compared to conventional medicine. The efficacy of the adjunct HM on improving the arterial blood gas (PaCO2 and PaO 2) for AECOPD and SCOPD remains inconclusive due to the heterogeneity among the studies. However, HM as an adjunct therapy improved clinical symptoms and quality of life (total score, activity score, and impact score of St. George's Respiratory Questionnaire). Studies with large-scale and double-blind randomized controlled trials are required to confirm the role of the adjunct HM in the management of COPD. © 2014 Hai Yong Chen et al.published_or_final_versio

Flexible graphene-coated carbon fiber veil/polydimethylsiloxane mats as electrothermal materials with rapid responsiveness

Flexible electrothermal mats with rapid responsiveness were prepared by spray-coating of graphene nanoplates (GNP) acetone dispersion on carbon fiber veil and following curing of polydimethylsiloxane (PDMS) on the mats. Morphological feature, electrical property, and electrothermal behavior of the mats with different area density from 55 to 20 g m−2 were investigated. Scanning electronic microscope (SEM) confirmed that pristine graphene nanoplates were uniformly deposited on the surface of carbon fiber resulting in volume resistance decreased substantially. Compared with the carbon fiber veil without coated GNP, the electric heating behavior of graphene-coated carbon fiber/PDMS mats were improved largely, such as the stead-state maximum temperature reached 297 °C, the maximum heating rate reached 5°Cs−1 tested by an infrared camera, the maximum power density reached 11.11 kW m−2. The respond time from room temperature 25 °C–200 °C was only 40 s tested by infrared thermal image. Even under high twisting/bending state or continuous stepwise voltage changes, the graphene-coated carbon fiber/PDMS mats retained stable electrical heating performance in aspects of temperature responsiveness and steady-state maximum temperature

Surface-enhanced Raman Spectroscopy Facilitates the Detection of Microplastics < 1 μm in the Environment

Micro- and nanoplastics are considered one of the top pollutants that threaten the environment, aquatic life and mammalian (including human) health. Unfortunately, the development of uncomplicated but reliable analytical methods that are sensitive to individual microplastic particles, with sizes smaller than 1 μm, remains incomplete. Here, we demonstrate the detection and identification of (single) micro- and nanoplastics, by using surface-enhanced Raman spectroscopy (SERS), with Klarite substrates. Klarite is an exceptional SERS substrate; it is shaped as a dense grid of inverted pyramidal cavities, made of gold. Numerical simulations demonstrate that these cavities (or pits) strongly focus incident light into intense hotspots. We show that Klarite has the potential to facilitate the detection and identification of synthesized and atmospheric/aquatic microplastic (single) particles, with sizes down to 360 nm. We find enhancement factors of up to two orders of magnitude for polystyrene analytes. In addition, we detect and identify microplastics with sizes down to 450 nm on Klarite, with samples extracted from ambient, airborne particles. Moreover, we demonstrate Raman mapping as a fast detection technique for sub-micron microplastic particles. The results show that SERS with Klarite is a facile technique that has the potential to detect and systematically measure nanoplastics in the environment. This research is an important step towards detecting nanoscale plastic particles that may cause toxic effects to mammalian and aquatic life when present in high concentrations

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.</p

Lantern-shaped screw loaded with autologous bone for treating osteonecrosis of the femoral head

Background: Treatment for osteonecrosis of the femoral head (ONFH) in young individuals remains controversial. We developed a lantern-shaped screw, which was designed to provide mechanical support for the femoral head to prevent its collapse, for the treatment of ONFH. The purpose of this study was to investigate the efficacy and safety of the lantern-shaped screw loaded with autologous bone for the treatment of pre-collapse stages of ONFH. Methods: Thirty-two patients were randomly divided into two groups: the lantern-shaped screw group (core decompression and lantern-shaped screw loaded with autogenous bone) and the control group (core decompression and autogenous bone graft). During 36 months follow-up after surgery, treatment results in patients were assessed by X-ray and computed tomography (CT) scanning as well as functional recovery Harris hip score (HHS). Results: Successful clinical results were achieved in 15 of 16 hips (94%) in the lantern-shaped screw group compared with 10 of 16 hips (63%) in the control group (p = 0.0325). Successful radiological results were achieved in 14 of 16 hips (88%) in the lantern-shaped screw group compared with 8 of 16 hips (50%) in the control group (P = 0.0221). Conclusion: The lantern-shaped screw loaded with autologous bone for the treatment of pre-collapse stages of ONFH is effective and results in preventing progression of ONFH and reducing the risk of femoral head collapse

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.</p