The Challenges of O2 Detection in Biological Fluids: Classical Methods and Translation to Clinical Applications.

Dissolved oxygen (DO) is deeply involved in preserving the life of cellular tissues and human beings due to its key role in cellular metabolism: its alterations may reflect important pathophysiological conditions. DO levels are measured to identify pathological conditions, explain pathophysiological mechanisms, and monitor the efficacy of therapeutic approaches. This is particularly relevant when the measurements are performed in vivo but also in contexts where a variety of biological and synthetic media are used, such as ex vivo organ perfusion. A reliable measurement of medium oxygenation ensures a high-quality process. It is crucial to provide a high-accuracy, real-time method for DO quantification, which could be robust towards different medium compositions and temperatures. In fact, biological fluids and synthetic clinical fluids represent a challenging environment where DO interacts with various compounds and can change continuously and dynamically, and further precaution is needed to obtain reliable results. This study aims to present and discuss the main oxygen detection and quantification methods, focusing on the technical needs for their translation to clinical practice. Firstly, we resumed all the main methodologies and advancements concerning dissolved oxygen determination. After identifying the main groups of all the available techniques for DO sensing based on their mechanisms and applicability, we focused on transferring the most promising approaches to a clinical in vivo/ex vivo setting.

Gravitational Wave-Signal Recognition Model Based on Fourier Transform and Convolutional Neural Network.

The recent detection of gravitational waves is a remarkable milestone in the history of astrophysics. With the further development of gravitational wave detection technology, traditional filter-matching methods no longer meet the needs of signal recognition. Thus, it is imperative that we develop new methods. In this study, we apply a gravitational wave signal recognition model based on Fourier transformation and a convolutional neural network (CNN). The gravitational wave time-domain signal is transformed into a 2D frequency-domain signal graph for feature recognition using a CNN model. Experimental results reveal that the frequency-domain signal graph provides a better feature description of the gravitational wave signal than that provided by the time-domain signal. Our method takes advantage of the CNN's convolution computation to improve the accuracy of signal recognition. The impact of the training set size and image filtering on the performance of the developed model is also evaluated. Additionally, the Resnet101 model, developed on the Baidu EasyDL platform, is adopted as a comparative model. Our average recognition accuracy performs approximately 4% better than the Resnet101 model. Based on the excellent performance of convolutional neural network in the field of image recognition, this paper studies the characteristics of gravitational wave signals and obtains a more appropriate recognition model after training and tuning, in order to achieve the purpose of automatic recognition of whether the signal data contain real gravitational wave signals.

Structure of microprismatic Fresnel lenses for creating uniform focal images.

Flat Fresnel lenses are known to form a point image in the focal plane. However, several practical applications require transforming lens to concentrate a parallel light beam into a uniformly illuminated light circle. We previously proposed a novel algorithm for simulating such a transforming Fresnel concentrator. In this study, we applied this method to the diamond-cutting technique to create prismatic refractive surfaces of high optical quality. To reduce the discreteness of formed images, each refractive lens zone was fabricated from several small identical microprisms in the simulation. The new fabricated circular light beam concentrators were investigated by computer modelling and experimentally with a collimated laser beam.

Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction.

The atmospheric particulate matter (PM) with a diameter of 2.5 µm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.