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.
We propose an algorithm for simulating matrix parameters suitable for mass- manufacturing the microprismatic plane-focusing Fresnel lenses. These specialized lenses create a homogeneous light spot at ...the focus, distinguishing them from traditional point-focusing Fresnel lenses. Incorporating plane-focusing lenses into automatic control systems with four-plane photodetectors enables the formation of linear direction-finding characteristics for moving objects with an expanded bearing angle. In solar photovoltaic modules, these lenses offer optimal solutions for reducing internal ohmic losses, thereby mitigating module overheating caused by sunlight concentration on the module surface. However, plane-focusing lenses are not currently available in optical practice, necessitating their mass production. In this paper the geometrical parameters of our plane-focusing microprism devices are specialized for lens and matrix formation using diamond cutting technique. This facilitates mass manufacturing of the lenses via thermo-pressing method. The simulated lens parameters have been investigated using computer modeling with Solidworks 2016 and TracePro 7.3. Samples of plane-focusing microprismatic lenses were fabricated by thermal pressing of polycarbonate workpieces with a metal stamp-matrix created using our simulation data. The optical characteristics of manufactured lens samples were experimentally tested using a collimated laser beam.
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.
New method (the K-method) for calculation of characteristics of complex networks is proposed. The method is based on transformation of the initial network and subsequent application of the Kirchhoff ...rules. The field of application of the method for sparse networks (in which nodes have a cause–effect character of the so-called ”cognitive maps”) is proposed. Two new characteristics of concept nodes (”pressure” and ”influence”) having a semantic interpretation are proposed. The advantages of the proposed K-method include its computational simplicity (in comparison with other known algorithms) comparable with the task of enumerating subgraphs for sparse networks of relatively small size (in practice — several hundred nodes). At the same time, the results obtained with the help of the K-method for a real network are correlating well enough with the results obtained using the impulse method.
•The simplified calculation method similar to electrical engineering one is proposed.•The method is devoid of the main contradictions inherent in the impulse method.•New weight characteristics of the concept nodes are proposed: pressure and influence.•The results obtained by using the K-method are in accordance with the impulse method.
Publication of preferential information via online social networks has recently increased in business promotional activities. This activity involves consideration of the propagation of preferential ...information in online social networks as well as the effects of such propagation on shopping choices. This paper proposes the susceptible-lurker-super-normal-recovered (SEAIR) dynamics model to address the propagation process of preferential information in the Weibo network. In the proposed SEAIR model, super spreader and lurker user compartments are adopted, and entry and exit are available to and from every compartment, which more realistically reflects circumstances in Weibo. Dynamic equations of the SEAIR model are proposed based on the mean-field theory, and the basic reproductive ratio (threshold) R0 derived. Furthermore, it is proved that the information-free equilibrium state of the SEAIR model is locally and globally stable when R0 is less than one, at which point the propagation process tends to disappear; there is a unique endemic equilibrium state when R0 is greater than one. To verify the performance of the SEAIR model, a Baidu App promotion activity in Weibo is used as a real case study. Compared to the conventional susceptible–infected–recovered (SIR) model, numerical results show that the SEAIR model reduces the root mean square error (RMSE) and the mean absolute error (MAE) by 33.2% and 24.74%, respectively. The influence of the main parameters on the performance of the SEAIR model are also analyzed.
•A SEAIR model is developed for preferential information propagation in Weibo.•The super spreader and lurker compartments are included in the SEAIR model.•An enter rate and exit rate are set for all compartments.•Local and global stable states are proved, representing the information-free equilibrium state of SEAIR model.•The RMSE of the simulated result with the SEAIR model was reduced by 33.2% from that of the SIR model.
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.
We developed an improved algorithm for an indoor positioning system based on an image sensor. By using the algorithm that tilts the image sensor when it is placed at room corners, the authors reduced ...the positioning error at the corners and increased the effective positioning area. When in room corners, the image sensor is placed at an appropriate angle that allows the reception of light from all three light-emitting diodes (LEDs). The unknown sensor position is calculated from the geometrical relations among the LED images created on the tilted image sensor plane. According to the obtained experimental results, signal–to-noise ratio of the received signal can be improved when the image sensor tilts at certain angle at the corner. It was found that the effective range of the tilt angle of the image sensor is estimated to be within the range of 30°–60° at the corners. The root mean-square of the positioning error obtained with the developed algorithm is 0.6 mm when the image sensor is located within the effective positioning area. The effective positioning area was increased by 15.3% at the corner areas when the image sensor was tilted by the optimal angle.
In recent years, online social networks have become an important site for companies to promote their latest products. Consequently, evaluating how many clients are affected by preferential ...information distributed in online social networks has become essential. In this paper, a novel dynamic model called the follower super forwarder client (FSFC) model is proposed to address the spreading behavior of preferential information in online social networks. The mean field theory is adopted to describe the formulas of the FSFC model and the key parameters of the model are derived from the past forwarding data of the preferential information. The edge between a large-degree node to a small-degree node has a greater weight. In addition, two kinds of infection probabilities are adopted for large-degree node forwarders and small-degree node forwarders. To evaluate the performance of the FSFC model, preferential data published on the Sina microblog (www.weibo.com) for the Vivo smartphone, Alibaba’s Tmall shopping site, and the Xiaomi phone were selected as real cases. Simulation results indicate that the relative errors of the output of the FSFC model compared with the actual data are 0.0068% (Vivo smartphone), 0.0085% (Tmall), and 0.032% (Xiaomi phone), respectively. The results verify that the FSFC model is a feasible model for describing the spreading behavior of preferential information in online social networks.
•A spreading model for dissemination of preferential information is proposed.•Double infection rate parameters are adopted in the proposed model.•The proposed model achieves good results in BA networks of various sizes.
We propose an algorithm for computing the influence matrix and rank distribution of nodes of a weighted directed graph by calculating the nodes’ mutual impact. The algorithm of accumulative impact ...solves problems of dimension and computational complexity arising in the analysis of large complex systems. The algorithm calculates the mutual impact of each pair of vertices, making it possible to rank the nodes according to their importance within the system and to determine the most influential components. It produces results similar to those of the commonly used impulse method when applied to graphs that are impulse-stable in an impulse process, while overcoming the disadvantages of the impulse method in other situations. Results are always obtained regardless of impulse stability; they do not depend on the initial impulse, so that the initial values of the weights affect the calculation results. When elements in the adjacency matrix of the weighted directed graph are multiplied by a constant factor, scale invariance is not violated, and the full affect for each of the nodes scales proportionally. Several examples of analyses of weighted directed graphs, including one related to the practical problem of urban solid waste removal, are provided to demonstrate the advantages of the proposed algorithm.
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.
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