Characterizing and Modeling the Hydrodynamics of Shallow Spouted Beds

The hydrodynamics of shallow, conical spouted beds of heavy particles were experimentally studied to determine how they differ from previous spouted bed reports in the literature. Key experimental measurements included minimum spouting velocity, time-average and time-varying (dynamic) pressure drop, time-average fountain height and time-average gas velocity profile in the bed. New correlations were developed for minimum spouting velocity, time-average pressure drop and fountain height based on the experimental data. The time-average gas velocity profile measurements confirmed that the beds in the present study exhibited gas flow features that were at least qualitatively similar to those previously reported for other experimental conical spouted beds and predicted by detailed computational fluid dynamics models. At least some of the major features of the observed spouted bed pulsation behavior appear to be captured by a simple zone-based model of ordinary differential equations. The equations are derived from time-differential mass and momentum balances over 4 spatial zones: entrainment, spout, fountain, and annulus. The dynamic behavior of the model is dominated by the entrainment zone, which includes the effects of 3 key processes: 1) Granular particle flow from the annulus into the area immediately above the gas inlet; 2) Radial leakage of gas outward from the inlet zone in response to the inward flowing particles and; 3) Upward flow of the main part of the inlet gas and subsequent particle entrainment in response to the gas-particle drag. Recommendations are made for further improvements to the model

Orbital Angular Momentum Waves: Generation, Detection and Emerging Applications

Orbital angular momentum (OAM) has aroused a widespread interest in many fields, especially in telecommunications due to its potential for unleashing new capacity in the severely congested spectrum of commercial communication systems. Beams carrying OAM have a helical phase front and a field strength with a singularity along the axial center, which can be used for information transmission, imaging and particle manipulation. The number of orthogonal OAM modes in a single beam is theoretically infinite and each mode is an element of a complete orthogonal basis that can be employed for multiplexing different signals, thus greatly improving the spectrum efficiency. In this paper, we comprehensively summarize and compare the methods for generation and detection of optical OAM, radio OAM and acoustic OAM. Then, we represent the applications and technical challenges of OAM in communications, including free-space optical communications, optical fiber communications, radio communications and acoustic communications. To complete our survey, we also discuss the state of art of particle manipulation and target imaging with OAM beams

A lidar for detecting atmospheric turbulence based on modified Von Karman turbulence power spectrum

Introduction: Atmospheric turbulence is a kind of random vortex motion. A series of turbulent effects, such as fluctuation of light intensity, occur when laser is transmitted in atmospheric turbulence.Methods: In order to verify the possibility of detecting atmospheric turbulence by the Mie-scattering lidar, firstly, based on the power spectrum method, the Zernike polynomial method is used to simulate generation of the modified Von Karman turbulent phase screen by low-frequency compensation. By comparing the obtained phase structure function with the theoretical value, the accuracy of the method is verified. Moreover, the transmission process of the Gaussian beam from Mie-scattering lidar through the phase screen is simulated, and the transmission characteristics of the beam under modified Von Karman turbulence are obtained by analyzing the fluctuation of light intensity. Secondly, based on the guidance for simulation analysis, a Miescattering lidar system for detecting the intensity of atmospheric turbulence was developed in Yinchuan area, and the atmospheric turbulence profile was inverted by detected scintillation index.Results: The results show it is feasible to use the Zernike polynomial method perform the low-frequency compensation, and the compensation effect of low order is better than that of high order compensation. The scintillation index of simulation is consistent with the actual detection result, and has the very high accuracy, indicating that the atmospheric turbulence detection using Mie-scattering lidar is effective.Conclusion: These simulations and experiments play a significant guiding role for the similar lidar to detect atmospheric turbulence

miRFam: an effective automatic miRNA classification method based on n-grams and a multiclass SVM

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are ~22 nt long integral elements responsible for post-transcriptional control of gene expressions. After the identification of thousands of miRNAs, the challenge is now to explore their specific biological functions. To this end, it will be greatly helpful to construct a reasonable organization of these miRNAs according to their homologous relationships. Given an established miRNA family system (e.g. the miRBase family organization), this paper addresses the problem of automatically and accurately classifying newly found miRNAs to their corresponding families by supervised learning techniques. Concretely, we propose an effective method, <it>miRFam</it>, which uses only primary information of pre-miRNAs or mature miRNAs and a multiclass SVM, to automatically classify miRNA genes.</p> <p>Results</p> <p>An existing miRNA family system prepared by miRBase was downloaded online. We first employed <it>n</it>-grams to extract features from known precursor sequences, and then trained a multiclass SVM classifier to classify new miRNAs (i.e. their families are unknown). Comparing with miRBase's sequence alignment and manual modification, our study shows that the application of machine learning techniques to miRNA family classification is a general and more effective approach. When the testing dataset contains more than 300 families (each of which holds no less than 5 members), the classification accuracy is around 98%. Even with the entire miRBase15 (1056 families and more than 650 of them hold less than 5 samples), the accuracy surprisingly reaches 90%.</p> <p>Conclusions</p> <p>Based on experimental results, we argue that <it>miRFam </it>is suitable for application as an automated method of family classification, and it is an important supplementary tool to the existing alignment-based small non-coding RNA (sncRNA) classification methods, since it only requires primary sequence information.</p> <p>Availability</p> <p>The source code of <it>miRFam</it>, written in C++, is freely and publicly available at: <url>http://admis.fudan.edu.cn/projects/miRFam.htm</url>.</p

Analysis on the Influencing Factors of Transformation of Green Logistics Industry of Dangshan Pear Based on ISM

The state strongly advocates the construction of ecological civilization, which brings opportunities to the production and consumption of green products. The green stream of the development of the pear (sweet pear) is of great significance for the county in Suzhou city, Anhui province, which is a major economic crop. There are many disadvantages in the traditional pear logistics mode, and it is changing in the direction of green logistics. In this paper, the author USES the interpretation structure model (ISM) method to refine and analyze the factors affecting the development of green logistics of the pear, and summarizes the 12 influencing factors and their interrelationships through literature collection, data access and other methods, and establishes the Adjacency matrix and the Accessibility matrix, and constructs the Six-order interpretation structure model. Based on the analysis of the model, six suggestions are proposed to promote the development of green logistics transformation in Dangshan County, and provide references for the development of green agricultural logistics industry in Dangshan County and other areas in China

Three New Ranidae Mitogenomes and the Evolution of Mitochondrial Gene Rearrangements among Ranidae Species

Various types of gene rearrangements have been discovered in the mitogenoes of the frog family Ranidae. In this study, we determined the complete mitogenome sequence of three Rana frogs. By combining the available mitogenomic data sets from GenBank, we evaluated the phylogenetic relationships of Ranidae at the mitogenome level and analyzed mitogenome rearrangement cases within Ranidae. The three frogs shared an identical mitogenome organization that was extremely similar to the typical Neobatrachian-type arrangement. Except for the genus Babina, the monophyly of each genus was well supported. The genus Amnirana occupied the most basal position among the Ranidae. The [Lithobates + Rana] was the closest sister group of Odorrana. The diversity of mitochondrial gene arrangements in ranid species was unexpectedly high, with 47 mitogenomes from 40 ranids being classified into 10 different gene rearrangement types. Some taxa owned their unique gene rearrangement characteristics, which had significant implication for their phylogeny analysis. All rearrangement events discovered in the Ranidae mitogenomes can be explained by the duplication and random loss model

A brief review of hypernetworks in deep learning

Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression. Hypernets have shown promising results in a variety of deep learning problems, includ- ing continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning. Despite their success across different problem settings, there is currently no comprehensive review available to inform researchers about the latest developments and to assist in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example of training deep neural networks using hypernets and propose categorizing hypernets based on five design criteria: inputs, outputs, variability of inputs and outputs, and the architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general scenarios where hypernets can be effectively employed. Finally, we discuss the challenges and future directions that remain underexplored in the field of hypernets. We believe that hypernetworks have the potential to revolutionize the field of deep learning. They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks. Through this review, we aim to inspire further advancements in deep learning through hypernetworks

A brief review of hypernetworks in deep learning

Hypernetworks, or hypernets in short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression etc. Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning etc. Despite their success across different problem settings, currently, there is no review available to inform the researchers about the developments and to help in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example to train deep neural networks using hypernets and propose categorizing hypernets based on five design criteria as inputs, outputs, variability of inputs and outputs, and architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general scenarios where hypernets can be effectively employed. Finally, we discuss the challenges and future directions that remain under-explored in the field of hypernets. We believe that hypernetworks have the potential to revolutionize the field of deep learning. They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks. Through this review, we aim to inspire further advancements in deep learning through hypernetworks

Dynamic inter-treatment information sharing for heterogeneous treatment effects estimation

Existing heterogeneous treatment effects learners, also known as conditional average treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment information sharing, and data have to be split among potential outcome functions to train CATE learners which can lead to biased estimates with limited observational datasets. To address this issue, we propose a novel deep learning-based framework to train CATE learners that facilitates dynamic end-to-end information sharing among treatment groups. The framework is based on \textit{soft weight sharing} of \textit{hypernetworks}, which offers advantages such as parameter efficiency, faster training, and improved results. The proposed framework complements existing CATE learners and introduces a new class of uncertainty-aware CATE learners that we refer to as \textit{HyperCATE}. We develop HyperCATE versions of commonly used CATE learners and evaluate them on IHDP, ACIC-2016, and Twins benchmarks. Our experimental results show that the proposed framework improves the CATE estimation error via counterfactual inference, with increasing effectiveness for smaller datasets