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

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

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

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

A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing

Language models (LMs) like BERT and GPT have revolutionized natural language processing (NLP). However, privacy-sensitive domains, particularly the medical field, face challenges to train LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring the preservation of data privacy. In this study, we systematically evaluate FL in medicine across $2$ biomedical NLP tasks using $6$ LMs encompassing $8$ corpora. Our results showed that: 1) FL models consistently outperform LMs trained on individual client's data and sometimes match the model trained with polled data; 2) With the fixed number of total data, LMs trained using FL with more clients exhibit inferior performance, but pre-trained transformer-based models exhibited greater resilience. 3) LMs trained using FL perform nearly on par with the model trained with pooled data when clients' data are IID distributed while exhibiting visible gaps with non-IID data. Our code is available at: https://github.com/PL97/FedNLPComment: Accepted by KDD 2023 Workshop FL4Data-Minin

Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks

Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More specifically, we compute motif matrices to capture the local topological information, then use them in the GCN aggregation process. The generated embedding at each snapshot is a weighted average of embeddings within a time window, where the weights are learnable parameters. Since the trust networks is signed on each edge, balance theory is used to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature

Why Are the Disabled People Willing to Participate in Sports: Taking Chinese Disabled Table Tennis Players as the Object of Investigation?

Abstract In this paper, questionnaire survey and data analysis are the main research methods. Its main purpose is to try to answer the question &quot;why are the disabled people willing to participate in sports&quot; and to explore some of the important factors that affect the participation of persons with disabilities. The object of the study is the 83 disabled table tennis players in the national training base. &quot;Questionnaire of Motivation Adapted Athletes (AQAM)&quot; is an international standard. Through descriptive analysis and independent sample T test data analysis, this study concludes three points: 1) &quot;enhancing physical fitness&quot;, &quot;loving table tennis sport&quot; and &quot;winning the respect of others&quot; are the main reasons that contribute to the participation in sports of disabled persons; 2) The motives of male and female athletes with disabilities to participate in sports are quite different; 3) The sports participation motivation of persons with disabilities is positively related to their family circumstances