Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments

Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and tracking of particles is a key source of error in PTV, especially for single camera defocusing, plenoptic imaging, and digital in-line holography (DIH) sensors. To address this issue, we developed stochastic particle advection velocimetry (SPAV): a statistical data loss that improves the accuracy of PTV. SPAV is based on an explicit particle advection model that predicts particle positions over time as a function of the estimated velocity field. The model can account for non-ideal effects like drag on inertial particles. A statistical data loss that compares the tracked and advected particle positions, accounting for arbitrary localization and tracking uncertainties, is derived and approximated. We implement our approach using a physics-informed neural network, which simultaneously minimizes the SPAV data loss, a Navier-Stokes physics loss, and a wall boundary loss, where appropriate. Results are reported for simulated and experimental DIH-PTV measurements of laminar and turbulent flows. Our statistical approach significantly improves the accuracy of PTV reconstructions compared to a conventional data loss, resulting in an average reduction of error close to 50%. Furthermore, our framework can be readily adapted to work with other data assimilation techniques like state observer, Kalman filter, and adjoint-variational methods

Understanding Translationese in Cross-Lingual Summarization

Given a document in a source language, cross-lingual summarization (CLS) aims at generating a concise summary in a different target language. Unlike monolingual summarization (MS), naturally occurring source-language documents paired with target-language summaries are rare. To collect large-scale CLS data, existing datasets typically involve translation in their creation. However, the translated text is distinguished from the text originally written in that language, i.e., translationese. In this paper, we first confirm that different approaches of constructing CLS datasets will lead to different degrees of translationese. Then we systematically investigate how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries. In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in real-world applications; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies. Lastly, we give suggestions for future CLS research including dataset and model developments. We hope that our work could let researchers notice the phenomenon of translationese in CLS and take it into account in the future.Comment: Accepted to the Findings of EMNLP 202

Machine learning and integrative analysis identify the common pathogenesis of azoospermia complicated with COVID-19

BackgroundAlthough more recent evidence has indicated COVID-19 is prone to azoospermia, the common molecular mechanism of its occurrence remains to be elucidated. The aim of the present study is to further investigate the mechanism of this complication.MethodsTo discover the common differentially expressed genes (DEGs) and pathways of azoospermia and COVID-19, integrated weighted co-expression network (WGCNA), multiple machine learning analyses, and single-cell RNA-sequencing (scRNA-seq) were performed.ResultsTherefore, we screened two key network modules in the obstructive azoospermia (OA) and non-obstructive azoospermia (NOA) samples. The differentially expressed genes were mainly related to the immune system and infectious virus diseases. We then used multiple machine learning methods to detect biomarkers that differentiated OA from NOA. Enrichment analysis showed that azoospermia patients and COVID-19 patients shared a common IL-17 signaling pathway. In addition, GLO1, GPR135, DYNLL2, and EPB41L3 were identified as significant hub genes in these two diseases. Screening of two different molecular subtypes revealed that azoospermia-related genes were associated with clinicopathological characteristics of age, hospital-free-days, ventilator-free-days, charlson score, and d-dimer of patients with COVID-19 (P < 0.05). Finally, we used the Xsum method to predict potential drugs and single-cell sequencing data to further characterize whether azoospermia-related genes could validate the biological patterns of impaired spermatogenesis in cryptozoospermia patients.ConclusionOur study performs a comprehensive and integrated bioinformatics analysis of azoospermia and COVID-19. These hub genes and common pathways may provide new insights for further mechanism research

Time-Space Relationship Analysis Model on the Bus Driving Characteristics of Different Drivers Based on the Traffic Performance Index System

With the extensive application of the concept of green traffic, the relationship between the driving characteristics of different drivers and energy consumption and traffic performance conditions, etc. is gradually becoming a research hotspot. Based on bus status data recorded by travel data recorders with a vehicle-mounted satellite positioning function and in view of external bus behaviours and driver’s performance, a bus driving characteristic model of drivers is established. A time-space analysis model of the driving characteristics of different drivers based on traffic performance index is also established through fuzzy association rules and a type-2 fuzzy set prediction algorithm. Test results show that the prediction algorithm can accurately describe the time-space relationship between the traffic congestion index and bus driving characteristic model and achieve relatively high prediction accuracy. The problem of the lagging release of traffic performance index caused by massive calculation for floating vehicle data can be effectively solved through this algorithm, which can serve as an important reference for analyzing traffic performance conditions, as well as the energy conservation and emission reduction of buses

Microbiome dysbiosis occurred in hypertrophic scars is dominated by S. aureus colonization

BackgroundThe mechanisms of hypertrophic scar formation and its tissue inflammation remain unknown.MethodsWe collected 33 hypertrophic scar (HS) and 36 normal skin (NS) tissues, and detected the tissue inflammation and bacteria using HE staining, Gram staining, and transmission electronic microscopy (TEM), in situ hybridization and immunohistochemistry for MCP-1, TNF-α, IL-6 and IL-8. In addition, the samples were assayed by 16S rRNA sequencing to investigate the microbiota diversity in HS, and the correlation between the microbiota and the indices of Vancouver Scar Scale(VSS)score.ResultsHE staining showed that a dramatically increased number of inflammatory cells accumulated in HS compared with NS, and an enhanced number of bacteria colonies was found in HS by Gram staining, even individual bacteria could be clearly observed by TEM. In situ hybridization demonstrated that the bacteria and inflammation cells co-localized in the HS tissues, and immunohistochemistry indicated the expression of MCP-1, TNF-α, IL-6, and IL-8 were significantly upregulated in HS than that in NS. In addition, there was a significantly different microbiota composition between HS and NS. At the phylum level, Firmicutes was significantly higher in HS than NS. At the genus level, S. aureus was the dominant species, which was significantly higher in HS than NS, and was strongly correlated with VSS indices.ConclusionMicrobiome dysbiosis, dominated by S. aureus, occurred in HS formation, which is correlated with chronic inflammation and scar formation, targeting the microbiome dysbiosis is perhaps a supplementary way for future scar management

Study on sensor fault instability prediction for the Internet of agricultural things based on largest Lyapunov exponent

U ovom se istraživanju primjenjuje algoritam najvećeg Lyapunovog eksponenta za predviđanje tipova greške u mreži bežičnog senzora "Interneta poljoprivrednih stvari". Podaci o greški u sustavu dobiveni su od Interneta poljoprivrednih stvari, koji se sastoji od mreže kalibriranog TDR senzora vlage tla u svrhu razvijanja modela za predviđanje nestabilnosti greške senzora na temelju algoritma najvećeg Lyapunovog eksponenta. U svrhu provjere primjenjivosti tog modela u predviđanju uzoraka za uvježbavanje pod različitim uvjetima, u ovom se istraživanju ispituje i uspoređuje takav algoritam s modelom C4.5 algoritma kao prikaza podataka o grešci za različite postotke uzoraka za uvježbavanje. Metoda najvećeg Lyapunovog eksponenta za predviđanje nestabilnosti primjenjuje se također na niz za uvježbavanje koji uglavnom uključuje normalne podatke. Algoritmom se postiže točnost predviđanja od 90,43 %, što je 5,55 % više nego kod algoritma C4.5 (84,88 %). Različiti algoritmi pokazuju određeni stupanj prilagodljivosti u različitim uvjetima primjene. Metodom najvećeg Lyapunovog eksponenta za predviđanje nestabilnosti postižu se bolji rezultati kad se koriste mnogi autentični primjeri. Rezultati testa prilagodljivosti primjene pokazuju da model predviđanja nestabilnosti greške senzora zasnovan na algoritmu najvećeg Lyapunovog eksponenta omogućuje pouzdan pristup za dobivanje informacija o greški senzora i predviđanje grešaka u Internetu Poljoprivrednih Stvari.This study uses the largest Lyapunov exponent algorithm to predict the fault types in the wireless sensor network of the Internet of Agricultural Things. System fault data are collected from the Internet of Agricultural Things, which is composed of a calibrated TDR soil moisture sensor network, to develop a sensor fault instability prediction model based on the largest Lyapunov exponent algorithm. To verify the applicability of this model in forecasting training samples under various conditions, this study tests and compares such algorithm with the C4.5 algorithm model as a fault data account for different percentages of training samples. The largest Lyapunov exponent instability prediction method is also applied on the training set that mostly comprises normal data. The algorithm achieves a prediction accuracy of 90,43 %, which is 5,55 % higher than that of the C4.5 algorithm (84,88 %). Different algorithms demonstrate a certain degree of adaptability in various application conditions. The largest Lyapunov exponent instability prediction method achieves better results when many accurate samples are used. The results from the application adaptability test show that the sensor fault instability prediction model based on the largest Lyapunov exponent algorithm provides a reliable approach for collecting sensor fault information collection and predicting faults in the Internet of Agricultural Things

A Systematic review of blockchain technology for government information sharing

Government information sharing (GIS) refers to that act of required or provided for duty government information, commercial information and public welfare information, and it is a basic issue of government services. However, the existing GIS has low transparency and is lack of flexibility between different departments. Aiming at such problems, this paper takes blockchain as a solution, and systematically summarizes the development of digital GIS, the advantages and challenges of blockchain and its theoretical research and practical applications. Specifically, it reviews e-government interactive structure, big data and other solutions, analyses their imperfections, and puts forward blockchain-based solutions. The blockchain improves government service efficiency and data security, meanwhile it faces challenges in throughput and supervision. In order to meet these challenges, researchers propose solutions based on three data exchange scenarios: government-to-government, government-to-enterprise and government-to-individual. In addition to researches, blockchain-based GIS is put into use. The electronic licensing project of Nanjing (China) government is taken as an example to illustrate that blockchain has the ability to openly and securely verify, track, and exchange data, so as to improve management ability and public service level of government agencies