bluuebunny/crossref_metadata_2025_split huggingface dataset:
Blockchain technology based exchanged information security for demand‐side management of grid‐connected microgrid using model predictive control
Authors: Bonyani Masoud , Ghanbarian Mohammad Mehdi , Simab Mohsen 
Published: November 2022

AbstractIn the present study, the economic model predictive control (EMPC) scheme is presented for the time of use tariff application of photovoltaic and diesel generator backup system, which is connected to the grid‐connected microgrid. Through the use of the closed‐loop control system, the prior open‐loop optimal control is enhanced. Minimizing the grid energy and fuel costs by evaluating the fuel level restrictions in the diesel tank is the main goal of this study. To accommodate the restrictions among controllable variables, this control method fulfils the load requirements. To gain the benefits of feedback and predictions, optimal power scheduling is modelled as a control problem. Furthermore, Blockchain technology is applied to secure interchanged information in the grid to avoid manipulating data by attackers. Specifically, the analysis is divided into two scenarios. The first one takes place in the alternative style when a blackout happens between [7 am and 6 pm] h and the other occurs in a grid energy mode when the network is available over 24 h. Energy performance, cost savings, and daily revenue have all been improved by EMPC. As a result, the daily energy savings are up to 52%, whereas diesel energy does not go over 85%.
Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization
Authors: Zhang Jun , Li Zhao‐yang , Wang Ling‐zhi , Chen Ying‐pin 
Published: November 2022

AbstractMost of the information obtained by humans comes from colour images. However, salt‐and‐pepper noise (SPN) during signal acquisition, encoding, transmission, and decoding easily interferes with the quality of colour images. Most existing SPN denoising methods decompose a colour image into three independent matrices according to the colour channel and then recover each channel signal independently, ignoring the strong data correlation between channels. In addition, most existing SPN denoising methods apply only a single model‐driven or data‐driven approach and fail to take the advantages of their combination fully. Therefore, we first regard a colour image contaminated by SPN as the sum of an SPN tensor and a tensor with missing data. In this manner, we transform the denoising problem into a low‐rank tensor reconstruction problem. We then introduce a model‐driven‐based parallel matrix factorization low‐rank tensor reconstruction algorithm and a data‐driven‐based FFDNet denoising network to restore the colour image better. The proposed method not only enhances the similarity of the colour image channels but also explores the deep prior of the colour image to capture the image details. Finally, the proposed method is compared with some advanced denoising methods. The results show that the proposed method achieves a competitive denoising performance.
Car‐following trajectory data imputation with adversarial convolutional neural network
Authors: Zhao De , Zhang Yan , Wang Wei , Hua Xuedong , Yang Min 
Published: November 2022

AbstractMissing values in the vehicle trajectory data undermine its application in traffic modelling and simulation. Traditional methods for missing data imputation rely on neighbor points of the missing/distorted data point and consequently can hardly handle trajectory with consecutive data loss. To fill in this research gap, external data with latent information should be considered to enhance the imputation. Hence, the primary objective of this study is to find an effective approach to impute consecutive missing data in the vehicle trajectory making use of leading vehicle trajectory. We proposed a novel imputation adversarial convolutional neural network (IACNN) by extending the CNN model with a multi‐objective loss function and adversarial learning framework for vehicle trajectory data imputation. Performance of the model is evaluated on the commonly used trajectory dataset NGSIM with comparison to other baseline models. It turns out that the proposed IACNN outperforms other baseline models in most data loss scenarios, especially with consecutive data loss.
Lightning inductive coupling characteristics of parallel cables: Computation and measurement validation
Authors: Zhou Mi , Zhao Weihan , Zheng Shengquan , Guo Yongming , Yang Zehong , Wang Jianguo , Cai Li , Fan Yadong 
Published: November 2022

AbstractA simulation model composed of an interfering cable and a disturbed cable is established for studying the lightning inductive characteristics between parallel cables. Effects of the horizontal distance between cables (5–40 cm), their height difference (−6–32 cm), as well as the length of disturbed cable (1–100 m) are investigated for the cable induced voltage, which is measured at the resistive load connected between one terminal of the disturbed cable and the ground, when various lightning surge currents (8/20 μs, 5/320 μs, and 0.5 μs/100 kHz) are delivered into the interfering cable. Our findings show that an increase of horizontal distance between cables can give rise to an exponential decrease of the cable induced voltage, and that there exist linear correlations between either the height difference of two cables or the length of disturbed cable and the cable induced voltage, which reaches maximum when the cables are located at the same height and of equal length. Verification experiments for the effect of horizontal distance have also been made, and, in general, they all show a reasonable agreement with the simulations. A detailed theoretical explanation of obtained findings is provided as well.
Knowledge and Self-Reported Practice of Postnatal Mothers on Sudden Infant Death Syndrome Prevention: A Cross-Sectional Study
Authors: Antony Roseline Kallakavumkal , Saldanha Sandra Jyothi 
Published: November 2022

AbstractThe word “Sudden Infant Death Syndrome (SIDS)” is used for a sleeping infant, who has apparently been quite well, is found unexpectedly dead. There are varied discussions throughout the world trying to find the possible reasons and preventive measures toward these sudden deaths. However, limited studies are undertaken regarding knowledge of mothers related to SIDS. The present study aimed at identifying the knowledge gaps present among the postnatal mothers toward SIDS and assess their practices that may influence SIDS among infants. The research design was a descriptive cross-sectional design and a 107 postnatal mothers were recruited focusing on the aim of the study through purposive sampling technique. A structured knowledge questionnaire and self-reported practice checklist was prepared and validated from experts.The findings of the study revealed that the majority of subjects 57 (53.3%) had good knowledge with a mean score of 9.34 ± 1.83 (maximum score-15). The mean practice score on prevention of SIDS among subjects was 13.76 ± 1.13 (maximum score-16). There was no significant correlation between knowledge and practice (r = −0.38, p = 0.7). However, significant association was seen between knowledge on prevention of SIDS and baseline variables such as education (p = 0.001), occupation (p = 0.001), place of residence (p = 0.001), and family income (p = 0.01). Moreover, there was a significant association between self-reported practice and age of mothers (p = 0.03).The study findings showed good knowledge level among majority of the mothers and executed good practice toward care of the infant. However, 30.8% of the mothers had average to poor level of knowledge, which is significant and needs attention.
Facile self-assembly of sandwich-like MXene layered multiscale structure nanocomposite
Authors: Guo ZhiJin , Zhang Yan , Zhou JianPing , Sun DaQian , Li HongMei 
Published: November 2022

Abstract
V2CT
x
shows a low ion diffusion barrier, while the multiple oxidation states of vanadium allow V2CT
x
to participate in multi-electron redox reactions, which demonstrates a greater potential for electrochemical energy storage applications. However, the lateral arrangement of V2CT
x
lamellae tends to cause the accumulation and collapse of the structure. Herein, the two-dimensional layered V2CT
x
is modified with Ag+ and Cu2+, the V2CT
x
/Ag/Cu composite is successfully prepared. The Ag nanoparticles (NPs) and Cu NPs grown between the layers of V2CT
x
can effectively suppress the accumulation of the lamellae and ensure the smooth transfer of electrolyte ions and electrons between the layers. Meanwhile, the doping of Ag NPs and Cu NPs can enlarge the interlayer spacing of V2CT
x
, which can expose more active contact sites for electrolyte ions, shorten the diffusion path of electrolyte ions, and effectively improve the electrochemical performance of V2CT
x
. The Ag NPs and Cu NPs agglomerate in the delamination of V2CT
x
to form a conductive channel covering multiple layers, which facilitates cross-layer electron transfer and reduces the internal resistance of the V2CT
x
/Ag/Cu composite. Basis on the above reasonable structural designs, the internal resistance of the V2CT
x
/Ag/Cu composite is only 0.72 Ω, showing excellent diffusion ability of K+. The result represents a new step forward in exploring the electrochemical properties of two dimensional materials of V2CT
x
.
QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data
Authors: Ssekagiri Alfred , Jjingo Daudi , Lujumba Ibra , Bbosa Nicholas , Bugembe Daniel L , Kateete David P , Jordan I King , Kaleebu Pontiano , Ssemwanga Deogratius 
Published: November 2022

Abstract

Summary
Next-generation sequencing (NGS) enables reliable detection of resistance mutations in minority variants of human immunodeficiency virus type 1 (HIV-1). There is paucity of evidence for the association of minority resistance to treatment failure, and this requires evaluation. However, the tools for analyzing HIV-1 drug resistance (HIVDR) testing data are mostly web-based which requires uploading data to webservers. This is a challenge for laboratories with internet connectivity issues and instances with restricted data transfer across networks. We present QuasiFlow, a pipeline for reproducible analysis of NGS-based HIVDR testing data across different computing environments. Since QuasiFlow entirely depends on command-line tools and a local copy of the reference database, it eliminates challenges associated with uploading HIV-1 NGS data onto webservers. The pipeline takes raw sequence reads in FASTQ format as input and generates a user-friendly report in PDF/HTML format. The drug resistance scores obtained using QuasiFlow were 100% and 99.12% identical to those obtained using web-based HIVdb program and HyDRA web respectively at a mutation detection threshold of 20%.

Availability and implementation
QuasiFlow and corresponding documentation are publicly available at https://github.com/AlfredUg/QuasiFlow. The pipeline is implemented in Nextflow and requires regular updating of the Stanford HIV drug resistance interpretation algorithm.

Supplementary information
Supplementary data are available at Bioinformatics Advances online.
DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces
Authors: Réau Manon , Renaud Nicolas , Xue Li C , Bonvin Alexandre M J J 
Published: November 2022

Abstract

Motivation
Gaining structural insights into the protein–protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein–protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein–protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations.

Results
We have developed DeepRank-GNN, a framework that converts protein–protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN’s performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank.

Availability and implementation
DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN.

Supplementary information
Supplementary data are available at Bioinformatics online.
Feasible weighted projected principal component analysis for semi-parametric factor models
Authors: Choi Sung Hoon 
Published: November 2022

Summary
Various factor estimation procedures have been developed, based on the latent factor model. They often consider general conditions that allow for correlations and heteroscedasticity. However, the conventional principal components method does not efficiently estimate the parameters. It also does not accommodate additional covariates, which explain the unknown factors, even if they are available. In particular, a few aggregated macroeconomic variables can be used as covariates in diffusion index forecasts. To account for these features, I propose the feasible weighted projected principal component (WPPC) analysis, based on semi-parametric factor models, and also establish its asymptotic properties. In addition, I apply the WPPC method to the diffusion index forecasting model. Finally, I investigate the performance of the WPPC estimator in forecasting excess bond returns using US bond market and macroeconomic data.
Modeling the thermal analysis of the uncovered solar collectors using perforated absorbent plate
Authors: Anupong Wongchai , Treve Mark , Muda Iskandar , Sapaev I B , Arana Julio Francisco Jimenez , Alfilh Raed H C , Alvarez José Ricardo Nuñez , Almassi Morteza 
Published: November 2022

Abstract
Uncovered solar collectors using perforated absorbent plates are a variety of reception collectors that are used for solar heating of air for consumption in air conditioning systems and dryers. The thermal performance of these collectors is subject to various factors including diameter and step holes, air suction velocity and solar radiation. Using modeling, a proper evaluation of the effect of each of these parameters on the thermal performance of the collector can be obtained. In this paper, by using heat transfer and energy balance modeling in different components of the sample collector, the effect of each of these parameters is estimated by using the preheatment of building air, thermal efficiency and output temperature of the collector according to the parameters of solar radiation, air suction velocity, diameter and step of the holes of the absorbent plate. Also, the efficiency of heat exchange of perforated plates is estimated for air suction velocity, diameter and step of holes. The results show that increasing the suction rate of air and solar radiation increases the thermal efficiency of the collector. Also, efficiency of the heat exchange (εHX), which decreases with increasing suction speed, is due to the decrease in the output temperature of the collector because of the increase in the amount of inlet air to it, increasing the suction rate from 60 m/h to 160 m/h reduces the efficiency of the heat exchange by 30%.
International Standardized Nomenclature for Outbred Stocks of Laboratory Animals
Authors: Festing M , Kondo K , Loosli R , Poiley S M , Spiegel A 
Published: November 2022

Preface by the Secretary-General of ICLA
In accordance with the «Aims of ICLA» (ICLA Bulletin No. 26, March 1970) the Governing Board established in 1969 a Working Party to prepare an International Nomenclature System for Outbred Animals.
The members were: Professor, Dr. A. Spiegel, Federal Republic of Germany, chairman.Dr. M. Festing, United KingdomDr. K. Kondo, JapanDr. R. Loosli, SwitzerlandMr. S. Poiley, U.S.A.
The nomenclature rules, completed and approved by the ICLA Governing Board on 8 December 1971, are published herewith. I am convinced that this system will bring order out of the existing chaos. The system is an offer to the world laboratory animal science, particularly the breeders and users. Editors of scientific journals, catalogues, and indices all over the world are also encouraged to require and use animal stock identification by this system for outbred animals used in experimentation.
The ICLA Governing Board would have preferred to have seen an international centralization of symbol registration. However, the ICLA Secretariat has not got the capacity necessary for such a task and some practical solution to the registration problem will have to be found by the Governing Board. A final aim should then be for ICLA to publish a comprehensive world list of breeder symbols at intervals.
Oslo, January 1972
Stian Erichsen
Secretary-General
Finite difference schemes for a size structured coagulation-fragmentation model in the space of Radon measures
Authors: Ackleh Azmy S , Lyons Rainey , Saintier Nicolas 
Published: November 2022

Abstract
We study a size-structured coagulation-fragmentation model formulated in the space of Radon measures. We reformulate this model as a mass conservation law on this space and establish its well-posedness. We develop and compare multiple finite difference schemes. In particular, we develop a fully explicit scheme and a semi-implicit scheme for the structured coagulation-fragmentation model, and we develop an explicit scheme based on the conservation law formulation. We prove convergence for each scheme and test the schemes against multiple well-known examples. We analyze and compare important properties of each scheme, such as mass conservation, order of convergence and computation time.
Virtual element method for the Navier–Stokes equation coupled with the heat equation
Authors: Antonietti Paola F , Vacca Giuseppe , Verani Marco 
Published: November 2022

Abstract
We consider the virtual element discretization of the Navier–Stokes equations coupled with the heat equation where the viscosity depends on the temperature. We present the virtual element discretization of the coupled problem, show its well-posedness and prove optimal error estimates. Numerical experiments that confirm the theoretical error bounds are also presented.
One Minute of EEG Data Provides Sufficient and Reliable Data for Identifying Lewy Body Dementia
Authors: Jin Lucy , Nawaz Huma , Ono Kenichiro , Nowell Justin , Haley Erik , Berman Brian D. , Mukhopadhyay Nitai D. , Barrett Matthew J. 
Published: November 2022

Objective:
To determine the minimum duration of electroencephalography (EEG) data necessary to differentiate EEG features of Lewy body dementia (LBD), that is, dementia with Lewy bodies and Parkinson disease dementia, from non-LBD patients, that is, Alzheimer disease and Parkinson disease.

Methods:
We performed quantitative EEG analysis for 16 LBD and 14 non-LBD patients. After artifact removal, a fast Fourier transform was performed on 90, 60, and thirty 2-second epochs to derive dominant frequency; dominant frequency variability; and dominant frequency prevalence.

Results:
In LBD patients, there were no significant differences in EEG features derived from 90, 60, and thirty 2-second epochs (all P>0.05). There were no significant differences in EEG features derived from 3 different groups of thirty 2-second epochs (all P>0.05). When analyzing EEG features derived from ninety 2-second epochs, we found that LBD had significantly reduced dominant frequency, reduced dominant frequency variability, and reduced dominant frequency prevalence alpha compared with the non-LBD group (all P<0.05). These same differences were observed between the LBD and non-LBD groups when analyzing thirty 2-second epochs.

Conclusions:
There were no differences in EEG features derived from 1 minute versus 3 minutes of EEG data, and both durations of EEG data equally differentiated LBD from non-LBD.
The Influence of Vaginal Microbiome and Clinical Factors on HPV Clearance: A Prospective Study
Authors: Yang Zhongzhou , Wu Hao , Wang Xiaohan , Tong Guoqing , Huang Zhuoqi , Wang Jie , Jiang Yuxin , Cao Min , Wang Yue , Deng Xingxing , Liu Na , Qi Le , Liu Mengping , Hutchins Andrew , Yao Bin , Shi Mang , Li Yantao , Zhu Shida 
Published: November 2022

AbstractBackgroundAlthough the microbiome and lifestyle factors are associated with HPV clearance, few studies have systematically explored the relevant factors. This formal follow-up prospective study aims to predict HPV clearance based on vaginal microbiota and lifestyle clinical factors.Participants and MethodsParticipants were recruited through a digital eHealth platform. Participants were unvaccinated for HPV and were assessed at baseline and a follow-up consultation between August 2021 and January 2022. Both clinical factors and cervicovaginal mucus (CVM) samples were collected from each participant. CVM samples were used to detect HPV and characterize vaginal microbiome by metagenomics. Lifestyle clinical factors were grouped into low-, middle-, and high-risk to operate the stratified analysis as well as survival analysis for HPV clearance.ResultsWe recruited 141 HPV-positive women at baseline. For the first follow-up, there were 116 HPV persistent infection and 25 HPV clearance participants. Among 28 clinical factors, six factors were identified as significantly associated: age, age of first sexual intercourse, diet balance, marital status, abortion and physical activity under the stratified analysis. Those with a middle-risk diet balance had an odds ratio (OR) (3.91, 95% CI: 1.02-28.03). Those with extremely high-risk diet balance also had a high OR (11.26, 95% CI: 1.35-122.14), but with a faster and higher proportion of HPV clearance. Conversely, clinical factors with low risk and correspondingly faster HPV clearance were physical activity, sleep quality, depression, and anxiety. Although HPV clearance was unrelated to each microbiome specie, HPV clearance was related to the lower overall diversity of species in the vaginal microbiome and the larger abundance oflactobacillus inners.ConclusionsThis study systematically depicts HPV clearance influenced by clinical factors and its relationship with the vaginal microbial ecosystem. HPV clearance can be improved by modulation of lifestyle habits and marital relationship. The findings from this prospective study have implications for the future design of guidelines to control cervical cancer or other HPV-related cancer, and therefore might be beneficial to women infected with HPV.
The Open Language Archives Community: a 20-year update
Authors: Bird Steven , Simons Gary F. 
Published: November 2022

PurposeThis paper reports on the first 20 years of the Open Language Archives Community (OLAC), comprehensive infrastructure for indexing and discovering language resources.Design/methodology/approachWe begin with the original vision, assess progress relative to the original requirements, and identify ongoing challenges.FindingsBased on the overview of OLAC history and recent developments and on the analysis of the situation in the language archives area as a whole, the authors propose an agenda for a more sustainable future for open language archiving.Originality/valueThis paper examines the progress of OLAC and discusses improvements in such areas as participation, access, and sustainability.