bluuebunny/crossref_metadata_2025_split huggingface dataset:
Selection and validation of reference genes suitable for gene expression analysis by Reverse Transcription Quantitative Real-Time PCR in Acinetobacter baumannii
Authors: Ferraz Lúcio Fábio Caldas , de Oliveira Paloma Aparecida Alves , Baboghlian Juliana , Ramos Clarissa Orandina Aparecida , Mançano Alquiandra Stefani Ferreira , Porcari Andréia , Girardello Raquel 
Published: July 2023

Abstract
Acinetobacter baumannii is a Gram-negative bacterium considered an emerging multi-drug-resistant pathogen. Furthermore, this bacterium can survive in extreme environmental conditions, which makes it a frequent cause of nosocomial infection outbreaks. Gene expression analyses by Reverse Transcription Quantitative Real-Time PCR (RT-qPCR) depend on a reference gene, also called an endogenous gene, which is used to normalize the generated data and thus ensure an accurate analysis with minimal errors. Currently, gene expression analyses in A. baumannii are compromised, as there are no reports in the literature describing the identification of validated reference genes for use in RT-qPCR analyses. For this reason, we selected twelve candidate reference genes of A. baumannii and assessed their expression profile under different experimental and culture conditions. The expression stability of the candidate genes was evaluated by using statistical algorithms such as BestKeeper, GeNorm, NormFinder, Delta CT, and RefFinder, in order to identify the most suitable candidate reference genes for RT-qPCR analyses. The statistical analyses indicated rpoB, rpoD, and fabD genes as the most adequate to ensure accurate normalization of RT-qPCR data in A. baumannii. The accuracy of the proposed reference genes was validated by using them to normalize the expression of the ompA gene, encoding the outer membrane protein A, in A. baumannii sensible and resistant to the antibiotic polymyxin. The present work provides suitable reference genes for precise RT-qPCR data normalization on future gene expression studies with A. baumannii.
SFC-SMOTE: A sample filtering clustering oversampling algorithm for imbalanced clinical diagnostic data
Authors: Xu Zhaozhao , Yang Fangyuan , Wang Hong , Sun Junding , Wang Shuihua , Zhang Yudong 
Published: July 2023

Abstract
Background: Medical data mining is an important research direction in the data mining field and has always been a research hotspot in the computer and medical fields for many years. Data mining algorithms usually assume that the sample distribution of data is balanced and the misclassification cost is equal. However, due to the characteristics of medical research, there are serious sample imbalances in the clinical diagnostic data collected, which leads to poor classification effects of data mining algorithms.
Methods: For the problem of sample imbalance in clinical diagnosis data, this paper proposes a sample filtering clustering oversampling algorithm (SFC SMOTE). Firstly, the k-means algorithm is used to group the clinical diagnosis data so that the similarity of the samples within the group is high while the similarity of the samples between the groups is low. Then, the sample filtering strategy is used to identify and filter the "noisy samples" in the minority and the majority and merge the filtered samples of the two classes according to the original class of the samples. Finally, the dynamic rate strategy is set according to the original two-class sample numbers, and the minority and majority samples are dynamically synthesized to restore the original structure of the data.
Results: Experimental results show that the three classification algorithms have the best classification effect on the dataset sampled by the SFC SMOTE algorithm. Among them, the average Sensitivity, Specificity and MCC of Random forest are 92.48%, 92.07% and 84.57%, respectively, which are significantly better than the existing algorithms. In addition, the Wilcoxon and Friedman test results show that the SFC SMOTE algorithm is significantly better than the existing algorithms.
Conclusion: The classification effect of the three classification algorithms has been significantly improved after all sampling algorithms. In addition, the oversampling algorithm is significantly better than the clustering oversampling algorithm. More importantly, no matter which classification algorithm is used, the classification effect of the dataset sampled by the SFC SMOTE algorithm is better.
HOP Count Based Energy Efficient Cluster Head Selection Using Leveling and Sectoring
Authors: Nazeer Mohd 
Published: July 2023

Abstract

WSN are mostly deployed in sensitive location where battery replacement is impossible, which lead to implementation of an energy efficient protocol to extend network life time. In unsupervised machine learning, clustering based hierarchical routing protocols the dissemination of energy occurs mostly due to local communication and cluster head selection, roles of the nodes are changed as cluster member or cluster head to solve fusion and localization problem and manage energy dissemination and increasing the network life time. Nodes can communicate with each other directly or indirectly manner. In this paper the proposed LS-LEACH algorithm solved the problem of optimized cluster head selection by using leveling and sectoring protocol. In LS-LEACH the deployment area is divided into levels and sectors, and selection of the cluster head is done by using hop count matrix. LS-LEACH has been evaluated by performing simulation in OMNET + + simulator and equated with existing EERMS algorithm by considering different metric such as last node death, first node death and network life time. The proposed algorithm can be utilized in various smart IoT based application for optimal cluster head selection.
Accurate felt-tip pen brands classification based on a convolutional neural network using data augmentation
Authors: Huang Jiadong , Wang Xiaobin , Zhang Guangteng , Yang Lei 
Published: July 2023

Abstract
Ink analysis played an important role in document examination, but the limited dataset made it difficult for many algorithms to distinguish inks accurately. This paper aimed to evaluate the feasibility of two data augmentation(DA) methods, Gaussian noise data augmentation (GNDA) and extended multiplicative signal augmentation (EMSA), for the classification of felt-tip pen ink brands. Four brands of felt-tip pens were analysed using FTIR spectroscopy. Five classification models were used, convolutional neural network (CNN), K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The results showed that the datasets generated by GNDA and EMSA are similar to the original datasets and have some diversity. The EMSA method had optimal classification results when combined with CNN, with classification accuracy (ACC), precision (PRE), recall (REC) and F1 score reaching 99.86%, 99.87%, 99.86%, 99.86%, and 99.86%, compared with GNDA-CNN method(ACC = 80.90%, PRE = 87.34%, REC = 81.62%, F1 score = 79.23%). This study shows that when raw spectral data is small, DA methods can be combined with neural network models to identify ink brands effectively.
Lightweight Joint Loss 2D Pose Estimation Network Based onCM-RTMPose
Authors: Cao Zherui , Xue Tao , Zhou Qianming 
Published: July 2023

Abstract
Human pose estimation tasks often need to be deployed on edge devices. While existing humanpose estimation networks can achieve good accuracy, their complex network structure leads to slowinference speed, which is not suitable for real-time tasks. At the same time, the large model structureis not conducive to model deployment. To address this issue, increasingly lightweight networks havebeen proposed in recent years, but existing lightweight networks have a certain gap in model accuracycompared to traditional pose estimation networks. We enhance the feature extraction and keypointrefinement capabilities of the model by adding spatial attention mechanism and using the transformermodule Meta3D, which is more suitable for pose estimation tasks. We trained a lightweight networkCM-RTMpose on the COCO dataset, which achieved an AP value of 69.8%, surpassing most existinglightweight pose estimation networks. To demonstrate the effectiveness of our method, we conductedcorresponding ablation experiments on the COCO and OCHuman datasets. The experiments showedthat our method achieved an AP value of 66.1% on the OCHuman dataset, surpassing the baseline network.
Investigation on Barium Zirconate Nanocomposite for Dielectric Properties
Authors: Sivakumar Seenivasan 
Published: July 2023

AbstractThe barium zirconate (BaZrO3) Perovskite is of interest for various fields of applications. It has high corrosion resistance with respect to molten phases formed during the synthesis of copper-oxide-based super conducting single crystal. In the present investigation, ZrO2and BaZrO3were prepared by co-precipitation method. The samples were characterized by powder X-ray diffraction, FT-IR spectra and EDAX-shows the elemental analysis of the specimens. ZrO2and BaZrO3SEM micrograph of the ZrO2and BaZrO3shows the morphological structure of both of the samples it resembles a nano cluster of spherical particles like a flower. Most of the particles arranged like an agglomerates. The Photoluminescence spectra indicates the blue emission at zirconate. The red emission region is shifted of barium. The high ion exchange ability and redox moment must make it useful in many catalystic processes and also it made the material is useful for the flurosense lamp and flat panel displays. The dielectric constant and dielectric loss were determined as a function of frequency at 1 MHz. It is found to be 63.56 forzirconia and 78.24 for barium zirconate. Due to its high dielectric constant value barium zirconate may be useful in case of ferroelectric applications.
A Review of the Informal Economy Driving Forces and Policy Measures against it: An Analysis of Six EU Countries
Authors: Schneider Friedrich , Asllani Alban 
Published: July 2023

Abstract
This study provides estimates of the size and development of the shadow economy in the countries of the European Union (EU) plus the United Kingdom from 2003 to 2022 using a MIMIC approach. The focus of the study is to provide an analysis of the main factors that drive economic agents to enter shadow economy activities. Our results show that the size of the shadow economy has declined markedly between 2003 and 2021 for almost all countries of the EU including the UK. Our results indicate that the average size of the shadow economy in 27 EU countries plus the UK has decreased from 22.6% of GDP in 2003 to 17.3% in 2022. However, our results also reveal that the size of the shadow economy increased slightly in most EU countries as a result of the direct or indirect impact of the Coronavirus pandemic from 2020 onward. Our analysis and results reveal that weak institutional quality, ineffective government institutions, complex and burdensome tax and regulatory systems, the lack of strong legal systems, and pervasive corruption are the main determinants of economic informality in most countries of the EU. Moreover, the study reviews and elaborates on the main driving forces and the policy measures implemented to reduce the shadow economy with an exclusive focus on six EU countries (Germany, Austria, Denmark, Greece, Italy and Romania).
JEL Classification: O5, C39, C51, C82, H11, H26, U17.
Identification of six genes associated with COVID-19-related Circadian rhythm dysfunction by integrated bioinformatic analysis
Authors: Xu Yanfeng , Zhang Mingyu , Wang Guanyun , Yang Jigang 
Published: July 2023

Abstract
Patients with coronavirus disease 2019 (COVID-19) might cause long-term burden of insomnia, while the common pathogenic mechanisms are not elucidated. The gene expression profiles of COVID-19 patients and healthy controls were retrieved from the GEO database, while gene set related with circadian rhythm were obtained from Genecards database. 76 shared genes were screened and mainly enriched in cell cycle, cell division and cell proliferation, and 6 hub genes were found out including CCNA2, CCNB1, CDK1, CHEK1, MKI67 and TOP2A, with positive correlation to plasma cells. In the TF-gene regulatory network, NFYA, NFIC, MEF2A and FOXC1 showed high connectivity with hub genes. This study identified six hub genes and might provide new insights into pathogenic mechanisms and novel clinical management strategies.
Twin-boundary engineering boosted undercoordinated active sites for scalable conversion of CO2 to ethylene
Authors: Voiry Damien , Zhang Yang , Lyu Pengbo , Petit Eddy , WU Huali , Wang Wensen , Ma Jingyuan , Wang Ying , Zhang Xiaolin , Salameh Chrystelle , Qi Kun 
Published: July 2023

Abstract
The development of highly selective and energy efficient technologies for electrochemical CO2 reduction combined with renewable energy sources holds great promise for advancing the field of sustainable chemistry. The engineering of copper-based electrodes provides a pathway for the conversion of CO2 into high-value multicarbon products (C2+). However, the ambiguous determination of the intrinsic CO2 activity and the maximization of the density of exposed active sites have severely limited the use of Cu for the realization of practical electrocatalytic devices. Here, we report a scalable strategy to obtain a high density of undercoordinated sites by maximizing the exposure of twin-boundary active sites using a direct chronoamperometric pulse method. Our numerical investigations predicted that twin-boundaries modulate the adsorption behavior of *CO on the Cu surface, which acts as a key intermediate species associated with the production of multicarbon species. We investigated the consequence of twin-boundary density on dendric Cu catalysts (TB-Cu) by combining transmission electron microscopy, in-situ Raman and X-ray photoelectron spectroscopy with detailed electrochemical measurements. A linear relationship between the Faradaic efficiency of the C2+ product and the presence of under-coordinated sites was observed, which allowed to directly quantify the contribution of the twin-boundary in Cu-based catalysts on the CO2RR properties and the formation of multicarbon products. Using a membrane electrode assembly electrolyzer, the high twin-boundary density Cu electrodes achieve a maximum Faradaic efficiency of 73.2 % for C2+ product formation and a full cell energy efficiency of 20.2 % at a specific current density of 303.6 mA cm-2. The TB-Cu was implemented in a 25 cm2 MEA electrolyzer and demonstrated selectivity of over 62 % for 70 hours together with current retention of 88.4 % at the applied potential of -3.80 V. The catalysts and electrolyzer were further coupled to an InGaP/GaAs/Ge triple-junction solar cell to demonstrate a solar-to-fuel (STF) conversion efficiency of 8.33 %. This work opens new avenues for the design of an undercoordinated Cu-based catalyst for the realization of solar-driven fuel production.
Exogenous systemin peptide treatment in olive alters Bactrocera oleae (Diptera, Tephritidae) oviposition preference
Authors: Sánchez Luiza , Cunha Ana E. , Rodrigues Nuno , Pereira José Alberto , Baptista Paula 
Published: July 2023

Abstract
The olive fruit fly, Bactrocera oleae (Rossi), is a key pest of the olive crop, whose control relies mostly on the use of insecticides. Plant peptides may represent a more environmentally-friendly tool to manage olive fly, due to their recognized role to activate and/or prime plant defence responses against pests. In this work, behavioural experiments (no-choice and two-choice) and analysis of volatile compounds were carried out in order to evaluate the impact of the exogenous application of the peptide systemin to olive tree on olive fly infestation, and to elucidate its mode of action to prime plant defence. The treatment of olive branches with 10 nM systemin showed to confer protection against olive fly, by reducing significantly the ovipositions (up to 3.0-fold) and the number of infested fruits (up to 2.9-fold) when compared to not-treated branches. This protective effect was even detected in neighbouring not-treated branches, suggesting the ability of systemin to trigger plant-to-plant communication. The deterrent activity of the primed olives was associated with the emission of the volatiles 2-ethyl-1-hexanol, 4-tert-butylcyclohexyl acetate and 1,2,3-trimethyl-benzene, which were negatively correlated with oviposition and fly infestation. Systemin also showed to trigger the biosynthesis of specific volatiles (esters) in olives in response to fly attacks. Overall, the observed protection conferred by systemin against olive fly is likely due to the emission of specific volatiles that can act as a defence and/or as signalling molecules to upregulate the plant defence response. Thus, systemin represents a novel and useful tool to manage olive fruit fly.
On surface of Apollonius
Authors: Végh Attila 
Published: July 2023

Abstract
Apollonius defined the circle as the set of points that have a given ratio $\mu$ of distances from two given points, where $\mu\not=1$. Generally, consider two $0$-symmetric, bounded, convex bodies $K$ and $K'$, which define two norms, whose unit balls are $K$ and $K'$. The surface of Apollonius is the set of points equidistant from centers of bodies $K$ and $K'$ with respect to these norms. We prove that the surface of Apollonius of two ellipsoids is a quadratic surface and we examine when this surface becomes a sphere.
Mathematics Subject Classification (2010). Primary 51N20, 52A20; Secondary 51N10, 52A10.
Optimization on Manifold Injection in DI Diesel Engine Fuelled with Acetylene
Authors: Sonachalam M , Manieniyan V , Senthilkumar R , Sıngh J Anıx Joel 
Published: July 2023

Abstract
Researchers proved that implementing new combustion technology and optimizing fuel quantity results in a significant reduction in conventional fossil fuel usage as well as emission levels. One of the low temperature combustion technologies is the Reactivity Controlled Compression Ignition combustion strategy, which is used to lower the overall combustion temperature and provide better control over the combustion. This study focuses on Reactivity Controlled Compression Ignition combustion technology, which is fueled by conventional diesel fuel as a high reactivity fuel injected through the injector and acetylene gas as a low reactivity fuel injected through the modified inlet manifold along with air to the cylinder. The modified engine setup was carried out for performance, emission and combustion tests under different load conditions, as well as different mass flow rate of acetylene gas as a low reactivity fuel which is injected along with air. The flow field of the low reactivity fuel at the inlet manifold is analyzed using the Computational Fluid Dynamics principle, which is used to select the optimum flow rate to improve combustion quality. According to the simulation results, the optimized flow rate of acetylene is 3 Liters Per Minute, and according to the experimentation, at 3 Liters Per Minutes acetylene injection, the brake thermal efficiency is improved by about 3.7%, and emissions such as Carbon Monoxide, Hydro Carbon, smoke intensity, and Oxides of Nitrogen are reduced by about 35%, 17%, 10%, and 21%, respectively.
Deep embedding clustering based on residual autoencoder
Authors: Li Mengli , Cao Chao , Li Chungui , Yang Shuhong 
Published: July 2023

Abstract

Clustering algorithm is one of the most widely used and influential analysis techniques. With the introduction of deep learning, deep embedding clustering algorithm has been developed rapidly and achieved good results. Much depends on the potential expression captured by the autoencoder network. Therefore, the quality of the potential expression directly determines the performance of the algorithm. In view of this, researchers have proposed many improvements. Although the performance has been slightly improved, they all have one shortcoming, that is, too much emphasis is placed on the original data reconstruction ability during the process of feature expression, which greatly limits the further expression of potential features according to specific clustering tasks. Moreover, there is a large amount of noise in the original data, so blindly emphasizing reconstruction will only backfire. Hence, we innovatively propose a deep embedding clustering algorithm based on residual autoencoder (DECRA) after in-depth research. Specifically, a novel autoencoder network with residual structure is proposed and introduced into deep embedded clustering tasks. The network introduces an adaptive weight layer in feature representation z, which can make it have good robustness, generalization for specific tasks, and adaptive learning of better feature embeddings according to category classification. In this paper, the reasons for the validity of this structure are explained theoretically, and comprehensive experiments on five benchmark datasets including various types show that the clustering performance of the DECRA is very competitive and significantly superior to the most advanced methods.
Investigation of functionalized magnetic graphene oxide using glycine and hexamethylenediamine ligands to remove Pb 2+ from aqueous solutions
Authors: Zarei Mahmoud , Rostamzadeh Paria , Asgari Elham , Hosseinzadeh Mehdi 
Published: July 2023

Abstract
This work is reserved to the synthesis, characteristics, and evaluation of the adsorption efficiency of nanocomposite-based adsorbents based on functionalized magnetic graphene oxide. The chemical structure and bond formation, crystal structure, and pollutant absorption by the adsorbent were characterized by Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, and atomic absorption spectroscopy respectively. The optimization process was analyzed using CCD and RSM, taking into account four independent variables including adsorbent mass, initial pH solution, agitation time and Pb2+ concentration. A comparative analysis was carried out on functionalized magnetic graphene oxide samples with HMDA and Gly regarding Pb2+ removal. Investigating the kinetic and isotherm studies toward the adsorbent and its resumption in successive sorption and desorption cycles, it was observed that the adsorption processes follow second-order kinetics and Freundlich isotherm; thermodynamic studies, including Gibbs free energy changes, enthalpy, and entropy changes, show that the absorption process is spontaneous and exothermic. The maximum adsorption rate obtained have been found as 86% and 98.38% respectively for as-synthesized and functionalized adsorbent by Gly and HMDA with operational parameters including adsorbent mass (5–25 mg), initial solution pH (2–10), agitation time (5–45 min), and initial Pb2+ concentration (5–25 mg.L− 1), Therefore, the HMDA functionalized nanocomposite was realized to be an acceptable adsorbent for pb2+ from an aqueous solution.