bluuebunny/crossref_metadata_2025 huggingface dataset:
Ultradispersed Mo/TiO2catalysts for CO2hydrogenation to methanol
Authors: Len Thomas , Bahri Mounib , Ersen Ovidiu , Lefkir Yaya , Cardenas Luis , Villar-Garcia Ignacio J. , Pérez Dieste Virginia , Llorca Jordi , Perret Noémie , Checa Ruben , Puzenat Eric , Afanasiev Pavel , Morfin Franck , Piccolo Laurent
Published: August 2021

Molybdenum species highly dispersed on selected titania materials catalyse CO2hydrogenation to methanol.
Cellulose nanocrystals from ionic liquids: a critical review
Authors: Shamshina Julia L. , Abidi Noureddine
Published: August 2021

The review highlights advantages and shortcomings of the synthetic methods for the preparation of cellulose nanocrystals, and describes their attributes such as surface morphology, size, crystallinity, and thermal properties.
Correction: Hafnium–zirconium oxide interface models with a semiconductor and metal for ferroelectric devices
Authors: Chae Kisung , Kummel Andrew C. , Cho Kyeongjae
Published: August 2021

Correction for ‘Hafnium–zirconium oxide interface models with a semiconductor and metal for ferroelectric devices’ by Kisung Chae et al., Nanoscale Adv., 2021, DOI: 10.1039/d1na00230a.
Prominent nonequilibrium effects beyond the standard first-principles approach in nanoscale electronic devices
Authors: Jiang Zhuoling , Yam Kah-Meng , Guo Na , Zhang Lishu , Shen Lei , Zhang Chun
Published: August 2021

With steady-state density functional theory (SS-DFT), we predict a novel type of nonequilibrium effect that is beyond the DFT based method, which explains ‘puzzling’ transport properties of silane molecules observed in experiments.
Developing deep blue (CIEy < 0.08) and pure blue (CIEy < 0.11) OLEDs via molecular engineering of carbazole moiety
Authors: Wei Wenkui , Li Jiuyan , Liu Di , Mei Yongqiang , Lan Ying , Tian Houru , Niu Rui , Liu Botao
Published: August 2021

Through carbazole modification, two new emitters with different emitting mechanisms were synthesized to obtain deep blue and pure blue OLEDs with CIE coordinates of (0.164, 0.077) and (0.157, 0.107) and EQEmax of 4.6% and 8.7%, respectively.
Time-dependent air quality and pollutant concentration in the Jingjinji region: future gas capture by green solvents
Authors: Chen Yu , Dai Fucai , Duan Yaoting , Ji Guipeng , Li Zhenyang , Liu Cong , Zhang Jixiang , Bai Yue , Wang Xin
Published: August 2021

Time-dependent air quality, pollutant concentration, major pollutants and pollution level in Jingjinji (around Beijing) from 2016 to 2020 are analyzed.
A tetrahedral DNA nanorobot with conformational change in response to molecular trigger
Authors: Liu Fengyu , Liu Xiaoming , Shi Qing , Maffeo Christopher , Kojima Masaru , Dong Lixin , Aksimentiev Aleksei , Huang Qiang , Fukuda Toshio , Arai Tatsuo
Published: August 2021

Using multiple parallel folding to aid the DOS folding into the TDN, which can respond to epithelial cell adhesion molecules (EpCAM) for a controlled conformational change.
Revealing self-aligned γ-SnTe ultrathin nanosheets in thermoelectric β-SnTe
Authors: Zhang Hu , Cheng Shao-Dong , Lu Lu , Mi Shao-Bo
Published: August 2021

An atomic-scale understanding of nanoscale precipitates in thermoelectric materials will help us explore their microstructure-property relationship, providing a strategy to optimize their thermoelectric properties.
Stimuli-responsive transmembrane anion transport by AIE-active fluorescent probes
Authors: Akhtar Nasim , Biswas Oindrila , Manna Debasis
Published: August 2021

Anticancer drug resistance implicates multifunctional mechanisms, and hypoxia is one of the key factors in therapeutic resistance. Herein, we report the development of AIE-active Cl− ion transporter which get activated under hypoxic environment.
Stereogradient polycaprolactones formed by asymmetric kinetic resolution polymerization of 6-methyl-ε-caprolactone
Authors: Lv Chengdong , Xu Guangqiang , Yang Rulin , Zhou Li , Wang Qinggang
Published: August 2021

Chiral phosphoric acid catalyzed asymmetric kinetic resolution polymerizations of 6-methyl-ε-caprolactone were developed to synthesize stereogradient polycaprolactones.
A visible-light photoredox-catalyzed four-component reaction for the construction of sulfone-containing quinoxalin-2(1H)-ones
Authors: Lv Yufen , Luo Jinyun , Lin Muze , Yue Huilan , Dai Bin , He Lin
Published: August 2021

A visible-light photoredox-catalyzed four component reaction of quinoxalin-2(1H)-ones, alkenes, aryldiazonium, and sodium metabisulfite leading to sulfone-containing quinoxalin-2(1H)-ones has been developed.
A palladium-catalyzed Heck/[4 + 1] decarboxylative cyclization cascade to access diverse heteropolycycles by using α-bromoacrylic acids as C1 insertion units
Authors: Zhang Minghao , Zhou Fengru , Xuchen Xinyu , Zhou Liwei , Deng Guobo , Liang Yun , Yang Yuan
Published: August 2021

A novel palladium-catalyzed Heck/[4 + 1] decarboxylative cyclization cascade of alkene-tethered aryl iodides by employing α-bromoacrylic acids as C1 insertion units is reported.
Electrochemical sensing platform for the simultaneous femtomolar detection of amlodipine and atorvastatin drugs
Authors: Kokab Tayyaba , Shah Afzal , Khan M. Abdullah , Nisar Jan , Ashiq Muhammad Naeem
Published: August 2021

Development of an ultra-sensitive electrochemical platform for the simultaneous detection of two high blood pressure drugs.
A facile approach to synthesize CdS–attapulgite as a photocatalyst for reduction reactions in water
Authors: Ma Ruixiao , Xie Liyan , Huang Yixuan , Zhuo Kangji , Xu Juan , Zhang Yanhui
Published: August 2021

The application of CdS–attapulgite composites in photocatalytic reduction of p-nitrophenol and Cr(vi) demonstrated that the attapulgite could overcome the limitations of CdS.
A novel bio-electro-Fenton system with dual application for the catalytic degradation of tetracycline antibiotic in wastewater and bioelectricity generation
Authors: Soltani Fatemeh , Navidjouy Nahid , Khorsandi Hassan , Rahimnejad Mostafa , Alizadeh Saber
Published: August 2021

In this new insight, the potential application of the Bio-Electro-Fenton system was surveyed with the aim of simultaneous degradation of tetracycline and in situ generation of renewable bioenergy without the need for an external electricity source.
Design, synthesis, and herbicidal activity of sec-p-menthane-7-amine derivatives as botanical herbicides
Authors: Zhang Hongmei , Chen Yuxiang , Xu Shichao , Wang Jing , Dong Huanhuan , Zhao Zhendong , Jiang Jianxin
Published: August 2021

In this study, a series of novel sec-p-menthane-7-amine derivatives were designed, synthesized, and evaluated to be used as efficient botanical herbicides owing to their post-emergence herbicidal activities against barnyard grass and rape.
Neuroinflammatory inhibitors from Gardneria nutans Siebold & Zuccarini
Authors: Si Ying-Ying , Wang Wei-Wei , Feng Qing-Mei , Zhao Zhen-Zhu , Xue Gui-Min , Sun Yan-Jun , Feng Wei-Sheng , Young Jun-Im , Wang Xian-Shi
Published: August 2021

Four new compounds were isolated from G. nutans. Compounds 1–2 are two rare monoterpene indole alkaloids with the glucosyl moiety located at C-12 and represent the first two examples of enantiomer of ajmaline type monoterpene indole alkaloids.
Preservice teachers’ enactment of formative assessment using rubrics in the inquiry-based chemistry laboratory
Authors: Zemel Yoram , Shwartz Gabby , Avargil Shirly
Published: August 2021

In recent years teachers’ education programs encourage preservice teachers to practice a variety of assessment methods to prepare them to be highly qualified practitioners who are capable in enhancing students’ scientific understanding. Formative assessment (FA) – also known as assessment for learning – involves the process of seeking and interpreting evidence about students’ ideas and actions to enhance and guide the learning process. An inquiry-based chemistry laboratory was chosen as the context of this research in which 13 preservice teachers studied the practice and application of FA. The preservice teachers evaluated students’ lab reports using two components of assessment – rubric-based scoring and providing students with feedback comments. Our goal was to understand whether guidance provided through the teacher education program affected preservice teachers’ FA enactment, which was reflected in their score variation and the quality of the written feedback comments provided to students. The study findings show that the total lab report score variation decreased in the 2nd assessment due to the explicit guidance. That is, the guidance provided the preservice teachers the opportunity to examine, discuss, and improve their own assessment knowledge and scoring process. However, the rubric dimensions that were perceived by preservice teachers as more open for discussion and interpretation – such as evidence-generating analysis and formulating conclusions – were challenging to assess and the explicit guidance created different thinking directions that led to increased variation scores. In these dimensions the guidance exposed the preservice teachers to the complexity of a rubric-based scoring in a FA manner. We recommend that the guidance preservice teachers receive regarding FA of inquiry-based lab reports, should include aspects of how to notice and interpret students’ ideas and only then respond with a formative feedback. The results of our study expand the theoretical knowledge regarding FA and have important implications for the preparation of future chemistry teachers and for the professional development of those already teaching chemistry in a classroom environment.
Substrate-independent three-dimensional polymer nanosheets induced by solution casting
Authors: Park Cheol Hun , Go Eun Min , Lee Kyung Min , Lee Chang Soo , Kwak Sang Kyu , Kim Jong Hak
Published: August 2021

Unprecedented substrate-independent polymeric 3D nanosheets were inducedviasimple solution casting using PEGBEM–POEM comb copolymer. A possible mechanism is the change in the polymer–solvent interactions on the surface.
Controlled viscoelastic particle encapsulation in microfluidic devices
Authors: Shahrivar Keshvad , Del Giudice Francesco
Published: August 2021

We provide the first experimental evidence of viscoelastic particle encapsulation with single-particle encapsulation efficiency up to 2-fold larger than the stochastic value.
Catalytically active sites of MOF-derived electrocatalysts: synthesis, characterization, theoretical calculations, and functional mechanisms
Authors: Zhai Zibo , Yan Wei , Dong Li , Deng Shuqi , Wilkinson David P. , Wang Xiaomin , Zhang Lei , Zhang Jiujun
Published: August 2021

MOF-derived catalysts are discussed in the atomic-level active sites. Advanced characterizations and DFT calculations are summarized for fundamental understanding of catalytic mechanisms. Challenges are analyzed and research directions are proposed.
Design of pre-catalysts for heterogeneous CO2 electrochemical reduction
Authors: He Jingfu , Wu Chenghui , Li Yanming , Li Changli
Published: August 2021

This work reviews the recent advances of pre-catalysts for CO2 reduction reaction (CO2RR) research. The important factors that may be responsible for the improvement of the CO2RR performance are categorized and a perspective is also presented.
Discharge voltage profile changes via physicochemical phenomena in cycled all-solid-state cells based on Li10GeP2S12 and LiNbO3-coated LiCoO2
Authors: Sun Xueying , Yamada Yuto , Hori Satoshi , Li Yuxiang , Suzuki Kota , Hirayama Masaaki , Kanno Ryoji
Published: August 2021

The influences of contact loss and chemical reaction in the cathode of an all-solid-state cell on the discharge profile were separated and physicochemically characterised in detail, thereby deepened the understanding of the degradation mechanism.
A treasure map for nonmetallic catalysts: optimal nitrogen and fluorine distribution of biomass-derived carbon materials for high-performance oxygen reduction catalysts
Authors: Zhao Yaling , Liu Yang , Chen Ye , Liu Xupo , Li Xiaoge , Gao Shuyan
Published: August 2021

The intrinsic laws of the catalytic performance and heteroatom distribution of a nonmetallic biomass-derived carbon material were revealed systematically, providing a new idea for high-throughput screening of heteroatom-doped catalysts.
Aminopeptidase N-targeting nanomolecule-assisted delivery of VEGF siRNA to potentiate antitumour therapy by suppressing tumour revascularization and enhancing radiation response
Authors: Li Xue , Li Jiang , Li Chunyin , Guo Qi , Wu Menglin , Su Lin , Dou Yan , Wu Xinhong , Xiao Zhaoxun , Zhang Xuening
Published: August 2021

Benefitting from superimposed effect of enhanced radiosensitizing and down-regulated angiogenic pathway, tGd–GNMssiRNA complex + X-ray treatment improves the tumouricidal effect and provide real-time imaging information to evaluate the antitumour effects.
3D/4D printed tunable electrical metamaterials with more sophisticated structures
Authors: Yang Dou , Mei Hui , Yao Li , Yang Wenqiang , Yao Yongtao , Cheng Laifei , Zhang Litong , Dassios Konstantinos G.
Published: August 2021

Absorbing metamaterials with more sophisticated structures printed using various 3D printers.
The carbon–carbon triple bond as a tool to design organic semiconductors for photovoltaic applications: an assessment of prospects and challenges
Authors: Seri Mirko , Marrocchi Assunta
Published: August 2021

This Perspective discusses the developments, opportunities, and challenges in the context of the use of alkyne-based organic semiconductors in photovoltaic devices.
Double agent indole-3-acetic acid: mechanistic analysis of indole-3-acetaldehyde dehydrogenase AldA that synthesizes IAA, an auxin that aids bacterial virulence
Authors: Shah Ateek , Mathur Yamini , Hazra Amrita B.
Published: August 2021

The large diversity of organisms inhabiting various environmental niches on our planet are engaged in a lively exchange of biomolecules, including nutrients, hormones, and vitamins. In a quest to survive, organisms that we define as pathogens employ innovative methods to extract valuable resources from their host leading to an infection. One such instance is where plant-associated bacterial pathogens synthesize and deploy hormones or their molecular mimics to manipulate the physiology of the host plant. This commentary describes one such specific example—the mechanism of the enzyme AldA, an aldehyde dehydrogenase (ALDH) from the bacterial plant pathogen Pseudomonas syringae which produces the plant auxin hormone indole-3-acetic acid (IAA) by oxidizing the substrate indole-3-acetaldehyde (IAAld) using the cofactor nicotinamide adenine dinucleotide (NAD+) (Bioscience Reports (2020) 40(12), https://doi.org/10.1042/BSR20202959). Using mutagenesis, enzyme kinetics, and structural analysis, Zhang et al. established that the progress of the reaction hinges on the formation of two distinct conformations of NAD(H) during the reaction course. Additionally, a key mutation in the AldA active site ‘aromatic box’ changes the enzyme’s preference for an aromatic substrate to an aliphatic one. Our commentary concludes that such molecular level investigations help to establish the nature of the dynamics of NAD(H) in ALDH-catalyzed reactions, and further show that the key active site residues control substrate specificity. We also contemplate that insights from the present study can be used to engineer novel ALDH enzymes for environmental, health, and industrial applications.
Training for communication in forensic science
Authors: Hoffman Brenda L. , Hackman Lucina , Lindenfeld Laura A.
Published: August 2021

Scientists are increasingly becoming better prepared to communicate science in a variety of different settings, yet significantly less attention has been paid to communicating science in the courtroom, a setting which carries major societal impact. This article explores key issues surrounding science communication in the courtroom. We outline a conceptual system for communication training that includes ideas about fostering greater collaboration across different stakeholder groups, and training expert witnesses to communicate scientific evidence in ways that are accessible and accurate. Critical to this concept is supporting communication that upholds the integrity of the science, while also maintaining expectations for interactions in the courtroom.
Creating Culturally Supportive Settings That Foster Literacy Development: Guidance for SLPs
Authors: Knight Carrie , Gregory Kyomi , Nellenbach Kristin
Published: August 2021

Purpose

The purpose of our tutorial is to provide a set of fundamental, research-based guidelines for speech-language pathologists (SLPs) who want to begin the process of creating culturally supportive settings that foster adolescent literacy development.

Method

Pertinent literature related to culturally relevant literacy practices for SLPs is reviewed. SLPs perceived barriers to providing general literacy services in schools and levels of cultural competence are identified, along with articles that support service ideas to meet the literacy-based needs of culturally diverse students on our caseloads.

Results

Drawing on the literature base and our collective research and clinical experiences, we propose four key guidelines SLPs can adopt to support their initial efforts in building culturally supportive settings: develop a shared vocabulary and accompanying concepts, curate and use culturally relevant texts, establish ongoing dialogue with key groups, and set high expectations.

Conclusions

SLPs are in a unique position to provide services that enable students from all cultural backgrounds to successfully support literacy development. Clinical applications of the guiding principles are provided.
Efficient indoor propagation channel prediction based on deep learning approach
Authors: Kedjar Khaled , Talbi Larbi , Nedil Mourad
Published: August 2021

In this study, efficient channel characterisation and modelling based on deep learning algorithms are developed and presented in a line‐of‐sight (LOS) scenario. The learning and the validation processes are performed using measurements from only one environment, enabling robust model learning and prediction results. Then, the model efficiency is analysed and validated using measurements from different environments that are not included in the learning process. Finally, the channel characterisation is made with the predicted and measured ones. The designed model achieved a highly accurate channel frequency response prediction within different environments without any prior information. The model root‐mean‐square error achieves up to 2% compared with the latest proposed models in the literature. Hence, an efficient modelling tool is provided for the future wireless communication design in a complex confined environment in LOS scenarios.
Data‐driven operation of the resilient electric grid: A case of COVID‐19
Authors: Noorazar H. , Srivastava A. , Pannala S. , K Sadanandan Sajan
Published: August 2021

Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g. Ukraine cyberattack, Hurricane Maria). The global pandemic COVID‐19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. Additionally, the pandemic introduces a significant degree of uncertainty to the grid operation in the presence of other challenges including aging power grids, high proliferation of distributed generation, market mechanism, and active distribution network. This situation increases the need for measures for the resiliency of power grids to mitigate the impact of the pandemic as well as simultaneous extreme events including cyberattacks and adverse weather events. Solutions to manage such an adverse scenario will be multi‐fold: (a) emergency planning and organisational support, (b) following safety protocol, (c) utilising enhanced automation and sensing for situational awareness, and (d) integration of advanced technologies and data points for ML‐driven enhanced decision support. Enhanced digitalisation and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be employed to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, the resilience of power grids in the face of pandemics is explored and various machine learning tools that can be helpful to augment human operators are discused by: (a) reviewing the impact of COVID‐19 on power grid operations and actions taken by operators/organisations to minimise the impact of COVID‐19, and (b) presenting recently developed tools and concepts of machine learning and artificial intelligence that can be applied to increase the resiliency of power systems in normal and extreme scenarios such as the COVID‐19 pandemic.
Thermal radiation pressure as a possible mechanism for losing small particles on asteroids
Authors: Bach Yoonsoo P. , Ishiguro Masateru
Published: August 2021

Context.Recent observations of dust ejections from active asteroids, including (3200) Phaethon, have drawn considerable interest from planetary astronomers studying the generation and removal of small dust particles on asteroids.

Aims.In this work, we aim to investigate the importance of thermal radiation pressure from asteroid regolith (AR) acting on small dust particles over the surface of the AR. In particular, we aim to understand the role of thermal radiation in the near-Sun environment.

Methods.We describe the acceleration of particles over the AR within the radiation fields (direct solar, reflected (scattered) solar, and thermal radiation) in addition to the asteroid’s rotation and gravitational field. Mie theory is used because the particles of interest have sizes comparable to thermal wavelengths (~1–100 μm), and thus the geometric approximation is not applicable. A new set of formalisms is developed for the purpose.

Results.We find that the acceleration of particles with spherical radius ≲1 μm to ~10 μm is dominated by the thermal radiation from the AR when the asteroid is in the near-Sun environment (heliocentric distancerh≲ 0.8 au). Under thermal radiation dominance, the net acceleration is towards space, that is, outwards from the AR. This outward acceleration is the strongest for particles of ~1 μm in radius, regardless of other parameters. A preliminary trajectory integration using the Phaethon-like model shows that such particles escape from the gravitational field within about 10 min. Our results are consistent with the previous observational studies on Phaethon in that the ejected dust particles have a spherical radius of ~1 μm.
XMM-Newton spectrum of the radio-loud quasar 3C 215: Slim accretion disk or SMBH binary
Authors: Mei A. , Tombesi F.
Published: August 2021

Context. Radio-loud active galactic nuclei (RL AGN) exhibit very powerful jet emission in the radio band, while the radio-quiet (RQ) AGN do not. This RL-RQ dichotomy would imply a sharp difference existing among these two classes, however, modern theoretical models and observations suggest a common nuclear environment that is possibly characterized by different working regimes.

Aims. We explore the geometrical structure and mutual interactions of the innermost components of the broad line radio galaxy 3C 215, with a particular focus on the accretion and ejection mechanisms involving the central supermassive black hole (SMBH). We compare these observational features with those of the RQ Seyfert 1 galaxies. Investigating their differences is aimed at improving our understanding of the jet launching mechanisms and devising an explanation for why this phenomenon is efficient only in a small fraction of all the AGNs.

Methods. Using high-quality data from a ∼60 ks observation with XMM-Newton, we carried out a detailed X-ray spectral analysis of 3C 215 in the broad energy range of 0.5−10 keV. We modeled the spectrum with an absorbed double power-law model for the primary continuum, reprocessed by reflection from ionized and cold neutral material and modified by relativistic blurring. We also compared our results with those of earlier multi-wavelength observations.

Results. We obtained a primary continuum photon index from the corona, namely, Γ1 = 1.97 ± 0.06, along with evidence of a jet contribution, modeled as a power law with photon index of Γ2 ≃ 1.29. The reflector, which is possibly attributed to the accretion disk and portions of the broad-line region (BLR), is ionized (logξ = 2.31−0.27+0.37 erg s−1 cm) and relatively distant from the SMBH (Rin > 38 Rg), where Rg = GMBH/c2 is the gravitational radius. The obscuring torus seems patchy, dust-poor, and inefficient, while the jet emission shows a twisted and knotted geometry. We propose three scenarios to describe the following characteristics: 1. An ADAF state in the inner disk; 2. A slim accretion disk; and 3. A sub-pc SMBH binary system (SMBHB).

Conclusions. While the first scenario is not in agreement with the SMBH accretion regime, the slim disk scenario is consistent with the observational features of this radio galaxy, showing that 3C 215 is similar to non-jetted AGNs, accreting at a high rate. Nonetheless, the first two scenarios are unable to account for the particular shape of 3C 215 jet emission. The SMBHB scenario seems to be in agreement with almost all 3C 215 observational features, but we are not able to unequivocally determine this source as a strong SMBHB candidate. A final determination will require further analysis.
Disks in close binary stars
Authors: Jordan Lucas M. , Kley Wilhelm , Picogna Giovanni , Marzari Francesco
Published: August 2021

Context. Close binaries (abin ≤ 20 au) are known to harbor planets, yet planet formation is unlikely to succeed in such systems. Studying the dynamics of disks in close binaries can help to understand how those planets could have formed.

Aims. We study the impact that numerical and physical parameters have on the dynamics of disks in close binaries. We use the γ-Cephei system as an example and focus on disk quantities such as disk eccentricity and the precession rate as indicators for the dynamical state of the disks.

Methods. We simulate disks in close binaries by performing two-dimensional radiative hydrodynamical simulations using a modified version of the FARGO code. First, we perform a parameter study for different numerical parameters to confirm that our results are robust. In the second part, we study the effects of different masses and different viscosities on the disks’ dynamics.

Results. Previous studies on radiative disks in close binaries used too low resolutions and too small simulation domains, which impacted the disk’s dynamics. We find that radiative disks in close binaries, after an initialization phase, become eccentric with mean eccentricities between 0.06 and 0.27 and display a slow retrograde precession with periods ranging from 4−40Tbin which depends quadratically on the disk’s mean aspect ratio. In general, the disks show a coherent, rigid precession which can be broken, however, by changes in the opacity law reducing the overall eccentricity of the disk.
Discovery of eRASSt J192932.9–560346: A bright, two-pole accreting, eclipsing polar
Authors: Schwope Axel , Buckley David A. H. , Malyali Adam , Potter Stephen , König Ole , Arcodia Riccardo , Gromadzki Mariusz , Rau Arne
Published: August 2021

We report the discovery of a bright (V ~ 15), eclipsing, two-pole accreting magnetic cataclysmic variable (CV), a polar, as counterpart of the SRG/eROSITA and Gaia transients eRASSt J192932.9–560346 and Gaia21bxo. Frequent large-amplitude changes of its brightness at X-ray and optical wavelengths by more than four magnitudes are indicative of a CV nature of the source. Identification spectra obtained with the 10m SALT telescope revealed the typical features of a magnetic CV: strong, broad HeI, HeII, and hydrogen Balmer emission lines superposed on a blue continuum. Time-resolved photoelectric polarimetry revealed that the circular polarization varies from −20 to +20% and the linear polarization varies from 0 to 10%, confirming the system to be a magnetic CV of the polar subclass. High-cadence photometry revealed deep, structured eclipses, indicating that the system is a two-pole accretor. The orbital period determined from the eclipse times is 92.5094 ± 0.0002 min. The X-ray spectrum is thermal only, and the implied luminosity is LX = 2.2 × 1031 erg s−1 at the Gaia-determined distance of 376 pc.
Performance Comparison of Various controllers on Semi-Active Vehicle Suspension System
Authors: Walavalkar Sarvesh , Tandel Viraj , Thakur Rahul Sunil , Kumar V.V Pramod , Bhuran Supriya
Published: August 2021

The value of a self-tuning adaptive semi-active control scheme for automotive suspension systems is discussed in this paper. The current vehicle suspension system uses fixed-coeffcient springs and dampers. The ability of vehicle suspension systems to provide good road handling and improve passenger comfort is usually valued. Passive suspension allows you to choose between these two options. Semi-Active suspension(SAS), on the other hand, can provide both road handling and comfort by manipulating the suspension force actuators directly. The semi-active suspension system for a quarter car model is compared to passive and various controllers such as Proportional-Integral, Proportional-Integral-Derivative, Internal model control (IMC)-PID, IMC-PID with filter, FUZZY, and Adaptive-network-based fuzzy inference system(ANFIS) in this analysis. This research could be relevant in the future for designing better car suspension adjustments to eliminate vertical jerks and rolling motion experienced by the vehicle body on bumps and humps.
Real Time Weather Monitoring System Using Iot
Authors: Sharma Puja , Prakash Shiva
Published: August 2021

In Today’s World, knowing live environmental condition is one of the biggest issues because there is an IoT of hurdles arrives when live environmental condition is measured. The proposed system will remove this problem since it monitors real-time weather conditions. In this proposed work we will monitor the live weather’s parameter of the Gorakhpur Region. The proposed system will work on the client-server architecture model using IoT. The system is organized in Two-tier Architecture. Our proposed system contains a various sensor which will monitor the temperature of the region, humidity, Rain value and pressure of the system. The sensor captured data and send it to the node MCU controller. Arduino ide is used to upload the sensed data. The serial monitor has worked as a gateway between the sensor and the cloud. The data is pushed by the sensor on a serial monitor. The serial monitors an IP address. The HTTP protocol is used to view the data on the webserver. This paper displays the data on the webserver and monitor the real-time data of weather using environmental parameter or sensor. Using a webserver, everyone can monitor the weather’s condition from anywhere without depending on any application or website. The data is available publicly. With the help of this proposed system, we measure the weather condition of the Gorakhpur Region. After getting results from the various sensor, it is observed that our proposed model achieves better results in comparison with the standard weather parameter.
Food Reviews Classification using multi-label convolutional neural network text classifier
Authors: Lasne Krutuja S , Nandrekar Sejal S , A Khan Ashraf , Ghorpade Tushar
Published: August 2021

Most commercial websites, such as Amazon, encourage users to leave reviews of the goods and services they get after purchasing them. For certain consumers, this analysis is critical when determining whether or not to buy a product. Understanding the consequences of feedback and correctly classifying their utility may therefore be an advantageous method for websites. The classification results can also be used as a review and recommendation program for ongoing success. Nowadays people visit several restaurants on various occasions. They get confused most of the time after having a look at the food menu. Based on the ratings and reviews of the dish it becomes easier for them to decide the dish they wish to order. However they unable to read each review of the previous customers. So to overcome this issue, we have proposed NLP (Natural Language Processing) technique and Spacy CNN (Convolutional Neural Network) pipeline system which will classify all the reviews in a single rating. Each review is labelled with a reviewer's score indicating the sentiment of the reviewers. Our task is to predict a reviewer’s score on a scale of 0 or 1. Where 1 indicates the users like the dish while 0 indicates that the reviewers were not satisfied with the dish.
Fake News Detection using Machine Learning
Authors: Kulkarni Prasad , Karwande Suyash , Keskar Rhucha , Kale Prashant , Iyer Sumitra
Published: August 2021

Everyone depends upon various online resources for news in this modern age, where the internet is pervasive. As the use of social media platforms such as Facebook, Twitter, and others has increased, news spreads quickly among millions of users in a short time. The consequences of Fake news are far-reaching, from swaying election outcomes in favor of certain candidates to creating biased opinions. WhatsApp, Instagram, and many other social media platforms are the main source for spreading fake news. This work provides a solution by introducing a fake news detection model using machine learning. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data are Random Forest, Logistic Regression, Decision Tree, KNN and Gradient Booster. Based on the output received the data is classified either as true or false data. Based on that, the user can find out whether the given news is fake or not on the webserver.
Indian Sign Language Recognition using Convolutional Neural Network
Authors: Patil Rachana , Patil Vivek , Bahuguna Abhishek , Datkhile Gaurav
Published: August 2021

Communicating with the person having hearing disability is always a major challenge. The work presented in paper is an exertion(extension) towards examining the difficulties in classification of characters in Indian Sign Language(ISL). Sign language is not enough for communication of people with hearing ability or people with speech disability. The gestures made by the people with disability gets mixed or disordered for someone who has never learnt this language. Communication should be in both ways. In this paper, we introduce a Sign Language recognition using Indian Sign Language.The user must be able to capture images of hand gestures using a web camera in this analysis, and the system must predict and show the name of the captured image. The captured image undergoes series of processing steps which include various Computer vision techniques such as the conversion to gray-scale, dilation and mask operation. Convolutional Neural Network (CNN) is used to train our model and identify the pictures. Our model has achieved accuracy about 95%
Voice Feature Extraction for Gender and Emotion Recognition
Authors: Nashipudimath Madhu M. , Pillai Pooja , Subramanian Anupama , Nair Vani , Khalife Sarah
Published: August 2021

Voice recognition plays a key function in spoken communication that facilitates identifying the emotions of a person that reflects within the voice. Gender classification through speech is a popular Human Computer Interaction (HCI) method on account that determining gender through computer is hard. This led to the development of a model for "Voice feature extraction for Emotion and Gender Recognition". The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have specific vocal traits because of their acoustical and perceptual variations along with a variety of emotions which bring their own specific perceptions. In order to explore this area, feature extraction requires pre-processing of data, which is necessary for increasing the accuracy. The proposed model follows steps such as data extraction, pre-processing using Voice Activity Detector(VAD), feature extraction using Mel-Frequency Cepstral Coefficient(MFCC), feature reduction by Principal Component Analysis(PCA) and Support Vector Machine (SVM) classifier. The proposed combination of techniques produced better results which can be useful in healthcare sector, virtual assistants, security purposes and other fields related to Human Machine Interaction domain.
Freehand to Digital Circuit Reconstruction Using HOG and SVM
Authors: Mittal Shristi , Satpute Rhutuja , Mohitte Shubhamm , Ragha Leena , Bhosale Dhanashri
Published: August 2021

Sketches are commonly used in the fields of engineering and architecture, especially for the early design phases. Engineers spend considerable time setting up initial designs using pencil and paper, and then redrawing them to any software. This problem can be solved by using the idea to scan the circuit sketch with android device which is drawn on the paper and translate it into standard layouts and run circuit simulations. The scanned image will be pre-processed and further segmented. The segmented image will be used to extract the features which are in turn given for classification. Recognizing sketches may seem so quick and intuitive to humans but it is really a big challenge for the machine. In this proposed work the aim is to achieve high precision trainable electronic circuit component recognizer for sketched circuits with fast response time and simple extensibility to new components.
Detection of Phishing Websites Using Ensemble Machine Learning Approach
Authors: M. Dharani , Badkul Soumya , Gharat Kimaya , Vidhate Amarsinh , Bhosale Dhanashri
Published: August 2021

In this paper, we propose the use of Ensemble Machine Learning Methods such as Random Forest Algorithm and Extreme Gradient Boosting (XGBOOST) Algorithm for efficient and accurate phishing website detection based on its Uniform Resource Locator. Phishing is one of the most widely executed cybercrimes in the modern digital sphere where an attacker imitates an existing - and often trusted - person or entity in an attempt to capture a victim’s login credentials, account information, and other sensitive data. Phishing websites are visually and semantically similar to real ones. The rise in online trading activities has resulted in a rise in the number of phishing scams. Cybersecurity jobs are the most difficult to fill, and the development of an automated system for phishing website detection is the need of the hour. Machine Learning is one of the most feasible methods to approach this situation, as it is capable of handling the dynamic nature of phishing techniques, in addition to providing an accurate method of classification.
Foreign money transfer using blockchain
Authors: Giri Nupur , Jodha Dheeraj Singh , Goyal Yash , Thite Akshay , Tiwari Abhay
Published: August 2021

This paper focuses on using blockchain technology to enhance efficiency and cost reduction in foreign transactions through the banking system. Blockchain provides crucial features such as immutability of records and decentralization, which is then used to carry out foreign transactions. This process could be a major change in the transactions carried out by removing middle banks during transactions thereby speeding up the process. This paper aims at designing an Ethereum decentralized Banking Application for foreign money transfer using blockchain technology. Smart Contract helps to eliminate middle banks in the process by acting on rules specified in the contract. Every participant needs to abide by these rules which make it trustworthy and maintain the authenticity of the process.
File Fragment Classification using Content Based Analysis
Authors: Bhat Anirudh , Likhite Aryan , Chavan Swaraj , Ragha Leena
Published: August 2021

One of the major components in Digital Forensics is the extraction of files from a criminal’s hard drives. To achieve this, several techniques are used. One of these techniques is using file carvers. File carvers are used when the system metadata or the file table is damaged but the contents of the hard drive are still intact. File carvers work on the raw fragments in the hard disk and reconstruct files by classifying the fragments and then reassembling them to form the complete file. Hence the classification of file fragments has been an important problem in the field of digital forensics. The work on this problem has mainly relied on finding the specific byte sequences in the file header and footer. However, classification based on header and footer is not reliable as they may be modified or missing. In this project, the goal is to present a machine learningbased approach for content-based analysis to recognize the file types of file fragments. It does so by training a Feed-Forward Neural Network with a 2-byte sequence histogram feature vector which is calculated for each file. These files are obtained from a publicly available file corpus named Govdocs1. The results show that content-based analysis is more reliable than relying on the header and footer data of files.
Virtual Dressing using Augmented Reality
Authors: Jadhav Omkar , Patil Anish , Sam Jeffy , Kiruthika M
Published: August 2021

With the rise in Virtual Reality technology now it is possible to bring the same experience of offline shopping in the online platform. Virtual Dressing using Augmented Reality is a platform where users can try clothes virtually. As users cannot physically try the dress in online mode this application will help the user to make a better judgement on how the dress will look on him. The proposed system will consist of an application where users can create their 3D model with just their pictures and can apply the garment on their model, which can be viewed in AR space to make a judgement of how the model looks on a person.
LEAF DISEASE DETECTION OF CUCURBITS USING CNN
Authors: Agrawal Isha , Hegde Prada , Shetty Pooja , Shingane Priyanka
Published: August 2021

Identification of plant disease is tough in agribusiness arena. If it is inaccurate, there occurs a tremendous damage in the production and economical price. Leaf Disease detection requires huge amount of work, knowledge, processing time in plant disease. The most used and edible vegetable all over the world is from cucurbitaceous family. The crops under this family have great economic value in the food industry and its production is done in large scale. This family consists of 965 species. If any of these plants catch disease then there would be a tremendous loss in the production of this field yields. Thus, treating them at early stage is best way to prevent such losses. Hence, Deep Learning Algorithm like CNN can be used to detect the diseases of the plants. The leaves of the plants would be used as primary material for identification of the disease, as they are much more visible on the leaves.
A Parallel Apriori Algorithm and FP- Growth Based on SPARK
Authors: Gupta Priyanka , Sawant Vinaya
Published: August 2021

Frequent Itemset Mining is an important data mining task in real-world applications. Distributed parallel Apriori and FP-Growth algorithm is the most important algorithm that works on data mining for finding the frequent itemsets. Originally, Map-Reduce mining algorithm-based frequent itemsets on Hadoop were resolved. For handling the big data, Hadoop comes into the picture but the implementation of Hadoop does not reach the expectations for the parallel algorithm of distributed data mining because of its high I/O results in the transactional disk. According to research, Spark has an in-memory computation technique that gives faster results than Hadoop. It was mainly acceptable for parallel algorithms for handling the data. The algorithm working on multiple datasets for finding the frequent itemset to get accurate results for computation time. In this paper, we propose on parallel apriori and FP-growth algorithm to finding the frequent itemset on multiple datasets to get the mining itemsets using the Apache SPARK framework. Our experiment results depend on the support value to get accurate results.
Real Time Gender Classification using Convolutional Neural Network
Authors: Mishra Pooja , Singh Neha , Chavan Pallavi
Published: August 2021

This paper presents real time gender classification using Convolutional Neural Network. Automatic classification of gender has become important to an growing array of applications particularly with the emergence of web networks and social media. Slavery was a significant moral problem in the nineteenth century. It was a struggle toward fascism in the modern period. The fight for gender equality across the world, as well as the need to divide gender for meaningful purposes, would, we conclude, be the most critical moral issue of this century. Differences are needed at different places, such as restrooms for men and restrooms for women; attire for men and attire for women; and so on, in order to plan and advance further in the technological sector. To decrease crime rates, to place the advertisements in malls precisely attracting more people based on gender, to keep track of genders in respective toilets or in trains, for personal services, etc. The authors propose the gender classification dilemma for real-time applications, in which a tool decides if the faces within the exposure belong to a female or a male. The primary fundamental region of experimentation in this venture is adjusting a few already distributed, successful designs utilized for gender orientation classification. Generally, facial structure variations have an effect on gender classification accuracy considerably, as a result of facial form and skin texture modification as they become old. This requires re- examination on the sexual orientation classification framework. By learning representations through the utilization of deep-convolutional neural networks (CNN), a major increase in performance is obtained on these tasks.
Single-chip multi-processing architecture for spaceborne SAR imaging and intelligent processing
Authors: Wang Shiyu , Zhang Shengbing , Huang Xiaoping , Chang Libo
Published: August 2021

The satellite-borne SAR image intelligent processing system needs to process on-orbit real-time imaging and various tasks of applications, for which reason designing a dedicated high-efficient single-chip multi-processor is of prioritized necessity that can simultaneously satisfy requirements of real-time and low power consumption. Aiming at on-chip data organization and memory access structure, two typical models of SAR(synthetic aperture radar) imaging CSA (chirp scaling) and neural network VGG-11 are analyzed, and then a collaborative computing model for the intelligent processing on remote sensing is extracted. A strip Tile data processing scheme and a dedicated multi-processing architecture is not only proposed, but a data organization and a caching strategy of Tile space synchronization splicing is also presented. In addition, the designed data caching structure among the processing units greatly reduces off-chip access memory bandwidth while supporting parallel pipeline execution of multi-task model. The chip adopts 28 nm CMOS technology featuring with merely 1.83 W of the overall power consumption, whose throughput and energy efficiency reaches 9.89TOPS and 5.4 TOPS/W, respectively. The present architecture can improve real-time performance of the on-orbit remote sensing intelligent processing platform while reducing the complexity of system designing, which is highly adaptive to differentiated expansions according to different models of algorithm.