bluuebunny/crossref_metadata_2025 huggingface dataset:
Synergistic Pd/Cu-catalyzed enantioselective Csp2–F bond alkylation of fluoro-1,3-dienes with aldimine esters
Authors: Yu Huimin , Zhang Qinglong , Zi Weiwei
Published: May 2022

Due to high bond dissociation energies of Csp2–F bonds, using fluorinated compounds in Csp2–Csp3 cross-coupling is difficult. Here the authors report a protocol for enantioselective Csp2–Csp3 coupling of dienyl fluorides with aldimine esters, enabled by synergistic copper and palladium catalysis. This reaction represents the first example of asymmetric Csp2–Csp3 cross-coupling involving an inert Csp2–F bond and provides expeditious access to chiral α-alkenyl α-amino acids with high enantioselectivity. Control experiments suggest that the Csp2–F bond activation occurs through a pathway involving PdH migratory insertion and subsequent allylic defluorination, rather than by direct oxidative addition of the Csp2–F bond to Pd(0). The detailed mechanism is further investigated by DFT calculation and the enantioselectivity is rationalized.
Comparative optimization of combinatorial CRISPR screens
Authors: Li Ruitong , Klingbeil Olaf , Monducci Davide , Young Michael J. , Rodriguez Diego J. , Bayyat Zaid , Dempster Joshua M. , Kesar Devishi , Yang Xiaoping , Zamanighomi Mahdi , Vakoc Christopher R. , Ito Takahiro , Sellers William R.
Published: May 2022

Combinatorial CRISPR technologies have emerged as a transformative approach to systematically probe genetic interactions and dependencies of redundant gene pairs. However, the performance of different functional genomic tools for multiplexing sgRNAs vary widely. Here, we generate and benchmark ten distinct pooled combinatorial CRISPR libraries targeting paralog pairs to optimize digenic knockout screens. Libraries composed of dual Streptococcus pyogenes Cas9 (spCas9), orthogonal spCas9 and Staphylococcus aureus (saCas9), and enhanced Cas12a from Acidaminococcus were evaluated. We demonstrate a combination of alternative tracrRNA sequences from spCas9 consistently show superior effect size and positional balance between the sgRNAs as a robust combinatorial approach to profile genetic interactions of multiple genes.
Cell cycle-specific phase separation regulated by protein charge blockiness
Authors: Yamazaki Hiroya , Takagi Masatoshi , Kosako Hidetaka , Hirano Tatsuya , Yoshimura Shige H.
Published: May 2022

Dynamic morphological changes of intracellular organelles are often regulated by protein phosphorylation or dephosphorylation1–6. Phosphorylation modulates stereospecific interactions among structured proteins, but how it controls molecular interactions among unstructured proteins and regulates their macroscopic behaviours remains unknown. Here we determined the cell cycle-specific behaviour of Ki-67, which localizes to the nucleoli during interphase and relocates to the chromosome periphery during mitosis. Mitotic hyperphosphorylation of disordered repeat domains of Ki-67 generates alternating charge blocks in these domains and increases their propensity for liquid–liquid phase separation (LLPS). A phosphomimetic sequence and the sequences with enhanced charge blockiness underwent strong LLPS in vitro and induced chromosome periphery formation in vivo. Conversely, mitotic hyperphosphorylation of NPM1 diminished a charge block and suppressed LLPS, resulting in nucleolar dissolution. Cell cycle-specific phase separation can be modulated via phosphorylation by enhancing or reducing the charge blockiness of disordered regions, rather than by attaching phosphate groups to specific sites.
Analysis of microbial community structure and volatile compounds in pit mud used for manufacturing Taorong-type Baijiu based on high-throughput sequencing
Authors: Liu Yanbo , Sun Mengxiao , Hou Pei , Wang Wenya , Shen Xiangkun , Zhang Lixin , Han Suna , Pan Chunmei
Published: May 2022

In this study, the pit mud used in manufacturing Taorong-type Baijiu was collected from the upper, middle, lower and bottom layers of pits at Henan Yangshao Liquor Co., LTD. High-throughput sequencing (HTS) technology was used to analyze the microbial community structure of the pit mud. In addition, the volatile compounds in the pit mud were subjected to preliminary qualitative analysis through headspace-solid phase microextraction and gas chromatography–mass spectrometry (GC–MS). The HTS results demonstrated that there were 5, 3, 5 and 5 dominant bacterial phyla (including 11, 11, 9 and 8 dominant bacterial genera) and 3, 3, 3 and 3 dominant fungal phyla (including 4, 7, 7 and 5 dominant fungal genera) in the pit mud from the F-S (upper), G-Z (middle), H-X (lower) and I-D (bottom) layers, respectively. In the qualitative analysis of the volatile compounds, a total of 77types of volatile compounds were detected in the pit mud, including 46, 45, 39 and 49 types in the pit mud from layers F-S, G-Z, H-X and I-D, respectively. Esters and acids were the two main components of the pit mud. The correlation between the microorganisms present and the main volatile compounds in the pit mud was analyzed. Lentimicrobium, Syner-01 and Blvii28_wastewater-sludge groups were found for the first time in pit mud used for manufacturing Taorong-type Baijiu. The findings of this study could provide a theoretical foundation for improving the quality of pit mud and the flavor of Taorong-type Baijiu.
Efficient processing of top-k frequent spatial keyword queries
Authors: Xu Tao , Xu Aopeng , Mango Joseph , Liu Pengfei , Ma Xiaqing , Zhang Lei
Published: May 2022

The rapid popularization of high-speed mobile communication technology and the continuous development of mobile network devices have given spatial textual big data (STBD) new dimensions due to their ability to record geographical objects from multiple sources and with complex attributes. Data mining from spatial textual datasets has become a meaningful study. As a popular topic for STBD, the top-k spatial keyword query has been developed in various forms to deal with different retrievals requirements. However, previous research focused mainly on indexing locational attributes and retrievals of few target attributes, and these correlations between large numbers of the textual attributes have not been fully studied and demonstrated. To further explore interrelated-knowledge in the textual attributes, this paper defines the top-k frequent spatial keyword query (tfSKQ) and proposes a novel hybrid index structure, named RCL-tree, based on the concept lattice theory. We also develop the tfSKQ algorithms to retrieve the most frequent and nearest spatial objects in STBD. One existing method and two baseline algorithms are implemented, and a series of experiments are carried out using real datasets to evaluate its performance. Results demonstrated the effectiveness and efficiency of the proposed RCL-tree in tfSKQ with the complex spatial multi keyword query conditions.
Grade group system and plasma androgen receptor status in the first line treatment for metastatic castration resistant prostate cancer
Authors: Cursano M. C. , Conteduca V. , Scarpi E. , Gurioli G. , Casadei C. , Gargiulo S. , Altavilla A. , Lolli C. , Vincenzi B. , Tonini G. , Santini D. , De Giorgi U.
Published: May 2022

In localized prostate cancer (PCa), Grade Group (GG) and Gleason Score (GS) have a well-established prognostic role. In metastatic castration resistant prostate cancer (mCRPC), the prognostic role of GS and GG is less defined. In first-line treatment of mCRPC, androgen receptor (AR)-directed drugs (abiraterone acetate, enzalutamide) and docetaxel represent the referring options. There is no evidence that the GS/GG systems can add information to guide the choice between AR-directed drugs and docetaxel in the first-line setting of mCRPC. Nowadays there are no validated biomarkers, which define patients who may benefit or not from hormonal treatments or chemotherapy. Androgen receptor (AR) copy number variations (CNV) are predictive factors of poor response to abiraterone and enzalutamide. There are no available data about the association between AR CNV and GG. In this retrospective study, we analysed the association of the highest GG score with AR CNV and their impact on the clinical outcome of AR-directed drugs and docetaxel as first-line therapy for mCRPC patients. Patients benefit from docetaxel, abiraterone or enzalutamide regardless the GG. However, the presence of GG5 and AR CNV gain identifies a subgroup of patients with poor prognosis, which could benefit from front-line docetaxel instead of AR-directed drugs.
Traditional Chinese medicine extracts as novel corrosion inhibitors for AZ91 magnesium alloy in saline environment
Authors: Li Haonan , Fan Min , Wang Kui , Bian Xiaolan , Jiang Haiyan , Ding Wenjiang
Published: May 2022

Zingiber officinale Roscoe extract, Raphanus sativus L. extract, Rheum palmatum extract, Coptis chinensis extract, Glycyrrhiza uralensis extract (GUE), Potentilla discolor extract (PDE) and Taraxacum officinale extract (TOE) were screened for the green corrosion inhibitors of AZ91 alloy in saline environment. The experiment results demonstrated that GUE, PDE and TOE can significantly enhance the corrosion resistance of AZ91 alloy by 73.4, 87.6 and 84.6%, respectively. Surface characterization using FTIR, UV–Vis and XPS revealed that the organic compounds of GUE, PDE and TOE can interact with the alloy surface to form a protective physisorbed film, effectively mitigating the corrosion process of AZ91 alloy. The present results may be helpful to discover the new green inhibitors with high inhibition efficiency for AZ91 alloy.
Therapeutic and tectonic keratoplasty with simple cryopreserved remnants of donor corneas: an 11 year retrospective case series
Authors: Kim Jae-Gon , Jun Jong Hwa
Published: May 2022

This study sought to describe the use of deep-frozen donor corneal remnants preserved after keratoplasty procedures for therapeutic or tectonic keratoplasty without subsequent optical keratoplasty. This single-center retrospective consecutive case series analyzed the electronic medical records of patients who had undergone therapeutic or tectonic keratoplasty using deep-frozen donor remains preserved in Optisol-GS, for the past 11 years at Keimyung University Dongsan Medical Center. Fifty-five surgical cases in 46 patients were included. Twenty-three surgical cases in 18 patients underwent therapeutic keratoplasty for refractory infectious corneal ulcer. Complete eradication of primary infection was achieved in 14 patients (77.8%). Tectonic keratoplasty were performed 32 cases in 28 patients. Twenty-seven of 28 patients were ultimately able to maintain anatomical integrity (96.4%). Mean uncorrected visual acuity improved from 1.77 ± 0.94 preoperatively to 1.31 ± 0.95 at the last follow-up postoperatively in the tectonic graft group by logarithm of the minimal angle of resolution (P = 0.002). There were no cases of graft rejection. Keratoplasty using cryopreserved donor tissue is a suitable surgical alternative for infectious or non-infectious corneal ulcers in elderly patients or patients with poor general condition. It could be a viable alternative to overcome the shortage of corneal donors.
Understanding flammability and bark thickness in the genus Pinus using a phylogenetic approach
Authors: Varner J. Morgan , Shearman Timothy M. , Kane Jeffrey M. , Banwell Erin M. , Jules Erik S. , Stambaugh Michael C.
Published: May 2022

Pinusspecies dominate fire-prone ecosystems throughout the northern hemisphere. Their litter drive fires that control plant community flammability and multiple ecological processes. To better understand the patterns and mechanisms of pine flammability, we measured leaf characteristics (needle length and thickness) and conducted combustion experiments on litter from 31 species. We paired flammability results with bark accumulation data and used phylogenetic generalized least squares regression to examine relationships between physical traits and flammability. Pine flammability varied widely among pines: flame heights and fuel consumption varied three-fold, and flaming and smoldering durations varied three- to six-fold. SubgenusPinusspecies were the most flammable and subgenusStrobusspecies had the lowest flammability. Needle length was the best predictor of flammability with a significant interaction with subgenus, suggesting that flammability of pines in subgenusStrobuswas more affected by physical traits than pines in subgenusPinus. Species in the subgenusPinusthat accumulated outer bark rapidly also had high flammability, while the relationship was not significant in subgenusStrobus. These results highlight the diverse patterns of flammability in North American pines and the complexity in the mechanisms causing differential flammability.
Author Correction: Multiscale heterogeneous optimal lockdown control for COVID-19 using geographic information
Authors: Neary Cyrus , Cubuktepe Murat , Lauffer Niklas , Jin Xueting , Phillips Alexander J. , Xu Zhe , Tong Daoqin , Topcu Ufuk
Published: May 2022

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
A computational study on the adsorption of arsenic pollutants on graphene-based single-atom iron adsorbents
Authors: Ma Kai , Zheng Di , Yang Weijie , Wu Chongchong , Dong Shuai , Gao Zhengyang , Zhao Xiaojun
Published: May 2022

The FeSA@Vs-N3 adsorbent has the largest adsorption ability for As2, As4, AsO and AsH3. The adsorption capacity of AsO is relatively stronger. After the competitive adsorption, AsO occupies the adsorption site at 300–900 K.
Quantifiable models for surface protonic conductivity in porous oxides – case of monoclinic ZrO2
Authors: Sun Xinwei , Gu Jie , Han Donglin , Norby Truls
Published: May 2022

Four quantifiable models are introduced to describe surface protonic conductivity in porous ZrO2.
Sustainable NaAlH4 production from recycled automotive Al alloy
Authors: Shang Yuanyuan , Pistidda Claudio , Milanese Chiara , Girella Alessandro , Schökel Alexander , Le Thi Thu , Hagenah Annbritt , Metz Oliver , Klassen Thomas , Dornheim Martin
Published: May 2022

To reduce the carbon footprint associated with the production of hydrogen storage materials and to reduce their cost, we pursue the possibility of obtaining high-quality hydride-based materials from industrial metals waste.
Thermoplasmonic nano-rupture of cells reveals annexin V function in plasma membrane repair
Authors: Moreno-Pescador Guillermo S. , Aswad Dunya S. , Florentsen Christoffer D. , Bahadori Azra , Arastoo Mohammad R. , Danielsen Helena Maria D. , Heitmann Anne Sofie B. , Boye Theresa L. , Nylandsted Jesper , Oddershede Lene B. , Bendix Poul Martin
Published: May 2022

Living cells respond to nanoscopic thermoplasmonic injury by recruiting an annular ring of annexin V.
Epitaxy: a methodological approach to the study of an old phenomenon
Authors: Bruno M. , Pastero L. , Cotellucci A. , Aquilano D.
Published: May 2022

We describe the use of an integrated methodological approach to study epitaxial phenomena.
Acyl amide-functionalized and water-stable iron-based MOF for rapid and selective dye removal
Authors: Ghasempour Hosein , Zarekarizi Farnoosh , Morsali Ali
Published: May 2022

Acyl amide-functionalized, highly porous and water-stable Fe3O cluster-based MOF for rapid and selective organic dye adsorption.
Prediction of SiS2 and SiSe2 as promising anode materials for sodium-ion batteries
Authors: Wang Xianpeng , Wang Lu , Li Youyong
Published: May 2022

We predict SiS2 and SiSe2 as the promising electrode materials for sodium-ion batteries with high capacities and desirable voltages.
Modelling mercury sorption of a polysulfide coating made from sulfur and limonene
Authors: Worthington Max J. H. , Mann Maximilian , Muhti Ismi Yusrina , Tikoalu Alfrets D. , Gibson Christopher T. , Jia Zhongfan , Miller Anthony D. , Chalker Justin M.
Published: May 2022

Detailed kinetic modelling of mercury uptake for a sulfur-limonene copolymer was undertaken. The effect of pH and salt concentration on mercury sorption, as well as selectivity, was assessed for the first time for this mercury-binding polymer.
Glyphosate sensing in aqueous solutions by fluorescent zinc(ii) complexes of [9]aneN3-based receptors
Authors: Garau Alessandra , Picci Giacomo , Bencini Andrea , Caltagirone Claudia , Conti Luca , Lippolis Vito , Paoli Paola , Romano Giammarco Maria , Rossi Patrizia , Scorciapino Mariano Andrea
Published: May 2022

Binding ability of Zn(ii) complexes of [9]aneN3-based receptors for selective recognition of glyphosate in aqueous media.
Pomegranate juice alters the microbiota in breast milk and infant stool: a pilot study
Authors: Henning Susanne M. , Yang Jieping , Lee Ru-Po , Huang Jianjun , Thames Gail , Korn Michelle , Ben-Nissan Dina , Heber David , Li Zhaoping
Published: May 2022

This interventional pilot study demonstrates that ellagic acid and its metabolites are absorbed by the nursing infant from breast milk, excreted in urine and impact the infant's gut microbiome. Phenolic compounds in breast milk might contribute health benefits to infants.
Measuring the refractive index and sub-nanometre surface functionalisation of nanoparticles in suspension
Authors: Mulkerns Niall M. C. , Hoffmann William H. , Ramos-Soriano Javier , de la Cruz Noelia , Garcia-Millan Teodoro , Harniman Robert L. , Lindsay Ian D. , Seddon Annela M. , Galan M. Carmen , Gersen Henkjan
Published: May 2022

An optical technique to precisely quantify the amount of active compound associated with nanoparticles in suspension is presented and experimentally verified, enabling differentiation between Angstrom-scale coronas on nanometre-sized carbon dots.
NiCoP–CeO2 composites for efficient electrochemical oxygen evolution
Authors: Li Jiyu , Wang Zeyan , Wang Peng , Zheng Zhaoke , Liu Yuanyuan , Cheng Hefeng , Huang Baibiao
Published: May 2022

A NiCoP–CeO2 composite was constructed by a simple hydrothermal method and thermal phosphating strategy. Benefiting from the confinement effect and optimized H2O adsorption ability, NiCoP–CeO2 exhibits superior OER performance than NiCoP.
Lithium achieves sequence selective ring-opening terpolymerisation (ROTERP) of ternary monomer mixtures
Authors: Rupf Susanne , Pröhm Patrick , Plajer Alex J.
Published: May 2022

A sequence selective ring-opening terpolymerisation of epoxides with CS2 and phthalic thioanhydride yielding poly(ester-alt-ester-alt-trithiocarbonates) is reported.
An injectable, naproxen-conjugated, supramolecular hydrogel with ultra-low critical gelation concentration—prepared from a known folate receptor ligand
Authors: Oliveira Carlos B. P. , Veloso Sérgio R. S. , Castanheira Elisabete M. S. , Figueiredo Pedro R. , Carvalho Alexandra T. P. , Hilliou Loic , Pereira Renato B. , Pereira David M. , Martins José A. , Ferreira Paula M. T. , Jervis Peter J.
Published: May 2022

Two dipeptide folate receptor ligands (1 and 2) were synthesised and tested for their hydrogelation ability. Dipeptide 1, containing naproxen, rapidly forms hydrogels at very low concentrations (0.03 wt%), whereas dipeptide 2 does not form hydrogels.
Controlled nucleation in evaporative crystallization using a confined-vapor driven solutal Marangoni effect
Authors: Pyeon Jeongsu , Park Soon Mo , Yoon Dong Ki , Kim Hyoungsoo
Published: May 2022

A confined boundary condition changes the crystallized pattern from a coffee-ring-like crystal stain to a large and pervasive crystal stain.
Active region chromospheric magnetic fields
Authors: Vissers G. J. M. , Danilovic S. , Zhu X. , Leenaarts J. , Díaz Baso C. J. , da Silva Santos J. M. , de la Cruz Rodríguez J. , Wiegelmann T.
Published: May 2022

Context.A proper estimate of the chromospheric magnetic fields is thought to improve modelling of both active region and coronal mass ejection evolution. However, because the chromospheric field is not regularly obtained for sufficiently large fields of view, estimates thereof are commonly obtained through data-driven models or field extrapolations, based on photospheric boundary conditions alone and involving pre-processing that may reduce details and dynamic range in the magnetograms.

Aims.We investigate the similarity between the chromospheric magnetic field that is directly inferred from observations and the field obtained from a magnetohydrostatic (MHS) extrapolation based on a high-resolution photospheric magnetogram.

Methods.Based on Swedish 1-m Solar Telescope Fe I6173 Å and Ca II8542 Å observations of NOAA active region 12723, we employed the spatially regularised weak-field approximation (WFA) to derive the vector magnetic field in the chromosphere from Ca II, as well as non-local thermodynamic equilibrium (non-LTE) inversions of Fe Iand Ca IIto infer a model atmosphere for selected regions. Milne-Eddington inversions of Fe Iserve as photospheric boundary conditions for the MHS model that delivers the three-dimensional field, gas pressure, and density self-consistently.

Results.For the line-of-sight component, the MHS chromospheric field generally agrees with the non-LTE inversions and WFA, but tends to be weaker by 16% on average than these when larger in magnitude than 300 G. The observationally inferred transverse component is systematically stronger, up to an order of magnitude in magnetically weaker regions, but the qualitative distribution with height is similar to the MHS results. For either field component, the MHS chromospheric field lacks the fine structure derived from the inversions. Furthermore, the MHS model does not recover the magnetic imprint from a set of high fibrils connecting the main polarities.

Conclusions.The MHS extrapolation and WFA provide a qualitatively similar chromospheric field, where the azimuth of the former is better aligned with Ca II8542 Å fibrils than that of the WFA, especially outside strong-field concentrations. The amount of structure as well as the transverse field strengths are, however, underestimated by the MHS extrapolation. This underscores the importance of considering a chromospheric magnetic field constraint in data-driven modelling of active regions, particularly in the context of space weather predictions.
The effect of spatial sampling on magnetic field modeling and helicity computation
Authors: Thalmann J. K. , Gupta M. , Veronig A. M.
Published: May 2022

Context. Nonlinear force-free (NLFF) modeling is regularly used to indirectly infer the 3D geometry of the coronal magnetic field, which is not otherwise accessible on a regular basis by means of direct measurements.

Aims. We study the effect of binning in time-series NLFF modeling of individual active regions (ARs) in order to quantify the effect of a different underlying spatial sampling on the quality of modeling as well as on the derived physical parameters.

Methods. We apply an optimization method to sequences of Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI) vector magnetogram data at three different plate scales for three solar active regions to obtain nine NLFF model time series. From the NLFF models, we deduce active-region magnetic fluxes, electric currents, magnetic energies, and relative helicities, and analyze those with respect to the underlying spatial sampling. We calculate various metrics to quantify the quality of the derived NLFF models and apply a Helmholtz decomposition to characterize solenoidal errors.

Results. At a given spatial sampling, the quality of NLFF modeling is different for different ARs, and the quality varies along the individual model time series. For a given AR, modeling at a certain spatial sampling is not necessarily of superior quality compared to that performed with a different plate scale. Generally, the NLFF model quality tends to be higher for larger pixel sizes with the solenoidal quality being the ultimate cause for systematic variations in model-deduced physical quantities.

Conclusions. Optimization-based modeling using SDO/HMI vector data binned to larger pixel sizes yields variations in magnetic energy and helicity estimates of ≲30% on overall, given that concise checks ensure the physical plausibility and high solenoidal quality of the tested model. Spatial-sampling-induced differences are relatively small compared to those arising from other sources of uncertainty, including the effects of applying different data calibration methods, those of using vector data from different instruments, or those arising from application of different NLFF methods to identical input data.
Impact of spatially correlated fluctuations in sunspots on metrics related to magnetic twist
Authors: Baumgartner C. , Birch A. C. , Schunker H. , Cameron R. H. , Gizon L.
Published: May 2022

Context.The twist of the magnetic field above a sunspot is an important quantity in solar physics. For example, magnetic twist plays a role in the initiation of flares and coronal mass ejections (CMEs). Various proxies for the twist above the photosphere have been found using models of uniformly twisted flux tubes, and are routinely computed from single photospheric vector magnetograms. One class of proxies is based onαz, the ratio of the vertical current to the vertical magnetic field. Another class of proxies is based on the so-called twist density,q, which depends on the ratio of the azimuthal field to the vertical field. However, the sensitivity of these proxies to temporal fluctuations of the magnetic field has not yet been well characterized.

Aims.We aim to determine the sensitivity of twist proxies to temporal fluctuations in the magnetic field as estimated from time-series of SDO/HMI vector magnetic field maps.

Methods.To this end, we introduce a model of a sunspot with a peak vertical field of 2370 Gauss at the photosphere and a uniform twist densityq = −0.024 Mm−1. We add realizations of the temporal fluctuations of the magnetic field that are consistent with SDO/HMI observations, including the spatial correlations. Using a Monte-Carlo approach, we determine the robustness of the different proxies to the temporal fluctuations.

Results.The temporal fluctuations of the three components of the magnetic field are correlated for spatial separations up to 1.4 Mm (more than expected from the point spread function alone). The Monte-Carlo approach enables us to demonstrate that several proxies for the twist of the magnetic field are not biased in each of the individual magnetograms. The associated random errors on the proxies have standard deviations in the range between 0.002 and 0.006 Mm−1, which is smaller by approximately one order of magnitude than the mean value ofq.
A scaled-up planetary system around a supernova progenitor
Authors: Squicciarini V. , Gratton R. , Janson M. , Mamajek E. E. , Chauvin G. , Delorme P. , Langlois M. , Vigan A. , Ringqvist S. C. , Meeus G. , Reffert S. , Kenworthy M. , Meyer M. R. , Bonnefoy M. , Bonavita M. , Mesa D. , Samland M. , Desidera S. , D’Orazi V. , Engler N. , Alecian E. , Miglio A. , Henning T. , Quanz S. P. , Mayer L. , Flasseur O. , Marleau G.-D.
Published: May 2022

Context. Virtually all known exoplanets reside around stars with M < 2.3 M⊙ either due to the rapid evaporation of the protostellar disks or to selection effects impeding detections around more massive stellar hosts.

Aims. To clarify if this dearth of planets is real or a selection effect, we launched the planet-hunting B-star Exoplanet Abundance STudy (BEAST) survey targeting B stars (M > 2.4 M⊙) in the young (5−20 Myr) Scorpius-Centaurus association by means of the high-contrast spectro-imager SPHERE at the Very Large Telescope.

Methods. In this paper we present the analysis of high-contrast images of the massive (M ~ 9 M⊙) star μ2 Sco obtained within BEAST. We carefully examined the properties of this star, combining data from Gaia and from the literature, and used state-of-the-art algorithms for the reduction and analysis of our observations.

Results. Based on kinematic information, we found that μ2 Sco is a member of a small group which we label Eastern Lower Scorpius within the Scorpius-Centaurus association. We were thus able to constrain its distance, refining in turn the precision on stellar parameters. Around this star we identify a robustly detected substellar companion (14.4 ± 0.8 MJ)at a projected separation of 290 ± 10 au, and a probable second similar object (18.5 ± 1.5 MJ) at 21 ± 1 au. The planet-to-star mass ratios of these objects are similar to that of Jupiter to the Sun, and the flux they receive from the star is similar to those of Jupiter and Mercury, respectively.

Conclusions. The robust and the probable companions of μ2 Sco are naturally added to the giant 10.9 MJ planet recently discovered by BEAST around the binary b Cen system. While these objects are slightly more massive than the deuterium burning limit, their properties are similar to those of giant planets around less massive stars and they are better reproduced by assuming that they formed under a planet-like, rather than a star-like scenario. Irrespective of the (needed) confirmation of the inner companion, μ2 Sco is the first star that would end its life as a supernova that hosts such a system. The tentative high frequency of BEAST discoveries is unexpected, and it shows that systems with giant planets or small-mass brown dwarfs can form around B stars. When putting this finding in the context of core accretion and gravitational instability formation scenarios, we conclude that the current modeling of both mechanisms is not able to produce this kind of companion. The completion of BEAST will pave the way for the first time to an extension of these models to intermediate and massive stars.
2D hybrid magneto-thermal model for PM induction heating device with rotating movement
Authors: Meriem Boubezari , Youcef Ouazir , Ammar Abdi , Georges Barakat , Yacine Amara
Published: May 2022

A new 2D-hybrid model in cylindrical coordinates intended for the study of a transient magneto-thermal coupled problem is proposed. This model is used to evaluate the spatiotemporal distribution of temperature in rotating aluminum billets heated by permanent magnets induction-heating device (PMIHD). The proposed hybrid model consists of coupling an exact analytical solution of the 2D magneto-harmonic field to a 2D transient thermal analysis where the temperature dependence of electrical and thermal physical properties of the heated billet is considered. Based on the separation variables technique, the analytical solution of magneto-harmonic problem serves to evaluate the induced current and the resulting power density loss distribution in the nonmagnetic billet. Furthermore, the 2D transient thermal analysis, based on the lumped thermal network model, is developed in radial and axial directions of the heated billet. In order to validate the 2D hybrid magneto-thermal model, the performances of the studied PMIHD are compared with those obtained with Finite Element Method (FEM).
Design and Implementation of Voice Controlled Wheelchair using MATLAB
Authors: Karande Kaushal Balu , Somani Sakshi , Zope Jagruti Dilip , Bhusari Balu
Published: May 2022

Most physically challenged people to rely on others for their motion and the number of such people is increasing day by day. Manual Wheelchairs are quite difficult to operate by physically challenged people as they require force for the motion. To overcome this drawback, conventional wheelchairs or powered wheelchairs are available in the markets. Even if they are meant to design for the comfort of users they are quite expensive, so not every physically challenged can afford them. To reduce the percentage of use of a manual wheelchair and to give physically challenged people a better experience and comfort in an affordable manner we have proposed this model. Our proposed model is based on neural networks and the backpropagation algorithm is used as a medium to train the Artificial neural networks which are trained by the user’s voice command so it uses voice recognition as a control mechanism. The proposed prototype is been trained with five voice commands such as (Forward, Backward, Left, Right, and Stop). These voice commands are successfully identified and converted to text. The following text is further written on target hardware wirelessly by using a Bluetooth module for the motion of DC Motors to control the directions of a wheelchair. The entire prototype is been designed on Matlab R2020a and Arduino UNO as target hardware. The main aim of our proposed work is to develop a smart wheelchair that overcomes the problems of existing systems and should be easy, impressionable, and convenient for physically challenged people.
Job Recommendation System Using Hybrid Filtering
Authors: Mulay Aneesh , Sutar Shriyash , Patel Jiten , Chhabria Aditi , Mumbaikar Snehal
Published: May 2022

As for today’s era, recruitment can be considered as one of most difficult process to undergo for job seeking candidate. Many fresher candidates face issue while job recruitment process to undergo which field of interest. The proposed system will help the user to overcome this difficulties by matching their work experience, skills and other details with appropriate companies suitable for respective user. The system will also help experienced users in getting their intended job on the basis of their last job profile. The job recommendation algorithm developed is tedious nor complicated and will be using user-friendly approach to implement job search.The proposed system consist of user dataset with various attributes and company dataset with company details. The profile matching of user with the respective companies can be done using various recommendation algorithms such as content-based,collaborative and hybrid filtering. Since, the content-based and collaborative approach have their own disadvantages, so here implement hybrid filtering which overcomes the disadvantages of the content-based and collaborative filtering. The user can expect a well-proof recommendation from our model. The Project will focus of developing the job recommendation system using hybrid filtering. As for today’s era, recruitment can be considered as one of most difficult process to undergo for job seeking candidate. Here, our job recommendation system comes in picture which neither is tedious nor complicated and makes use of user-friendly approach and helps user to accomplish the task easily.The project will also be focusing on developing the android application which will add a better user interface. The Android application will be user friendly and the user just have to fill in basic details such as his past years of experiences, project, internship, etc. That’s it,the rest part of recommending the job to the users will be done safely by the recommendation model of this project.
Machine Translation Systems for English Captions to Hindi Language Using Deep Learning
Authors: Singh Arvinder , Bhase Ninad , Jain Manav , Ghorpade Tushar
Published: May 2022

Machine Translation is the process of translating text from one language to another which helps to reduce the conversation gap among people from different cultural backgrounds. The task performed by the Machine Translation System is to automatically translate between pairs of different natural languages, where Neural Machine Translation System stands out from all because it provides fluent translation along with reasonable translation accuracy. The Convolution Neural Network encoder is used to find patterns in the images and encode it into a vector that is passed to the Long Short Term Memory decoder which finds the caption word-by-word to best describe the image. Upon reaching the end-line token, the entire description of the image in English is generated and that is our output for that particular image. Automatically creating the description of an image in English using any natural language sentences and then translating it using Neural Machine Translation to Hindi is a very challenging task. It requires expertise in both image processing as well as natural language processing. In this paper, the aim is to compare the two Machine Translation Systems: Google Translation System and the proposed Neural Machine Translation System to convert the text obtained from an image in English to Hindi language.
Multiple Disease Prognostication Based On Symptoms Using Machine Learning Techniques
Authors: Patil Kajal , Pawar Sakshee , Sandhyan Pramita , Kundale Jyoti
Published: May 2022

Disease Prediction system that uses Machine Learning forecasts the ailments on the basis of the data pertaining to the symptoms entered by the user and provides trustworthy findings based on that data. If the patient isn’t in any danger and the user merely wants to know what kind of ailment he or she has had. It is a system that gives the user suggestions and methods on how to keep their health system in good shape, as well as a way to find out if they have a sickness utilizing this forecast. Due to a diversity of diseases and a lower doctor-patient ratio, the use of particular disease prediction technologies as well as concerns about health has risen. We are focusing on offering customers with an instant and accurate disease prognosis based on the symptoms they enter, as well as the severity of the condition projected. It will provide the best algorithm and doctor consultation. Different machine learning algorithms are employed to forecast illnesses, ensuring speedy and reliable predictions.
Sentiment Analysis of Images using Machine Learning Techniques
Authors: Gherkar Yash , Gujar Parth , Gaziyani Amaan , Kadu Siddhi
Published: May 2022

Sentiment analysis is the process of identifying the idea of a text. People share the comments on social media stating their knowledge of the event and would like to know if most other people had a good or bad experience at the same event. This distinction can be made through Emotional-Analysis. Sentiment analysis captures informal text comments, posts and images from all comments shared by different users and classifies comments into different categories as neutral, negative or positive. This is also called as polarity separation. Various different types of ML and in-depth learning methods may be utilised in Sentiment Analysis like Support Vector Machines, NB, Haar Cascade, LBPH, CNN, etc. Emerging rise in popularity in Social Media has established a trend of posting images in restaurants to express their opinion on the food, ambience, etc which can be a useful resource to obtain opinion and feedback from the Customers. In this paper, the implementation of Sentiment Analysis on images containing users along with their faces from the restaurants review revealing it more efficacious in classifying and identifying sentiments of review-images.
A Decision-Making Tool for Creating and Identifying Face Sketches
Authors: Bhoir Manish , Gosavi Chandan , Gade Prathamesh , Alte Bhavana
Published: May 2022

A criminal can be quickly identified and prosecuted using a face sketch based on an eyewitness description . Several applications for converting hand-drawn face drawings and using them to automatically identify and recognize the suspect from the police database have been proposed in the past, but the existing system dealt with some drawbacks. It featured a lot of flaws, including as a limited facial features kit and a cartoonish feel to the constructed suspect face, which made it much more difficult to use these apps and acquire the results and efficiency that were required. In this paper, we present a stand- alone tool that allows users to create composite face sketches of suspects without the need for forensic artists. The application offers a drag-and-drop feature that can match the produced composite facial sketch with the criminal database in real time. This can be done considerably more rapidly and efficiently using deep learning and cloud infrastructure.
Study and Analysis of Stock Market Prediction Techniques
Authors: Kokare Siddhesh , Kamble Anvit , Kurade Shubham , Patil Deepali
Published: May 2022

Stock marketplace is a complicated and demanding system in which people make more money or lose their entire savings. The stock market prediction having high accuracy yields more profit for stock investors. Stock market data is generated in a very large amount and it varies quickly every second. The decision making in stock marketplace is a very challenging and strenuous task of financial stock market. The development of efficient models for prediction decisions is very difficult because of the convolution of stock market financial data and should have high accuracy. This study attempts to compare existing models for the stock market. Various Machine learning methods like Long Short Term Memory (LSTM), Convolution Neural Networks (CNN) and Convolution Neural Networks – Long Term Short Memory (CNN-LSTM) have been used for the comparison. The models are estimated using conventional strategic measure: MAE (Mean Absolute Error). The measured low values indicates that the models are effective in predicting stock prices.
Smart Bus System for Specially Challenged Individuals
Authors: Mithbavkar Amey , Salavkar Bhavesh , Avhad Anuja , Bhuran Supriya Y.
Published: May 2022

This paper introduces a system design about bus management system based on Wi-Fi module which implemented the basic functions of the intelligent public transport management system, such as monitoring the time of bus arrival, departing from the bus station and reporting stations name automatically. This system can ensure the punctuality of vehicles to run, improve the automation level of reporting stations, and quality of public transport service. The management system has low cost and thus it is more feasible. This System can be a brilliant helping hand for the individuals who are mentally and physically challenged and help them in reaching their respective destinations on time because, in our respective transportation system, the priority will be to drop the physically and mentally impaired individuals as well as senior citizens safely on time.
A comparative study of fine-tuning deep learning models for MRI Images
Authors: Gamare Vrushabh , Kharaje Vaibhav , Borole Shreyash , Ingle Saguna
Published: May 2022

A brain tumor is an abnormal development of cells that reproduce uncontrollably and without any external stimulation. If tumors are not found early enough, brain tumors can be fatal to one’s health. Specialists and neurosurgeons employ magnetic resonance imaging (MRI) scans to diagnose brain tumors. Several deep learning methods for detecting the existence of brain tumors have been developed to overcome these constraints. The accurate detection of the size and location of a brain tumor is crucial in the diagnosis of the tumor. Medical image processing is a highly complex and tough discipline in which image processing and its methods are an active research topic. There are various technical deep learning and machine learning algorithms which are used to detect brain tumor. We used CNN architecture, ResNet, VGG16 and inception network in this paper and did a comparative study to find out the maximum accuracy for detecting brain tumor. When these algorithms are imposed to MRI images, the prediction of brain tumours is done quickly, and the higher accuracy aids in the treatment of patients. In this paper, after complete procedure and analysis of four different algorithms, we found out that CNN architecture is the most suitable with highest accuracy.
Automatic Number Plate Recognition System for Indian Number Plates using Machine Learning Techniques
Authors: Hajare Gayatri , Kharche Utkarsh , Mahajan Pritam , Shinde Apurva
Published: May 2022

India being a country where the population is above 1.3 billion where each person has at least one car of his/her use. Considering this, the number of cars driven on the roads of India must be greater than the population of the people in the country. India being a diverse country, diversity is not only seen in the language of the number plates but also in size, spacing between the letters on the number plate and the font of the number plate. Diversity differs from state to state. Even though most of the people are using English Number plates, there is no certain law as to how a number plate should be, so some people tend to have number plates according to their preferences. To withstand these problems, we have created a system using You Only Look Once version 5 (YOLOv5) for number plate detection and Google Tesseract for Character Recognition.
Comparison of Real-Time Face Detection and Recognition Algorithms
Authors: Borkar Yasar , Mascarenhas Reeve , Tambadkar Shubham , Gawande Jayanand P.
Published: May 2022

With the phenomenal growth of video and image databases, there is a tremendous need for intelligent systems to automatically understand and examine information, as doing so manually is becoming increasingly difficult. The face is important in social interactions because it conveys information. Detecting a person's identity and feelings Humans do not have a great deal of ability to identify. Machines have different faces. As a result, an automatic face detection system is essential.in face recognition, facial expression recognition, head-pose estimation, and human–computer interaction, and so on Face detection is a computer technology that determines the location and size of a person's face. It also creates a digital image of a human face. Face detection has been a standout topic in the science field This paper provides an in-depth examination of the various techniques investigated for face detection in digital images. Various face challenges and applications. This paper also discusses detection. Detection features are also provided. In addition, we hold special discussions on the practical aspects of developing a robust face detection system, and finally. This paper concludes with several promising research directions for the future.
Plant Disease Detection Using Cnn
Authors: Shelar Nishant , Shinde Suraj , Sawant Shubham , Dhumal Shreyash , Fakir Kausar
Published: May 2022

Plants and crops that are infected by pests have an impact on the country's agricultural production. Usually, farmers or professionals keep a close eye on the plants in order to discover and identify diseases. However, this procedure is frequently time-consuming, costly, and imprecise. Plant disease detection can be done by looking for a spot on the diseased plant's leaves. The goal of this paper is to create a Disease Recognition Model that is supported by leaf image classification. To detect plant diseases, we are utilizing image processing with a Convolution neural network (CNN). A convolutional neural network (CNN) is a form of artificial neural network that is specifically intended to process pixel input and is used in image recognition.
Disease Detection Using Artificial Intelligence
Authors: Shah Sumeet , Tattu Akshata , Sheregar Ashna , Mane Sneha
Published: May 2022

Currently, artificial intelligence is widely used to aid humans in a variety of ways. One area where artificial intelligence is particularly beneficial is medical image detection where diagnostic procedures require the collection and processing of large amounts of data for particular diseases. The topics covered in this paper include Pneumonia, Lung Cancer, and Brain Tumors. Early detection and treatment are crucial when treating these types of diseases. The paper describes the use of a convolutional neural network algorithm in order to process medical images so that it can aid in decision making process and help save time. Machine learning is useful in increasing consistency and accuracy in detection. The highest accuracy that the model has achieve 94.297%.