bluuebunny/crossref_metadata_2025_split huggingface dataset:
Constraints constrained
Authors: Schaefer Ronald P. , Egbokhare Francis O. 
Published: June 2022

Abstract
We investigate the framing typology for motion events initiated by Talmy (1985, 2000) relative to predications in Emai from West Benue Congo. We concentrate on Path, which appears either in a satellite or a verb. Two main framing patterns emerge. With a Cause co-event verb, Path lexicalizes in a verb when articulating punctual or durative contact but in a satellite when expressing basic dislocation and extended dislocation. Extended dislocation requires a Cause co-event verb in series with a verb of extended motion and a Path satellite. We interpret extended dislocation not as representing an additional framing type but as a reflex of Emai lexical and morphosyntactic resources. Consequently, we hold to a semantic typology whose primary design principle is Path assignment to a verb or a non-verb, with equipollent framing receiving a secondary status.
Sub Coulomb barrier d+
208Pb scattering in the time-dependent basis function approach
Authors: Yin Peng , Du Weijie , Zuo Wei , Zhao Xingbo , Vary James P 
Published: June 2022

Abstract
We employ the non-perturbative time-dependent basis function (tBF) approach to study the scattering of the deuteron on 208Pb below the Coulomb barrier. We obtain the bound and discretized scattering states of the projectile, which form the basis representation of the tBF approach, by diagonalizing a realistic Hamiltonian in a large harmonic oscillator basis. We find that the higher-order inelastic scattering effects are noticeable for sub barrier scatterings with the tBF method. We have successfully reproduced experimental sub Coulomb barrier elastic cross section ratios with the tBF approach by considering only the electric dipole (E1) component of the Coulomb interaction between the projectile and the target during scatterings. We find that the correction of the polarization potential to the Rutherford trajectory is dominant in reproducing the data at very low bombarding energies, whereas the role of internal transitions of the deuteron projectile induced by the E1 interaction during the scattering becomes increasingly significant at higher bombarding energies.
Fault diagnosis of industrial robot gearbox using joint interclass and intraclass mappings
Authors: Yang Shuai , Kong Lijie , Hou Ming , Bai Yun , Li Chuan 
Published: June 2022

Abstract
Due to complex structure and harsh operating conditions, it remains challenging to accurately diagnose industrial robot gearbox fault patterns. For this reason, a joint interclass and intraclass mappings (JIIM) strategy is presented in this paper to enhance the performance of industrial robot gearbox fault diagnosis. To this end, an echo state network (ESN) is first adopted to map the training data into their class centers for minimizing the interclass distance. Meanwhile, the maximization of interclass distance in the data space is achieved by equalizing the distance between class centers. For the mapped data, another ESN is proposed to classify the fault patterns. The proposed JIIM method is evaluated by fault-diagnosis experiments for industrial robot gearboxes. Improvements owing to minimizing the intraclass distance and equalizing the interclass distance are discussed in detail. Results show that the presented JIIM can efficiently improve the performance of industrial robot gearbox fault diagnosis.
A novel federated deep learning framework for diagnosis of partial discharge in gas-insulated switchgear
Authors: Wang Yanxin , Yan Jing , Jing Qianzhen , Wang Jianhua , Geng Yingsan 
Published: June 2022

Abstract
In recent years, many different deep learning methods have been developed to ensure the safe and stable operation of gas-insulated switchgear (GIS). However, the use of these methods to achieve excellent results depends on obtaining as much training data as possible, which is difficult to accomplish because of conflicts of interest among different clients and privacy concerns. To address this issue, this paper proposes a novel federated deep learning (FDL) for the diagnosis of partial discharge (PD) in GIS. A federated learning (FL) based on an improved federated averaging algorithm is proposed, which allows different clients to collaboratively participate in model training and preserves data privacy. In addition, a novel subtractive attention Siamese network is introduced for feature extraction and classification, which achieves the high-precision classification of unbalanced data. Experimental results showed that the diagnostic accuracy of the proposed FDL reached 95.61%, which was significantly higher than that achieved by other methods. The proposed FDL can also achieve excellent performance in the case of unbalanced samples and small samples. As a distributed learning, FL does not require clients to share data, and clients can collaboratively develop an effective global diagnostic model, which provides a novel solution for GIS PD diagnosis.
Photocatalytic activity of green synthesized cadmium sulfide quantum dots on the removal of RhB dye and its cytotoxicity and antibacterial studies
Authors: Dadmehr Mehdi , Korouzhdehi Behnaz , Tavassoli Abolfazl , Malekkiani Mitra 
Published: June 2022

Abstract
Presence of inorganic pollutants in water reservoirs is the treating factor for human health and environment. Semiconductor quantum dots (QDs) has been regarded as one of the most efficient nanoparticles for their enhanced photocatalytic activity. Medicinal plants are the safe sources to provide green template for biosynthesis of inorganic nanoparticles such as quantum dots. In order to determine the photocatalytic and biological application of cadmium sulfide quantum dots (CdS QDs), a biosynthesis approach was employed using saffron (Crocus sativus L.) stigma extract as the green reaction substrate. The biosynthesis process was evaluated at different pH condition to obtain the most efficient CdS QDs. Characterization of prepared CdS QDs were determined through UV–vis and fluorescence spectroscopy, FTIR and TEM analysis. The obtained results showed well dsispersed and uniform QDs during green synthesis at the optimum condition. The absorption and electrical properties of green synthesized CdS QDs showed the lowest energy bandgap of 2.4 at pH 11. Photocatalytic activity of CdS QDs on Rhodamine B degradation showed 92% degradation after 80 min under UV light irradiation. The antibacterial and cell cytotoxicity of green synthesized CdS QDs were assayed by disk diffusion and MTT assays respectively. Obtained results showed significant antibacterial effect of CdS QDs against gram-positive and gram-negative bacteria including B. subtilis (90%) and E. coli (96%) respectively. Moreover, cytotoxicity of prepared CdS Qds through MTT assay indicated 79% apoptosis induction on MCF-7 breast cancer cells.
Visualisation of matrix product: using light to clarify an abstract mathematical concept
Authors: Pinochet Jorge , Bussenius Cortada Walter 
Published: June 2022

Abstract
Teaching the noncommutativity of the product of matrices to high school or college level students is a difficult task when approached from a purely formal perspective. The aim of this paper is to present a simple experimental activity for teaching the noncommutativity of the matrix product, based on the Jones calculus, a mathematical formalism for describing polarised light by means of matrices and vectors. This activity can also be useful to introduce students to the use of matrices in physics, and to illustrate how abstract mathematics can become a powerful tool to help us explain and describe the real world.
Development of an operation trajectory design algorithm for control of multiple 0D parameters using deep reinforcement learning in KSTAR
Authors: Seo J. , Na Y.-S. , Kim B. , Lee C.Y. , Park M.S. , Park S.J. , Lee Y.H. 
Published: June 2022

Abstract
This work develops an artificially intelligent (AI) tokamak operation design algorithm that provides an adequate operation trajectory to control multiple plasma parameters simultaneously into different targets. An AI is trained with the reinforcement learning technique in the data-driven tokamak simulator, searching for the best action policy to get a higher reward. By setting the reward function to increase as the achieved β
p, q
95, and l
i are close to the given target values, the AI tries to properly determine the plasma current and boundary shape to reach the given targets. After training the AI with various targets and conditions in the simulation environment, we demonstrated that we could successfully achieve the target plasma states with the AI-designed operation trajectory in a real KSTAR experiment. The developed algorithm would replace the human task of searching for an operation setting for given objectives, provide clues for developing advanced operation scenarios, and serve as a basis for the autonomous operation of a fusion reactor.
Optimisation studies of the TAIGA-Muon scintillation detector array
Authors: Astapov I. , Bezyazeekov P. , Blank M. , Bonvech E. , Borodin A. , Brueckner M. , Budnev N. , Bulan A. , Chernov D. , Chiavassa A. , Dyachok A. , Gafarov A. , Garmash A. , Grebenyuk V. , Gress E. , Gress O. , Gress T. , Grinyuk A. , Grishin O. , Horns D. , Igoshin A. , Ivanova A.D. , Ivanova A.L. , Kalmykov N. , Kindin V. , Kiryuhin S. , Kokoulin R. , Kompaniets K. , Korosteleva E. , Kozhin V. , Kravchenko E. , Kryukov A. , Kuzmichev L. , Lagutin A. , Lavrova M. , Lemeshev Y. , Lubsandorzhiev B. , Lubsandorzhiev N. , Lukanov A. , Lukyantsev D. , Malakhov S. , Mirgazov R. , Mirzoyan R. , Monkhoev R. , Osipova E. , Pakhorukov A. , Pan A. , Pankov L. , Panov L. , Petrukhin A. , Poddubnyi I. , Podgrudkov D. , Poleschuk V. , Ponomareva V. , Popesku M. , Popova E. , Porelli A. , Postnikov E. , Prosin V. , Ptuskin V. , Pushnin A. , Raikin R. , Rubtsov G. , Ryabov E. , Sagan Y. , Samoliga V. , Satyshev I. , Silaev A. , Silaev(junior) A. , Sidorenkov A. , Sinegovsky S. , Skurikhin A. , Sokolov A. , Sulakov V. , Sveshnikova L. , Tabolenko V. , Tanaev A. , Tarashchansky B. , Ternovoy M. , Tkachev L. , Tluczykont M. , Togoo R. , Ushakov N. , Vaidyanathan A. , Volchugov P. , Volkov N. , Voronin D. , Wischnewski R. , Zagorodnikov A. , Zhaglova A. , Zhurov D. , Yashin I. 
Published: June 2022

Abstract
The TAIGA astroparticle observatory is progressing with the
deployment of new detector stations. The Tunka-Grande —
scintillation counter array of the observatory expands with the new
TAIGA-Muon stations. Several simulation studies were conducted for
optimisation of the new station positioning and
performance. Extensive air showers induced by gamma quanta or a
proton in the range from 100 TeV to 1 PeV at a zenith angle of 0°
were used for these studies. Based on the developed simulation, the
capabilities of identification of high energy extensive air showers
were studied. The soil thickness, the detector and station
positions, the lowest measurable energy range of the cosmic rays,
and different methods of air shower identification were
investigated.
Study on the performance of a resistive Micromegas in a controlled O2 environment
Authors: Bhattacharya Deb Sankar , Swirski Thorben , Böhm Burkhard , Ströhmer Raimund , Trefzger Thomas 
Published: June 2022

Abstract
Being the state of the art, the Micro-Pattern Gaseous Detectors (MPGD) are widely accepted in almost every particle collider experiment. Micromegas is a type of MPGD which is famous for its simple single-stage amplification, high and stable gain, low ion feedback, and excellent spatial and temporal resolutions.
The chance of O2 contamination in a gaseous detector is not negligible as the abundance of O2 in air is nearly 21%. Contamination of detector-gas by O2 may occur due to insufficient gas-tightness or the use of long plastic pipes. Being an electronegative gas, O2 attach with the electrons inside a gaseous detector and influence the performance. The concern is higher when the detector has a large scale extension.
We are reporting our measurements on the diffusion of O2 through a couple of commonly used plastic pipes. Then, in a hermetic gas system, by infusing O2 in a controlled and precise way, we have measured the effects of O2 on the performance of a resistive Micromegas. In pure Ar-CO2 mixture, we have also compared the gas-gain of the Micromegas with simulation to better estimate the gas-gap. The effects of temperature and pressure fluctuations on our study have also been investigated.
The feasibility of reconstructing hydroclimate over West Africa using tree-ring chronologies in the Mediterranean region
Authors: Fosu Boniface O , Cook Edward R , Biasutti Michela , Buckley Brendan M , Nicholson Sharon E 
Published: June 2022

Abstract
Dendrochronology in West Africa has not yet been developed despite encouraging reports suggesting the potential for long tree-ring reconstructions of hydroclimate in the tropics. This paper shows that even in the absence of local tree chronologies, it is possible to reconstruct the hydroclimate of a region using remote tree rings. We present the West Sub-Saharan Drought Atlas (WSDA), a new paleoclimatic reconstruction of West African hydroclimate based on tree-ring chronologies from the Mediterranean Region, made possible by the teleconnected climate relationship between the West African Monsoon and Mediterranean Sea surface temperatures. The WSDA is a one-half degree gridded reconstruction of summer Palmer Drought Severity indices from 1500 to 2018 CE, produced using ensemble point-by-point regression. Calibration and verification statistics of the WSDA indicate that it has significant skill over most of its domain. The three leading modes of hydroclimate variability in West Africa are accurately reproduced by the WSDA, demonstrating strong skill compared to regional instrumental precipitation and drought indices. The WSDA can be used to study the hydroclimate of West Africa outside the limit of the longest observed record and for integration and comparison with other proxy and archaeological data. It is also an essential first step toward developing and using local tree-ring chronologies to reconstruct West Africa’s hydroclimate.
Disentangling physical and dynamical drivers of the 2016/17 record-breaking warm winter in China
Authors: Zhang Tuantuan , Deng Yi , Chen Junwen , Yang Song , Gao Peng , Zhang Henian 
Published: June 2022

Abstract
Understanding the physical and dynamical origin of regional climate extremes remains a major challenge in our effort to anticipate the occurrences and mitigate the adverse impacts of these extremes. China was hit by a record-breaking hot winter in 2016/17 with remarkable surface warming over the northern and southeastern regions. Here we made a quantitative attribution of this 2016/17 winter’s surface temperature anomalies utilizing an updated version of the coupled atmosphere-surface climate feedback response analysis method (CFRAM), that recently incorporates the effect of aerosols and more species of trace gases (CFRAM-A). The CFRAM-A analysis reveals that the overall warming pattern may be largely attributed to the effects of anomalous water vapor, atmospheric dynamics, and aerosols, followed by anomalies of surface albedo, clouds, solar irradiance, ozone, and carbon dioxide. The effect of methane turns out to be negligible. Anomalies in surface dynamics provides an overall cooling effect, compensating the warming associated with other processes to some extent. Among the three major positive contributors, effects of water vapor and atmospheric dynamics prevail over both northern and southeastern China while the impact of anthropogenic aerosols appears much more pronounced over southeastern China, likely due to the implementation of highly effective emission reduction policies in China since 2013. The CFRAM-A thus provides an efficient, model-free approach for quantitatively understanding sources of regional temperature extremes and for assessing the impacts of environmental policies regulating anthropogenic emissions of aerosols and atmospheric trace gases.
Gravity-based patterning of osteogenic factors to preserve bone structure after osteochondral injury in a large animal model
Authors: Zlotnick Hannah M , Locke Ryan C , Hemdev Sanjana , Stoeckl Brendan D , Gupta Sachin , Peredo Ana P , Steinberg David R , Carey James L , Lee Daeyeon , Dodge George R , Mauck Robert L 
Published: June 2022

Abstract
Chondral and osteochondral repair strategies are limited by adverse bony changes that occur after injury. Bone resorption can cause entire scaffolds, engineered tissues, or even endogenous repair tissues to subside below the cartilage surface. To address this translational issue, we fabricated thick-shelled poly(D,L-lactide-co-glycolide) microcapsules containing the pro-osteogenic agents triiodothyronine and β-glycerophosphate, and delivered these microcapsules in a large animal model of osteochondral injury to preserve bone structure. We demonstrate that the developed microcapsules ruptured in vitro under increasing mechanical loads, and readily sink within a liquid solution, enabling gravity-based patterning along the osteochondral surface. In a large animal, these mechanically-activated microcapsules (MAMCs) were assessed through two different delivery strategies. Intra-articular injection of control MAMCs enabled fluorescent quantification of MAMC rupture and cargo release in a synovial joint setting over time in vivo. This joint-wide injection also confirmed that the MAMCs do not elicit an inflammatory response. In the contralateral hindlimbs, chondral defects were created, MAMCs were patterned in situ, and nanofracture (Nfx), a clinically utilized method to promote cartilage repair, was performed. The Nfx holes enabled marrow-derived stromal cells to enter the defect area and served as repeatable bone injury sites to monitor over time. Animals were evaluated one and two weeks after injection and surgery. Analysis of injected MAMCs showed that bioactive cargo was released in a controlled fashion over two weeks. A bone fluorochrome label injected at the time of surgery displayed maintenance of mineral labeling in the therapeutic group, but resorption in both control groups. Alkaline phosphatase (AP) staining at the osteochondral interface revealed higher AP activity in defects treated with therapeutic MAMCs. Overall, this study develops a gravity-based approach to pattern bioactive factors along the osteochondral interface, and applies this novel biofabrication strategy to preserve bone structure after osteochondral injury.
Lightweight jet reconstruction and identification as an object detection task
Authors: Pol Adrian Alan , Aarrestad Thea , Govorkova Ekaterina , Halily Roi , Klempner Anat , Kopetz Tal , Loncar Vladimir , Ngadiuba Jennifer , Pierini Maurizio , Sirkin Olya , Summers Sioni 
Published: June 2022

Abstract
We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN large hadron collider (LHC). Collision events produced at the LHC and represented as an image composed of calorimeter and tracker cells are given as an input to a Single Shot Detection network. The algorithm, named PFJet-SSD performs simultaneous localization, classification and regression tasks to cluster jets and reconstruct their features. This all-in-one single feed-forward pass gives advantages in terms of execution time and an improved accuracy w.r.t. traditional rule-based methods. A further gain is obtained from network slimming, homogeneous quantization, and optimized runtime for meeting memory and latency constraints of a typical real-time processing environment. We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point. We show that the ternary network closely matches the performance of its full-precision equivalent and outperforms the state-of-the-art rule-based algorithm. Finally, we report the inference latency on different hardware platforms and discuss future applications.
Sparcle: assigning transcripts to cells in multiplexed images
Authors: Prabhakaran Sandhya 
Published: June 2022

AbstractMotivationImaging-based spatial transcriptomics has the power to reveal patterns of single-cell gene expression by detecting mRNA transcripts as individually resolved spots in multiplexed images. However, molecular quantification has been severely limited by the computational challenges of segmenting poorly outlined, overlapping cells and of overcoming technical noise; the majority of transcripts are routinely discarded because they fall outside the segmentation boundaries. This lost information leads to less accurate gene count matrices and weakens downstream analyses, such as cell type or gene program identification.ResultsHere, we present Sparcle, a probabilistic model that reassigns transcripts to cells based on gene covariation patterns and incorporates spatial features such as distance to nucleus. We demonstrate its utility on both multiplexed error-robust fluorescence in situ hybridization, single-molecule FISH data, probabilistic cell typing in situ sequencing, spatially resolved transcript amplicon readout mapping and MERFISH from Vizgen. Sparcle improves transcript assignment, providing more realistic per-cell quantification of each gene, better delineation of cell boundaries and improved cluster assignments. Critically, our approach does not require an accurate segmentation and is agnostic to technological platform.Availability and implementationThe code is available at: https://github.com/sandhya212/Sparcle_for_spot_reassignmentsContactsandhya.prabhakaran@moffitt.orgSupplementary informationSupplementary data are available at Bioinformatics Advances online.
MarkerMAG: linking metagenome-assembled genomes (MAGs) with 16S rRNA marker genes using paired-end short reads
Authors: Song Weizhi , Zhang Shan , Thomas Torsten 
Published: June 2022

AbstractMotivationMetagenome-assembled genomes (MAGs) have substantially extended our understanding of microbial functionality. However, 16S rRNA genes, which are commonly used in phylogenetic analysis and environmental surveys, are often missing from MAGs. Here, we developed MarkerMAG, a pipeline that links 16S rRNA genes to MAGs using paired-end sequencing reads.ResultsAssessment of MarkerMAG on three benchmarking metagenomic datasets with various degrees of complexity shows substantial increases in the number of MAGs with 16S rRNA genes and a 100% assignment accuracy. MarkerMAG also estimates the copy number of 16S rRNA genes in MAGs with high accuracy. Assessments on three real metagenomic datasets demonstrate 1.1- to 14.2-fold increases in the number of MAGs with 16S rRNA genes. We also show that MarkerMAG-improved MAGs increase the accuracy of functional prediction from 16S rRNA gene amplicon data. MarkerMAG is helpful in connecting information in MAG databases with those in 16S rRNA databases and surveys and hence contributes to our increasing understanding of microbial diversity, function and phylogeny.Availability and implementationMarkerMAG is implemented in Python3 and freely available at https://github.com/songweizhi/MarkerMAG.Supplementary informationSupplementary data are available at Bioinformatics online.