bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linguistic expressions of Goal, Source and Place in Polynesian languages
Authors: Moyse-Faurie Claire 
Published: November 2020

Abstract
In Polynesian languages, as in many other Oceanic languages, the linguistic expression of Source and Goal is mainly express by (i) demonstratives and directional modifiers, which combine deictic and spatial information (toward speaker, addressee or third person, upwards, downwards, transverse axe), (ii) locative static and dynamic prepositions which may combine with body-part terms to introduce local and landmark nouns, or place names, and (iii) posture and motion verbs. We examine the occurrences of the Source and Goal prepositions on the one hand, and the directional modifiers on the other, taking into account their compatibilities, the spatial coding they convey, the position of the participants, and the verb meaning. In Polynesian languages, Goal and Source are of similar complexity, though in different ways, and a variety of resources can express fine-grained distinctions for Source vs. Goal depending on the position of the figure.
Asymmetries in Path expression in Ye’kwana
Authors: Cáceres Arandia Natalia 
Published: November 2020

Abstract
Ye’kwana is an Amazonian language of the Cariban family spoken by a group of about 8,700 people in Venezuela and Brazil.
This paper explores the expression of Path in spontaneous motion events based on spoken data collected for the documentation and description
of the language including data collected with the Trajectoire elicitation material (Ishibashi et al. 2006).
In Ye’kwana, Path is mainly expressed by postpositional and adverbial stems: there is a rich inventory of 80 postpositions
all compatible with locative and either allative or perlative uses and 29 spatial adverbs, most of deictic nature. Source is expressed with
a dedicated suffix (-nno) which combines with almost all the spatial postpositions and adverbs. The data show that the
asymmetries in the expression of Path are not only found between Source and Goal but also need to include the expression of Medium for which
the language has dedicated forms.
Source-Goal (a)symmetry in Romanian
Authors: Papahagi Cristiana 
Published: November 2020

Abstract
According to Talmy (2000), a motion event has four conceptual components: Figure, Motion, Path and one or more Grounds. Path can be further decomposed into Source, Medium and Goal (or: departure, passing and arrival). In many languages, intuitive pairs of motion events such as come/go seem to indicate that Source and Goal are equally able to build the image of the Path. However, numerous studies have pointed to an asymmetry in favor of Goal in motion descriptions. Using the corpus elicited during the Trajectoire project, this paper explores Source-Goal asymmetries in Romanian; this concerns adposition inventories (which are symmetrical for Source and Goal), adposition-verb combinations, and the attention payed by speakers to Source viz. Goal-oriented motion. The paper postulates possible semantic causes of Source-Goal asymmetry not identified in previous literature, such as the bounded nature of the Ground, and motion being associated with a particular human activity.
Multi-branch convolutional neural networks with integrated cross-entropy for fault diagnosis in diesel engines
Authors: Zhao Haipeng , Mao Zhiwei , Zhang Jinjie , Zhang Xudong , Zhao Nanyang , Jiang Zhinong 
Published: November 2020

Abstract
Fault diagnosis based on deep learning has become a hot research topic because of the successful application of deep learning in other fields. Due to variable operating conditions and a harsh operating environment, it is extremely difficult to effectively diagnose some typical faults of diesel engines. When operating conditions and environmental factors change, the performance of deep learning models also become extremely unstable. In order to solve these problems, this paper proposes a novel deep learning model, called multi-branch convolutional neural networks (MBCNNs) with an integrated cross-entropy. MBCNN can be embedded in the proposed model and simultaneously equipped with four auxiliary classifiers. The proposed model is trained on two different datasets separately, which consist of six diesel engine faults. The trained network model was compared with other methods to prove the superiority of this network model. Meanwhile, by adding Gaussian white noise, the performance of the MBCNN in different noise environments is investigated. The final results show that the MBCNN with integrated cross-entropy can effectively diagnose different diesel engine faults under variable operating conditions.
A four-layer Ag-ZnO-LPFG structure for improving temperature sensitivity and coupled-wavelength transmittance stability
Authors: Wang Jin , Wang Li , Su Xueqiong , Pan Yong , Gao Dongwen , Cheng Peng , Yan Huanhuan 
Published: November 2020

Abstract
A novel long-period fiber grating (LPFG) with a four-layer structure is proposed, made by coating double layers of both ZnO and Ag onto a bare LPFG. Pulsed laser deposition (PLD) technology can effectively control the flatness and nanoscale thickness of ZnO film, which is conducive to achieving full energy coupling with the Ag layer and the grating. Compared with SiO2, ZnO has a higher thermal expansion coefficient and refractive index, which ensures that coated LPFG has a higher temperature sensitivity. As the fourth layer of the structure, colloidal silver is coated onto the surface of the ZnO film at an optimal thickness by a spin-coating method. The higher thermal expansion coefficient of the Ag film and a plasma resonance effect on its surface cause the temperature sensitivity to increase again. The sensitivity of the coated Ag-ZnO-LPFG structure is 1.4 times higher than that of the bare fiber. The temperature sensitivity is increased from 0.063 nm °C−1 to 0.088 nm °C−1, while retaining good linearity. We also solve the problem whereby the coating disturbs the coupled-wavelength transmittance, and by doing so, we increase the stability of the coupled-wavelength transmittance and enhance measurement accuracy.
Adaptive latent state modeling of brain network dynamics with real-time learning rate optimization
Authors: Yang Yuxiao , Ahmadipour Parima , Shanechi Maryam M 
Published: November 2020

AbstractObjective. Dynamic latent state models are widely used to characterize the dynamics of brain network activity for various neural signal types. To date, dynamic latent state models have largely been developed for stationary brain network dynamics. However, brain network dynamics can be non-stationary for example due to learning, plasticity or recording instability. To enable modeling these non-stationarities, two problems need to be resolved. First, novel methods should be developed that can adaptively update the parameters of latent state models, which is difficult due to the state being latent. Second, new methods are needed to optimize the adaptation learning rate, which specifies how fast new neural observations update the model parameters and can significantly influence adaptation accuracy.Approach. We develop a Rate Optimized-adaptive Linear State-Space Modeling (RO-adaptive LSSM) algorithm that solves these two problems. First, to enable adaptation, we derive a computation- and memory-efficient adaptive LSSM fitting algorithm that updates the LSSM parameters recursively and in real time in the presence of the latent state. Second, we develop a real-time learning rate optimization algorithm. We use comprehensive simulations of a broad range of non-stationary brain network dynamics to validate both algorithms, which together constitute the RO-adaptive LSSM.Main results. We show that the adaptive LSSM fitting algorithm can accurately track the broad simulated non-stationary brain network dynamics. We also find that the learning rate significantly affects the LSSM fitting accuracy. Finally, we show that the real-time learning rate optimization algorithm can run in parallel with the adaptive LSSM fitting algorithm. Doing so, the combined RO-adaptive LSSM algorithm rapidly converges to the optimal learning rate and accurately tracks non-stationarities.Significance. These algorithms can be used to study time-varying neural dynamics underlying various brain functions and enhance future neurotechnologies such as brain-machine interfaces and closed-loop brain stimulation systems.
Decoding working memory task condition using magnetoencephalography source level long-range phase coupling patterns
Authors: Syrjälä Jaakko , Basti Alessio , Guidotti Roberto , Marzetti Laura , Pizzella Vittorio 
Published: November 2020

Abstract

Objective. The objective of the study is to identify phase coupling patterns that are shared across subjects via a machine learning approach that utilises source space magnetoencephalography (MEG) phase coupling data from a working memory (WM) task. Indeed, phase coupling of neural oscillations is putatively a key factor for communication between distant brain areas and is therefore crucial in performing cognitive tasks, including WM. Previous studies investigating phase coupling during cognitive tasks have often focused on a few a priori selected brain areas or a specific frequency band, and the need for data-driven approaches has been recognised. Machine learning techniques have emerged as valuable tools for the analysis of neuroimaging data since they catch fine-grained differences in the multivariate signal distribution. Here, we expect that these techniques applied to MEG phase couplings can reveal WM-related processes that are shared across individuals. Approach. We analysed WM data collected as part of the Human Connectome Project. The MEG data were collected while subjects (n = 83) performed N-back WM tasks in two different conditions, namely 2-back (WM condition) and 0-back (control condition). We estimated phase coupling patterns (multivariate phase slope index) for both conditions and for theta, alpha, beta, and gamma bands. The obtained phase coupling data were then used to train a linear support vector machine in order to classify which task condition the subject was performing with an across-subject cross-validation approach. The classification was performed separately based on the data from individual frequency bands and with all bands combined (multiband). Finally, we evaluated the relative importance of the different features (phase couplings) for classification by the means of feature selection probability. Main results. The WM condition and control condition were successfully classified based on the phase coupling patterns in the theta (62% accuracy) and alpha bands (60% accuracy) separately. Importantly, the multiband classification showed that phase coupling patterns not only in the theta and alpha but also in the gamma bands are related to WM processing, as testified by improvement in classification performance (71%). Significance. Our study successfully decoded WM tasks using MEG source space functional connectivity. Our approach, combining across-subject classification and a multidimensional metric recently developed by our group, is able to detect patterns of connectivity that are shared across individuals. In other words, the results are generalisable to new individuals and allow meaningful interpretation of task-relevant phase coupling patterns.
Biofabrication of 3D printed hydroxyapatite composite scaffolds for bone regeneration
Authors: Kim Yoontae , Lee Eun-Jin , Davydov Albert V , Frukhtbeyen Stanislav , Seppala Jonathan E , Takagi Shozo , Chow Laurence , Alimperti Stella 
Published: November 2020

Abstract
Biofabrication has been adapted in engineering patient-specific biosynthetic grafts for bone regeneration. Herein, we developed a three-dimensional (3D) high-resolution, room-temperature printing approach to fabricate osteoconductive scaffolds using calcium phosphate cement (CPC). The non-aqueous CPC bioinks were composed of tetracalcium phosphate, dicalcium phosphate anhydrous, and Polyvinyl butyral (PVB) dissolved in either ethanol (EtOH) or tetrahydrofuran (THF). They were printed in an aqueous sodium phosphate bath, which performs as a hardening accelerator for hydroxyapatite formation and as a retainer for 3D microstructure. The PVB solvents, EtOH or THF, affected differently the slurry rheological properties, scaffold microstructure, mechanical properties, and osteoconductivity. Our proposed approach overcomes limitations of conventional fabrication methods, which require high-temperature (>50 °C), low-resolution (>400 μm) printing with an inadequate amount of large ceramic particles (>35 μm). This proof-of-concept study opens venues in engineering high-resolution, implantable, and osteoconductive scaffolds with predetermined properties for bone regeneration.
Reversing the cytotoxicity of uric acid by supramolecular encapsulation with acyclic cucurbit[n]uril
Authors: Kong Lingguang , Liang Jing , Yang Lei , An Shu , Gao Chuanzhu , Liao Xiali , Zhao Yulin , Yang Bo 
Published: November 2020

Abstract
Supramolecular encapsulation, which removes harmful substances from organisms, has evolved into a new strategy. In this paper, three supramolecular complexes of acyclic cucurbit[n]urils (ACBs) with uric acid (UA) were prepared, and the inclusion behavior of ACBs and UA was studied by fluorescence spectroscopy, UV–vis spectroscopy and nuclear magnetic resonance. Furthermore, the effect of the complexes of UA with ACBs on the expression of inflammatory biomarkers in human hepatoma HepG2 cell lines was characterized through C-reactive protein (CRP) western blot. The results showed UA molecules can be recognized by three ACBs with different binding constants, and ACBs successfully blocked the inflammatory stimulation of UA on HepG2 cell lines and inhibited the expression of the major inflammatory factor CRP by the formation of complexes between UA and ACBs. This article proves that ACBs can efficiently reverse the cytotoxicity of UA, which provides a new method for treating hyperuricemia disease.
Is working less really good for the environment? A systematic review of the empirical evidence for resource use, greenhouse gas emissions and the ecological footprint
Authors: Antal Miklós , Plank Barbara , Mokos Judit , Wiedenhofer Dominik 
Published: November 2020

Abstract
Is reducing paid working time (WT) a potential win-win climate change mitigation strategy, which may simultaneously serve environmental sustainability and human well-being? While some researchers and commentators frequently refer to such ‘double-dividends’, most climate and environmental discussions ignore this topic. The societal relevance of paid WT and the potential role of its reduction as a demand-side measure for mitigating the climate- and ecological crisis calls for a critical review of the evidence. Here we systematically review the empirical, quantitative literature on the relationships between paid WT and a number of environmental indicators: resource use (incl. energy), greenhouse gas emissions and the ecological footprint. We applied two comprehensive search queries in two scientific databases; screened ∼2500 articles published until December 2019, and used citation snowballing to identify relevant research. However, we only found 15 fully relevant studies, as well as a number of partially relevant ones. This literature employs substantially different scopes, indicators and statistical methods, each with important caveats, which inhibits a formal quantitative evidence synthesis but usefully informs a critical discussion of the research frontier. Most studies conclude that reductions in paid WT reduce environmental pressures, primarily by decreasing incomes and consumption expenditures. However, existing research does not provide reliable guidance beyond the established link between expenditures and environmental impacts. Quantifying the effects of time use changes and macro-economic feedbacks through productivity, employment, and the complementarity or substitution between human labour and natural resources in production processes has proven to be difficult. To better understand the environmental impacts of specific types of WT reductions, new forms of data collection as well as studies at different scales and scopes are required. The critical discussion of the existing literature helps to conceptually map the pathways investigated so far and to identify crucial next steps towards more robust insights.
Role of interactions in a closed quenched driven diffusive system
Authors: Pal Bipasha , Gupta Arvind Kumar 
Published: November 2020

Abstract
We study the non-equilibrium steady states in a closed system consisting of interacting particles obeying exclusion principle with quenched hopping rate. Cluster mean field approach is utilized to theoretically analyze the system dynamics in terms of phase diagram, density profiles, current, etc, with respect to interaction energy E. It turns out that on increasing the interaction energy beyond a critical value, E
c, shock region shows non-monotonic behavior and contracts until another critical value

E

c

1

is attained; a further increase leads to its expansion. Moreover, the phase diagram of an interacting system with specific set of parameters has a good agreement with its non-interacting analogue. For interaction energy below E
c, a new shock phase displaying features different from non-interacting version is observed leading to two distinct shock phases. We have also performed Monte Carlo simulations extensively to validate our theoretical findings.
Experimental study of forming limit diagram and mechanical properties of aluminum foils processed by the accumulative roll bonding
Authors: Alisaraei E A , Hashemi R , Rahmatabadi D , Sommitsch C 
Published: November 2020

Abstract
In this article, for the first time, the forming limit diagram (FLD) and mechanical properties of aluminum foil samples processed by the accumulative roll bonding (ARB) process have been studied experimentally. For this purpose, thin aluminum foils with a thickness of 200 microns have been produced using ARB in five passes at ambient temperature. By rising the number of ARB passes, the ultimate tensile strength (UTS) enhanced drastically, and at the end pass of ARB, it reached 393 MPa, about 5.9 times larger than the initial sample. Also, during the ARB process, the applied strain increased, and the thickness of the layers decreased, and the bonding quality between layers improved. SEM images of tensile fracture surface after five cycles showed the mechanism of fracture retained ductile. However, due to the unevenly applied strain, the dimples were drawn in different directions, and their depth and number were reduced relative to the raw material. The area under the FLDs, a criterion of formability, declined sharply after the first pass and then increased at a low rate until the final pass. The trend of similar changes of formability in the tensile (elongation) and Nakazima tests (FLDs) was reported. Responsibility for all mechanical properties and ductility changes is related to the ARB process’s nature and the two dominant mechanisms of strain hardening and grain refinement.
Fish mucus mediated biosynthesis of copper oxide nanoparticles: spectral characterization, morphology and biological activity
Authors: Chinnadurai G , Subramanian R , Ahamed Maqusood 
Published: November 2020

Abstract
Simple methodology was developed to synthesize copper oxide nanoparticles (CuO NPs) using mucus of Channa striatus (C. striatus). The mucus of C. striatus is known for its biological properties due to the presence of numerous amino acids. This mucus was used as stabilizing agent for CuO NPs synthesis from copper acetate. The prepared CuO NPs were characterized by fourier transforms infrared spectrometer (FTIR), powder x-ray diffraction (XRD), x-ray photoelectron spectroscopy (XPS), scanning electron microscope (SEM), energy dispersive x-ray analysis (EDX) and transmission electron microscope (TEM) coupled with selected area diffraction pattern (SAED). The FTIR study suggested the utilization of mucus in the synthesis of CuO NPs. The XRD data also confirmed formation of pure crystalline phase of CuO NPs. Fish mucus stabilized CuO NPs exhibited significant activity against HeLa cells. The results of cell death clearly indicated that the synthesized CuO nanoparticles could be served as a biomaterial for anticancer treatment.
Retrospective Analyses Associate Hemostasis Activation Biomarkers With Poor Outcomes in Patients With COVID-19
Authors: Moosavi Mitchell , Wooten Melanie , Goodman Abigail , Nahab Fadi B , Duncan Alexander , Maier Cheryl , Guarner Jeannette 
Published: November 2020

Abstract

Objectives
Patients with coronavirus disease 2019 (COVID-19) have thromboembolic complications. Assessment of coagulation and other markers could be useful to understand their coagulopathy.

Methods
We performed a retrospective study of inflammatory and coagulation parameters, including prothrombin fragment 1.2 (PF1.2), thrombin-antithrombin complexes (TATs), fibrin monomers, and D-dimer, in hospitalized patients with COVID-19. We compared the markers in patients with thrombosis, admission to the intensive care unit (ICU), and poor outcome.

Results
Of the 81 patients, 9 (11%) experienced an acute thrombotic event (4 with pulmonary embolism, 3 with venous thrombosis, and 2 with stroke). PF1.2 was elevated in 32 (39%) patients, TATs in 54 (67%), fibrin monomers in 49 (60%), and D-dimer in 76 (94%). Statistically significant elevation in PF1.2 and TATs was seen in patients admitted to the ICU, while D-dimer and fibrin monomers were significantly elevated in patients with poor outcomes. The presence of multiple abnormal coagulation parameters was associated with ICU admission. Other parameters with statistically significant results included abnormal WBC counts and elevated C-reactive protein, which were associated with ICU admission and poor outcomes.

Conclusions
Our data demonstrate that abnormalities of biomarkers of hemostasis activation and inflammatory markers are associated with poor outcomes in patients with COVID-19.
Secondary Upper Blepharoplasty: Converting Static Folds Into Dynamic Folds
Authors: Huang Jiuzuo , Feng Shouyun , Dong Ruijia , Wang Hefei , Hamra Sam T , Liu Zhigang , Long Xiao 
Published: November 2020

Abstract

Background
For the patients seeking secondary upper blepharoplasty, a static double-eyelid fold featuring an immobile lower flap and depression of the fold is common.

Objectives
In this study, the authors propose a novel technique of reconstructing pretarsal tissue defects (PTDs) to converting static folds to dynamic folds.

Methods
A total of 203 patients with static folds underwent revision surgery. After complete adhesion release of the lower flap, a PTD was identified, which was defined as an area deficient of orbicularis oculi muscle in front of the tarsal plate. If the width of the PTD was over 2 mm, tissue transfer was performed to reconstruct the PTD, usually with a free retro-orbicularis oculus fat graft or a pretarsal orbicularis oculi flap.

Results
Among the 105 patients with severe static folds, 67 received retro-orbicularis oculus fat grafts and 38 received orbicularis oculi muscle flaps. This technique converted a static fold into a dynamic fold. The surgery satisfaction rate was 86.7%. Complications included partial fold loss (n = 7, 3.4%), complete fold loss (n = 3, 1.5%), sunken upper eyelids (n = 5, 2.5%), multiple folds (n = 3, 1.5%), an unnatural curve of the double fold (n = 5, 2.5%), and asymmetric folds (n = 4, 2.0%).

Conclusions
To convert a static fold to a dynamic fold, we devised a technique that releases adhesion of the lower flap and reconstructs the PTD with retro-orbicularis oculus fat graft or an orbicularis oculi muscle flap. Our study achieved a high patient satisfaction rate, and the resulting fold mimicked the dynamics of the congenital double-eyelid fold.

Level of Evidence: 4