bluuebunny/crossref_metadata_2025 huggingface dataset:
Hypotensive and antihypertensive effects of an aqueous extract from Guinep fruit (Melicoccus bijugatus Jacq) in rats
Authors: Nwokocha Chukwuemeka R. , Gordon Alexia , Palacios Javier , Paredes Adrian , Cifuentes Fredi , Francis Sheena , Watson JeAnn , Delgoda Rupika , Nwokocha Magdalene , Alexander-Lindo Ruby , Thompson Rory , Minott-Kates Donna , Yakubu Momoh A.
Published: October 2020

Melicoccus bijugatus Jacq (Mb) has been reported to have cardiovascular modulatory effects. In this study, we evaluated the antihypertensive effects and mechanism of action of Mb on NG-Nitro-l-arginine Methyl Ester (l-NAME) and Deoxycorticosterone Acetate (DOCA) rat models. Aqueous extract of Mb fruit (100 mg/kg) was administered for 6 weeks to rats by gavage and blood pressure was recorded. Effects of the extract on vascular reactivity was evaluated using isolated organ baths, and tissues were collected for biochemical and histological analysis. The systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP) were significantly (P < 0.05) reduced with extract (100 mg/kg) administration and treatment compared to the hypertensive models. Mb (100 µg/mL) reduced the vascular contractility induced by phenylephrine (PE), and caused a dose-dependent relaxation of PE-induced contraction of aortic vascular rings. The vasorelaxation properties seemed to be endothelium dependent, as well as nitric oxide (NO) and guanylyl cyclase, but not prostaglandin dependent. Histomicrograph of transverse sections of the ventricles from the Mb group did not show abnormalities. The extract significantly (P < 0.05) reduced an l-NAME induced elevation of cardiac output and Creatine Kinase Muscle-Brain (CKMB), but had no significant impact on the activities of arylamine N-acetyltransferase. In conclusion, Mb significantly decreased blood pressure in hypertensive models. The extract possesses the ability to induce endothelium dependent vasodilation, which is dependent on guanylyl cyclase but not prostaglandins.
Comparison of manual and machine learning image processing approaches to determine fungiform papillae on the tongue
Authors: Cattaneo Camilla , Liu Jing , Wang Chenhao , Pagliarini Ella , Sporring Jon , Bredie Wender L. P.
Published: October 2020

Human taste perception is associated with the papillae on the tongue as they contain a large proportion of chemoreceptors for basic tastes and other chemosensation. Especially the density of fungiform papillae (FP) is considered as an index for responsiveness to oral chemosensory stimuli. The standard procedure for FP counting involves visual identification and manual counting of specific parts of the tongue by trained operators. This is a tedious task and automated image analysis methods are desirable. In this paper a machine learning image processing method based on a convolutional neural network is presented. This automated method was compared with three standard manual FP counting procedures using tongue pictures from 132 subjects. Automated FP counts, within the selected areas and the whole tongue, significantly correlated with the manual counting methods (all ρs ≥ 0.76). When comparing the images for gender and PROP status, the density of FP predicted from automated analysis was in good agreement with data from the manual counting methods, especially in the case of gender. Moreover, the present results reinforce the idea that caution should be applied in considering the relationship between FP density and PROP responsiveness since this relationship can be an oversimplification of the complexity of phenomena arising at the central and peripherical levels. Indeed, no significant correlations were found between FP and PROP bitterness ratings using the automated method for selected areas or the whole tongue. Besides providing estimates of the number of FP, the machine learning approach used a tongue coordinate system that normalizes the size and shape of an individual tongue and generated a heat map of the FP position and normalized area they cover. The present study demonstrated that the machine learning approach could provide similar estimates of FP on the tongue as compared to manual counting methods and provide estimates of more difficult-to-measure parameters, such as the papillae's areas and shape.
Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data
Authors: Miura Kotaro , Goto Shinichi , Katsumata Yoshinori , Ikura Hidehiko , Shiraishi Yasuyuki , Sato Kazuki , Fukuda Keiichi
Published: October 2020

Regular aerobic physical activity is of utmost importance in maintaining a good health status and preventing cardiovascular diseases (CVDs). Although cardiopulmonary exercise testing (CPX) is an essential examination for noninvasive estimation of ventilatory threshold (VT), defined as the clinically equivalent to aerobic exercise, its evaluation requires an expensive respiratory gas analyzer and expertize. To address these inconveniences, this study investigated the feasibility of a deep learning (DL) algorithm with single-lead electrocardiography (ECG) for estimating the aerobic exercise threshold. Two hundred sixty consecutive patients with CVDs who underwent CPX were analyzed. Single-lead ECG data were stored as time-series voltage data with a sampling rate of 1000 Hz. The data of preprocessed ECG and time point at VT calculated by respiratory gas analyzer were used to train a neural network. The trained model was applied on an independent test cohort, and the DL threshold (DLT; a time of VT estimated through the DL algorithm) was calculated. We compared the correlation between oxygen uptake of the VT (VT–VO2) and the DLT (DLT–VO2). Our DL model showed that the DLT–VO2 was confirmed to be significantly correlated with the VT–VO2 (r = 0.875; P < 0.001), and the mean difference was nonsignificant (−0.05 ml/kg/min, P > 0.05), which displayed strong agreements between the VT and the DLT. The DL algorithm using single-lead ECG data enabled accurate estimation of VT in patients with CVDs. The DL algorithm may be a novel way for estimating aerobic exercise threshold.
Walker-like domain wall breakdown in layered antiferromagnets driven by staggered spin–orbit fields
Authors: Otxoa Rubén M. , Roy P. E. , Rama-Eiroa R. , Godinho J. , Guslienko K. Y. , Wunderlich J.
Published: October 2020

Within linear continuum theory, no magnetic texture can propagate faster than the maximum group velocity of the spin waves. Here, by atomistic spin dynamics simulations and supported by analytical theory, we report that a strongly non-linear transient regime due to the appearance of additional magnetic textures results in the breaking of the Lorentz translational invariance. This dynamical regime is akin to domain wall Walker-breakdown in ferromagnets and involves the nucleation of an antiferromagnetic domain wall pair. While one of the nucleated domain walls is accelerated beyond the magnonic limit, the remaining pair remains static. Under large spin–orbit fields, a cascade of multiple generation and recombination of domain walls are obtained. This result may clarify recent experiments on current pulse induced shattering of large domain structures into small fragmented domains and the subsequent slow recreation of large-scale domains.
Enhanced E/Z-photoisomerization and luminescence of stilbene derivative co-coordinated in di-β-diketonate lanthanide complexes
Authors: Chen Lu , Tan Yu , Xu Han , Wang Kun , Chen Zhou-Hui , Zheng Nuan , Li Yu-Qian , Lin Li-Rong
Published: October 2020

Five lanthanide complexes with a stilbene derivative and di-β-diketonates were synthesized and their enhanced E/Z-photoisomerization and luminescence properties were studied.
Application of MOF-based materials in electrochemical sensing
Authors: Ma Teng , Li Huibo , Ma Jian-Gong , Cheng Peng
Published: October 2020

A summary of the most recent advancements of metal–organic frameworks (MOFs) for electrochemical sensing is listed in this frontier article.
Alternating polarity for enhanced electrochemical synthesis
Authors: Schotten Christiane , Taylor Connor J. , Bourne Richard A. , Chamberlain Thomas W. , Nguyen Bao N. , Kapur Nik , Willans Charlotte E.
Published: October 2020

Improved synthesis through the use of alternating polarity.
Capillary interactions between soft capsules protruding through thin fluid films
Authors: Wouters Maarten , Aouane Othmane , Sega Marcello , Harting Jens
Published: October 2020

When a suspension dries, the suspending fluid evaporates, leaving behind a dry film composed of the suspended particles. We consider here the role of the particles softness on the drying process of a film with suspended fluid-filled elastic capsules.
Design of hydrogel-based scaffolds for the treatment of spinal cord injuries
Authors: Ghane Nazanin , Beigi Mohammad-Hossein , Labbaf Sheyda , Nasr-Esfahani Mohammad-Hossein , Kiani Amirkianoosh
Published: October 2020

Hydrogel-based scaffold design approaches for the treatment of spinal cord injuries.
SAR‐ATR method based on dual convolution capsule network
Authors: Touafria Mohamed , Yang Qiang
Published: October 2020

Synthetic aperture radar (SAR) image classification is one of the most important subjects in automatic target recognition. Therefore, identifying the correct class of targets has significant importance to take a decision. Recently, several deep learning techniques, especially the convolutional neural networks (CNNs), have improved the SAR images classification performance due to its powerful perspective of feature learning and reasoning. Yet, CNN's generally need a huge amount of data for training and do not accurately manage the transformations in the input data. These drawbacks are overcome using a relatively new deep learning approach called capsule networks (CapsNets). In this study, the authors propose a method that adapts and incorporates CapsNet for the SAR image classification problem and improve recognition accuracy through a dual convolution CapsNet framework. Results obtained while experimenting on the moving and stationary target acquisition and recognition data set prove the effectiveness and the robustness of the proposed framework. The proposed experimental results demonstrate the superiority of the employed method overcoming both CNNs and CapsNet separate methods in term of classification accuracy.
SARGON – Smart energy domain ontology
Authors: Haghgoo Maliheh , Sychev Ilya , Monti Antonello , Fitzek Frank H.P.
Published: October 2020

The internet of things (IoT) is a paradigm where the fragmentation of standards, platforms, services, and technologies, often scattered among different vertical domains. Consequently, the smart energy system is one of the vertical domains in which IoT technology is investigated. At the early stages of studying the IoT domains that deal with big data and interoperability, a semantic layer can be served to approach the difficulty of heterogeneity in information and data representation from IoT devices. In 2015, smart appliance reference ontology (SAREF) was introduced to interconnect data of smart devices and facilitate the communication between IoT devices that use different protocols and standards. The modular design of SAREF concedes the definition of any new vertical domain describing functions that the devices perform. In this study, SARGON – SmArt eneRGy dOmain oNtology is offered which extends SAREF to cross‐cut domain‐specific information representing the smart energy domain and includes building and electrical grid automation together. SARGON ontology is powered by smart energy standards and IoT initiatives, as well as real use cases. It involves classes, properties, and instances explicitly created to cover the building and electrical grid automation domain. This study exhibits the development of SARGON and demonstrates it through a web application.
The WISSH quasars project
Authors: Bischetti M. , Feruglio C. , Piconcelli E. , Duras F. , Pérez-Torres M. , Herrero R. , Venturi G. , Carniani S. , Bruni G. , Gavignaud I. , Testa V. , Bongiorno A. , Brusa M. , Circosta C. , Cresci G. , D’Odorico V. , Maiolino R. , Marconi A. , Mingozzi M. , Pappalardo C. , Perna M. , Traianou E. , Travascio A. , Vietri G. , Zappacosta L. , Fiore F.
Published: October 2020

Context. Sources at the brightest end of the quasi-stellar object (QSO) luminosity function, during the peak epoch in the history of star formation and black hole accretion (z ∼ 2−4, often referred to as “Cosmic noon”) are privileged sites to study the cycle of feeding & feedback processes in massive galaxies.

Aims. We aim to perform the first systematic study of cold gas properties in the most luminous QSOs, by characterising their host-galaxies and environment. These targets exhibit indeed widespread evidence of outflows at nuclear and galactic scales.

Methods. We analyse ALMA, NOEMA and JVLA observations of the far-infrared continuum, CO and [CII] emission lines in eight QSOs (bolometric luminosity LBol ≳ 3 × 1047 erg s−1) from the WISE-SDSS selected hyper-luminous (WISSH) QSOs sample at z ∼ 2.4−4.7.

Results. We report a 100% emission line detection rate and a 80% detection rate in continuum emission, and we find CO emission to be consistent with the steepest CO ladders observed so far. Sub-millimetre data reveal presence of (one or more) bright companion galaxies around ∼80% of WISSH QSOs, at projected distances of ∼6−130 kpc. We observe a variety of sizes for the molecular gas reservoirs (∼1.7−10 kpc), mostly associated with rotating disks with disturbed kinematics. WISSH QSOs typically show lower CO luminosity and higher star formation efficiency than infrared matched, z ∼ 0−3 main-sequence galaxies, implying that, given the observed SFR ∼170−1100 M⊙ yr−1, molecular gas is converted into stars in ≲50 Myr. Most targets show extreme dynamical to black-hole mass ratios Mdyn/MBH ∼ 3−10, two orders of magnitude smaller than local relations. The molecular gas fraction in the host-galaxies of WISSH is lower by a factor of ∼10−100 than in star forming galaxies with similar M*.

Conclusions. Our analysis reveals that hyper-luminous QSOs at Cosmic noon undergo an intense growth phase of both the central super-massive black hole and of the host-galaxy. These systems pinpoint the high-density sites where giant galaxies assemble, where we show that mergers play a major role in the build-up of the final host-galaxy mass. We suggest that the observed low molecular gas fraction and short depletion timescale are due to AGN feedback, whose presence is indicated by fast AGN-driven ionised outflows in all our targets.
Quelles évolutions possibles pour les systèmes de polyculture-élevage ? Résultats d’ateliers participatifs et de modélisation dans quatre régions françaises
Authors: Mosnier Claire , Dubosc Nelly , Abdouttalib Ikram , Candau Dominique , Carel Yannick , Chauvat Sophie , Fougy Florian , Guerre Émilie , Magnin Lionel , Ramonteu Sonia
Published: October 2020

Les systèmes de polyculture-élevage peuvent utiliser les complémentarités entre les cultures et l’élevage pour réduire leur consommation d’intrants. Dans quatre régions françaises, des réunions d’experts ont été organisées pour discuter de l’évolution possible de ces systèmes selon trois scénarios prospectifs contrastés : « Ultra-libéralisation et mondialisation galopante » (S1), « Économie territoriale et recentrage sur la qualité » (S2) et « Transition agroécologique et énergétique » (S3) et pour réfléchir aux modalités possibles pour soutenir la polyculture-élevage dans chacun des scénarios. Pour appuyer ces discussions, les impacts des éléments structurants des scénarios ont été simulés à l’aide du modèle bioéconomique Orfee pour une ferme type de culture-élevage de chaque région. Les experts s’accordent pour dire que le S1 irait vers une réduction de l’élevage et une plus grande spécialisation en grandes cultures, que le S2 serait le plus favorable à des systèmes diversifiés et que le S3 induirait une baisse de l’élevage, mais une intégration plus forte entre élevage et cultures. Les résultats des simulations vont dans le même sens, excepté pour la production laitière qui augmente dans le S1 en raison de la prévision d’une augmentation du prix du lait et d’une main-d’œuvre non limitante. Les simulations ont permis de calculer des indicateurs de durabilité : S1 est supérieur pour les indicateurs économiques, S2 meilleur pour les indicateurs sociaux et S3 plus favorable à l’environnement. Les simulations soulignent également la nécessité de maintenir l’élevage dans une transition agroécologique pour réduire les engrais minéraux et les pesticides.
Estimating the mechanical properties of friction welded AISI410 MSS joints using empirical relationship
Authors: Bakkiyaraj M. , Saikrishnan G. , Balasubramanian V.
Published: October 2020

The present study aims to develop the numerical relationship to attain maximum tensile strength of the friction welded AISI410 MSS joints by using response surface methodology. The friction welding (FW) process parameters considered for optimization namely rotational speed, upset pressure, friction time, and upset time. The FW parameters of 31 sets have been used to manufacture the joints to attain the maximum strength. Moreover, the detail relates the optimization procedure with respect to welding conditions on AISI410 MSS joints and its optimized parameters were reported. The optimized results have been correlated with the weld interface of the specimen with reference to tensile properties, macro and microstructure features, hardness, and fracture morphology. Finally, the results concluded that the rotational speed as the most effectual process parameter on the joint properties of the FW AISI410 MSS joint.
Effects of cadmium toxicity on sesame seed germination explained by various nonlinear growth models
Authors: Parmoon Ghasem , Moosavi Seyed Amir , Poshtdar Adel , Siadat Seyed Ataollah
Published: October 2020

This experiment was conducted to evaluate the effects of cadmium toxicity on the seed germination properties of sesame cultivars (Halil, Nazok Shakhei, Oltan, and Yellow White) using non-linear growth models. Seeds were germinated in petri dishes (90 mm diameter), with a double layered of filter paper soaked in distilled water as control treatment or cadmium (CdSO4) solutions in concentrations of 0, 100, 200, 400, 600, 800 and 1000 ppm. Each treatment was replicated four times and included 50 seeds in each petri dishes. Hyperbolastic growth models for the description of seed germination data were compared to Richards, Weibull, Logistic and Gompertz models. Our results revealed that all studied cultivars were germinated at the cadmium-polluted germination medium to a maximum of 200 ppm without reduction in germination fraction, while seed germination rate was decreased at this concentration compared to 0 and 100 ppm. Increasing cadmium concentration resulted in the germination fraction reduction from 1 to 0.4 at the 400 ppm. The recommended hyperbolastic growth model was type III or (H3) as it provided the best model with the lowest Corrected Akaike Information Criterion for cv. Halil (AICc = −14.6) and cv. Nazok Shakhei, (AICc = −14.2).
Maximum likelihood estimation in location-scale families using varied L ranked set sampling
Authors: Al-Omari Amer I.
Published: October 2020

Recently, a generalized ranked set sampling (RSS) scheme has been introduced which encompasses several existing RSS schemes, namely varied L RSS (VLRSS), and it provides more precise estimators of the population mean than the estimators with the traditional simple random sampling (SRS) and RSS schemes. In this paper, we extend the work and consider the maximum likelihood estimators (MLEs) of the location and scale parameters when sampling from a location-scale family of distributions. In order to give more insight into the performance of VLRSS with respect to SRS and RSS schemes, the asymptotic relative precisions of the MLEs using VLRSS relative to that using SRS and RSS are compared for some usual location-scale distributions. It turns out that the MLEs with VLRSS are more precise than those with the existing sampling schemes.
Graphene transfer passivates GaAs
Authors: Singidas B. G. , De los Reyes A. E. , Bardolaza H. R. , Vasquez J. D. E. , Salvador A. A. , Estacio E. S. , Sarmago R. V.
Published: October 2020

Graphene–semiconductor junction interface states influence the carrier recombination processes in emerging optoelectronic devices. The large density of interface states in the graphene–GaAs junction is partly formed by oxidation in air of the GaAs surface. A graphene transfer presented herein reduces the arsenic species in the GaAs oxide and maintains the reduction over a span of at least one year. The photoluminescence and terahertz emission spectra show reduced surface trapping of photogenerated carriers in GaAs with graphene-capped oxide. These findings demonstrate a 2D material transfer that passivates a 3D semiconductor surface. A consequence of the passivation is observed by photoreflectance modulation spectroscopy of graphene covered semi-insulating GaAs. The built-in surface field is sufficiently screened by optically pumped carriers to reveal an enhanced excitonic absorption just below the GaAs bandgap. The absorption critical point anomalously red shifts by 4–6 meV from the bulk exciton characteristic energy, an effect we attribute to the exciton absorption occurring closer to the graphene–GaAs interface and influenced by the near-surface GaAs dielectric polarization.
Dynamics of skyrmion bags driven by the spin–orbit torque
Authors: Zeng Zhaozhuo , Zhang Chunlei , Jin Chendong , Wang Jianing , Song Chengkun , Ma Yunxu , Liu Qingfang , Wang Jianbo
Published: October 2020

Skyrmion bags are found in liquid crystals and predicted to exist in ferromagnetic films. They are expected to become the information carriers of the new generation racetrack memory due to their high-degree of freedom of topological charge number. Here, we numerically and theoretically investigate the dynamics of skyrmion bags driven by the spin–orbit torque in an infinite ferromagnetic film and a nanostrip with a finite length. It is found that the constant velocity of the skyrmion bag in the ferromagnetic film depends on its topological charge number, while in the case of motion in nanostrips with the skyrmion bag moving along the boundary, the velocity in the first approximation is independent of the topological charge number. The simulation results are in good agreement with the calculation results. Finally, we show that the skyrmion bag can pass through defects and impurities in the nanostrip due to topological protection. Our results reveal the dynamics of skyrmion bags driven by the spin–orbit torque and may also be beneficial for the development of racetrack memory based on skyrmion bags.
Transport properties and electronic density-of-states of Zn-doped colusite Cu26Cr2Ge6S32
Authors: Pavan Kumar Ventrapati , Mitra Sunanda , Guélou Gabin , Supka Andrew R. , Lemoine Pierric , Raveau Bernard , Al Rahal Al Orabi Rabih , Fornari Marco , Suekuni Koichiro , Guilmeau Emmanuel
Published: October 2020

Thermoelectric colusites, one of the most recently identified and most promising family of complex Cu–S materials, have quickly attracted significant attention based on their outstanding performance. Herein, we investigate the effect of zinc for copper substitution on the thermoelectric properties of the high-performance Cr–Ge colusite, Cu26Cr2Ge6S32. We discuss the striking impact of the aliovalent Zn/Cu substitution on the charge carrier mobility and effective mass and the consequences on the electrical and thermal transport properties. The investigation is supported by first-principles calculations of the electronic density-of-states of doped colusites. The theoretical study reveals the removal of the sharp features at the top of the valence manifold with the incorporation of Zn in the conductive network, with a strong reduction in the estimated relaxation time. These theoretical and experimental observations confirm the importance of disorder within the conductive network and the high sensitivity of the Cu-S tetrahedral framework toward defects in high-performance thermoelectric colusites.
Macquarie–Yaegl Partnership: Community Capability Strengthening Through Western and Indigenous Science
Authors: Jamie Joanne F.
Published: October 2020

The rich customary knowledge possessed by Indigenous people from around the world has provided intellectually stimulating academic research opportunities and has been a successful avenue for healthcare and drug discovery as well as commercial native foods, flavours, fragrances, nutraceuticals, cosmetics, and agricultural products. When conducted with benefit sharing and reciprocity as core agenda, such research can provide community capability strengthening and immense rewards for both the Indigenous people and the academic research team involved, as well as benefiting potentially many others. This account shares my experiences as a natural products and medicinal chemistry academic, of working with Australian Aboriginal Elders, most notably from Yaegl Country of northern New South Wales, on investigating their bush medicines. Together we have facilitated the recognition and preservation of Yaegl Country customary knowledge and through initiation of a science leadership program, the National Indigenous Science Education Program, we have promoted educational attainment and STEM engagement in Australian Aboriginal youth. While this account is authored as my own personal statement of the Macquarie–Yaegl partnership, I am indebted to the Yaegl Aboriginal Elders and other Australian Aboriginal people I have worked with, and my university, school and community collaborators, my research team and student volunteers, who have all enabled the outcomes described in this account to be realised, and have made the experience so rewarding. I am also thankful to the Royal Australian Chemical Institute for the recognition of the value of this work through the award of a 2019 Royal Australian Chemical Institute Citation.
Genetic dissection of 2-heptenal content in soybean (Glycine max) seed through genome-wide association study
Authors: Wang Zhikun , Yang Mingming , Wang Yuanzhuo , Yang Chao , Zhao Xue , Tian Lizheng , Shao Yupeng , Bao Gege , Huang Jinxiu , Xia Ning , Han Yingpeng
Published: October 2020

Association analysis is an alternative to conventional, family-based methods for detecting the location of gene(s) or quantitative trait loci (QTLs), and provides relatively high resolution in terms of defining the genome position of a gene or QTL. Flavour is an essential quality characteristic of soymilk; however, soymilk contains volatile compounds unacceptable to consumers. One of main constituents in the volatiles of normal soymilk is 2-heptenal, which is thought to be a degradative oxidation product of polyunsaturated acids. In this study, a genome-wide association study using 24651 single-nucleotide polymorphisms (SNPs) was performed to identify quantitative trait nucleotides (QTNs) controlling 2-heptenal content in soybean (Glycine max (L.) Merr.) seed from a natural population of 110 soybean germplasm accessions. We detected 62 significant QTNs located on 18 different chromosomes that are significantly associated with 2-heptenal content in soybean seed. Among these, 17 QTNs co-localised with QTLs previously found to be related to protein, oil and/or fatty acid content in soybean seed. We also identified some candidate genes involved in lipid metabolism. These findings further our understanding of the genetic basis of 2-heptenal content in soybean seed and the improvement of marker-assisted breeding efficiency, which will be important for breeding soybean cultivars with low 2-heptenal content.
PPM1F controls integrin activity via a conserved phospho-switch
Authors: Grimm Tanja M. , Dierdorf Nina I. , Betz Karin , Paone Christoph , Hauck Christof R.
Published: October 2020

Control of integrin activity is vital during development and tissue homeostasis, while derailment of integrin function contributes to pathophysiological processes. Phosphorylation of a conserved threonine motif (T788/T789) in the integrin β cytoplasmic domain increases integrin activity. Here, we report that T788/T789 functions as a phospho-switch, which determines the association with either talin and kindlin-2, the major integrin activators, or filaminA, an integrin activity suppressor. A genetic screen identifies the phosphatase PPM1F as the critical enzyme, which selectively and directly dephosphorylates the T788/T789 motif. PPM1F-deficient cell lines show constitutive integrin phosphorylation, exaggerated talin binding, increased integrin activity, and enhanced cell adhesion. These gain-of-function phenotypes are reverted by reexpression of active PPM1F, but not a phosphatase-dead mutant. Disruption of the ppm1f gene in mice results in early embryonic death at day E10.5. Together, PPM1F controls the T788/T789 phospho-switch in the integrin β1 cytoplasmic tail and constitutes a novel target to modulate integrin activity.
Apparent modification of the jet-like yield in proton–proton collisions with large underlying event
Authors: Bencédi Gyula , Ortiz Antonio , Tripathy Sushanta
Published: October 2020

This paper presents the production of charged particles associated with high-p
T trigger particles (

8
<

p

T

t
r
i
g
.

<
15

GeV/c) at midrapidity in proton–proton collisions at

s

=
5.02

TeV simulated with PYTHIA 8.244. The study is performed as a function of the relative transverse activity classifier, R
T, which is the relative charged-particle multiplicity in the transverse region (π/3 < |ϕ
trig. − ϕ
assoc.| < 2π/3) of the di-hadron correlations, and it is sensitive to the multi-parton interactions. The evolution of the yield of associated particles on both the towards and the away regions (

3
⩽

p

T

a
s
s
o
c
.

<
8

GeV/c) as a function of R
T is investigated. We propose a strategy which allows for the modelling and subtraction of the underlying event contribution from the towards and the away regions in challenging environments like those characterised by large R
T. We found that the signal in the away region becomes broader with increasing R
T, while its corresponding yield is independent of R
T. Contrarily, the yield increases with R
T in the towards region. This effect is reminiscent of that seen in heavy-ion collisions, where an enhancement of the yield in the towards region for 0%–5% central Pb–Pb collisions at

s

N
N

=
2.76

TeV was reported. A discussion on the origin of these effects in PYTHIA, as well as their implications for the interpretation of recent LHC results for pp collisions, is presented.
Many-body tunneling and nonequilibrium dynamics in double quantum dots with capacitive coupling
Authors: Hou Wenjie , Wang Yuandong , Zhao Weisheng , Zhu Zhengang , Wei Jianhua , Luo Honggang , Yan Yijing
Published: October 2020

Double quantum dots (DQDs) systems may be the minimal setups for realization of QD-based qubits and quantum computation. Pauli spin blockade (PSB) and a kind of novel many-body tunneling (MBT) are identified to play important roles in these systems, and dominate the quantum tunneling at moderate and weak interdot coupling t, respectively. On the other hand, inter-dot Coulomb interaction U′ and related inter-dot Coulomb blockade (IDCB) is inevitable in DQDs. However, what would happen on the effect of U′ in DQDs has not been touched, in particular for PSB and MBT. Here, we study the tunneling processes and transport properties with various U′ in series-coupled DQDs, and find MBT process is rather robust against U′ within U′/U < 0.1, where U is the intra-dot Coulomb interaction. Meanwhile, the linearity relationship between the carrier doublon number and MBT current remains valid. These findings enrich the understanding of the many-body tunneling in the DQDs and may shed light on the manipulation of the QD-based qubits.
Minimal entropy production rate of interacting systems
Authors: Wolpert David H
Published: October 2020

Many systems are composed of multiple, interacting subsystems, where the dynamics of each subsystem only depends on the states of a subset of the other subsystems, rather than on all of them. I analyze how such constraints on the dependencies of each subsystem’s dynamics affects the thermodynamics of the overall, composite system. Specifically, I derive a strictly nonzero lower bound on the minimal achievable entropy production rate of the overall system in terms of these constraints. The bound is based on constructing counterfactual rate matrices, in which some subsystems are held fixed while the others are allowed to evolve. This bound is related to the ‘learning rate’ of stationary bipartite systems, and more generally to the ‘information flow’ in bipartite systems. It can be viewed as a strengthened form of the second law, applicable whenever there are constraints on which subsystem within an overall system can directly affect which other subsystem.
Finite-range viscoelastic subdiffusion in disordered systems with inclusion of inertial effects
Authors: Goychuk Igor , Pöschel Thorsten
Published: October 2020

This work justifies further paradigmatic importance of the model of viscoelastic subdiffusion in random environments for the observed subdiffusion in cellular biological systems. Recently, we showed (2018, PCCP, 20, 24140) that this model displays several remarkable features, which makes it an attractive paradigm to explain the physical nature of subdiffusion occurring in biological cells. In particular, it combines viscoelasticity with distinct non-ergodic features. We extend this basic model to make it suitable for physical phenomena such as subdiffusion of lipids in disordered biological membranes upon including the inertial effects. For lipids, the inertial effects occur in the range of picoseconds, and a power-law decaying viscoelastic memory extends over the range of several nanoseconds. Thus, in the absence of disorder, diffusion would become normal on a time scale beyond this memory range. However, both experimentally and in some molecular-dynamical simulations, the time range of lipid subdiffusion extends far beyond the viscoelastic memory range. We study three 1d models of correlated quenched Gaussian disorder to explain the puzzle: singular short-range (exponentially correlated), smooth short-range (Gaussian-correlated), and smooth long-range (power-law correlated) disorder. For a moderate disorder strength, transient viscoelastic subdiffusion changes into the subdiffusion caused by the randomness of the environment. It is characterized by a time-dependent power-law exponent of subdiffusion α(t), which can show nonmonotonous behavior, in agreement with some recent molecular-dynamical simulations. Moreover, the spatial distribution of test particles in this disorder-dominated regime is shown to be a non-Gaussian, exponential power distribution with index χ = 1.45–2.3, which also correlates well with molecular-dynamical findings and experiments. Furthermore, this subdiffusion is nonergodic with single-trajectory averages showing a broad scatter, in agreement with experimental observations for viscoelastic subdiffusion of various particles in living cells.
Multi-objective Planning for Distributed Generation with Consideration of the Randomness of DG output
Authors: Yu-Dong Bai , Hao Wang
Published: October 2020

DG as an alternative clean energy has a rapid development with the advantages of economy, environment protection and so on. But the randomness of power output has an influence on the security and stability of power system. At the same time, the trade-off among multiple optimization goals is often involved in the actual planning process. Therefore, using multi-objective optimization method to plan DG has wider application prospects. In this paper, a multi-objective optimization model of DG planning is established from three aspects of economy, safety and environmental protection. The flexible parameters of power system are introduced to represent the multi-objective optimization sub-functions and constraint conditions. Considering the influence of the stochastic feature of DG output, the multi-objective programming model is transformed into a flexible integrated optimization problem. One of the goals is lower cost, the others are safety and environmental protection. At the same time the objectives of economy, security and environmental protection are optimized comprehensively after DG integration, which are greatly adapted to the actual needs of the power grid planning department. In addition, the introduction of the static voltage stability margin optimization sub objective is of great significance for solving the problems caused by the uncertainty of load forecasting during the planning process.
Influence of VSC Converter Station on Switching Overvoltage of LCC Converter Station in LCC-VSC Hybrid DC System
Authors: Wang Lei , He Huiwen , Wang Dongju , Zhou Jiao
Published: October 2020

LCC-VSC hybrid DC system combines the advantages of LCC in transmitting large-capacity electric energy and VSC in power transmitting to weak AC power system without commutation failure, which makes the transmission form more flexible. A HVDC overvoltage calculation model of VSC converter station with LCC converter at the sending end and MMC converter at the receiving end is established in this paper. The principle of influence which is exerted by VSC converter station on LCC converter station is researched. The differences of Overvoltage of LCC converter station in hybrid DC System with blocking diode and not are compared and analyzed. The reason for larger overvoltage energy of LCC converter station is obtained. The necessity of blocking VSC station reverse DC voltage output is presented. The results show that the blocking diode plays an important role in limiting the overvoltage energy of LCC converter station in Hybrid DC system, which needs to be equipped in Hybrid DC system. After the blocking diode is installed, the insulation coordination of the LCC converter station at the transmission end can be the same as that of the conventional LCC HVDC transmission system.
An New Method for Detecting Harmonic Currents
Authors: Chen Xianfeng , Guo Lin , Gong Jie
Published: October 2020

The widespread use of power electronics has led to increasing problems with harmonics in power systems. The ip-iq detection method is a common method to detect harmonics. If there is a symmetry of the three phases of the power system, the ip-iq detection method can have very large errors in detecting the harmonic current. To solve this problem, an new method for detecting harmonic currents is proposed in this paper. The method solves the phase between the positive sequence voltage of each phase and the actual voltage by using the delay method to obtain the positive sequence fundamental signal of the fundamental current error problem. The simulation was then carried out using MATLAB/SIMULINK, and the simulation results confirmed the method still accurately detects harmonic currents in all cases. Therefore, this method is of great research importance for the elimination of harmonics.
Physical Layer Secure Data Transmission for Mobile Edge Computing: Beamforming and Artificial Noise
Authors: Xu Aidong , Tang Jie , Zeng Yonggang , Li Li Cheng , Jiang Yixin , Zhang Yunan , Wen Hong
Published: October 2020

As increasing threat to wireless data communication for mobile edge computing and high cost of conventional encryption implement in wireless environments, the physical layer security has aroused widespread concern to increase the security of data transmission in mobile edge computing. However, too much idealistic and unrealistic assuming in this area’s research result in security can hardly guarantee in the reality. This paper proposed a secure communications for mobile edging computing, which is using the sequcece combining with physical layer beamforming and artificial noise. By contrast to conventional schemes, the proposed scheme could enhance the security with probability 1 in two adverse situation. An advantage of the proposed scheme is that the upper layer can help the physical layer to defend each other, while up-layer system break by opponent, natural randomness of physical channel will play role to ensure system safety. We conduct simulations to verity the effectiveness of the proposed scheme for mobile edging computing.
A Low Voltage Governance and Optimization Strategy for Distribution Network Based on Multi-objective Particle Swarm Optimization Algorithm
Authors: Lu Zhu , Shasha Chen , Wenxiong Mo , Yong Wang , Guangmao Li , Guoyan Chen
Published: October 2020

With the increase of residential power consumption and the expansion of power system, distribution networks may face low voltage problem. The low voltage problem affects the normal power consumption of user and the safe and stable operation of power system. To solve it, a low voltage governance and optimization strategy for distribution network is proposed in this paper. First, a low-voltage identification method is proposed to determine low voltage governance objects and form the low-voltage database. Then, an optimization model considering multiple governance measures is derived and finally the multi-objective particles swarm optimization method is applied to solve it. The proposed method has been tested by a local distribution network system in Guangzhou. The simulation results show good governance effect of reducing low voltage area and improving the economy and stability of power grid, which can be used in the low voltage governance and power flow optimization for the distribution network.
Pin Defect Detection Method of UAV Patrol Overhead Line Based on Cascaded Convolution Network
Authors: Chaoyue Gu , Zhe Li , Jintao Shi , Gehao Sheng , Xiuchen Jiang
Published: October 2020

Pin plays the role of fixing power equipment on the overhead line. Once it is missing, it will lay a serious hidden danger for the normal operation of the overhead line. In order to improve the efficiency of UAV patrol transmission line and improve the detection rate of pin defect of transmission line, this paper proposes a pin defect detection method based on cascaded convolution network. In view of the complex background of inspection image and the small size of pin, the overall detection method is divided into two parts: positioning and diagnosis. Firstly, all fastener positions including pins are located by the improved Faster-RCNN network, and then RetinaNet network is cascaded to diagnose the defects of the fastener. The difficulty of learning is decreased and the generalization ability is improved in this way. Finally the experiment shows that this method can effectively detect the pin defects in the UAV patrol image.
Construction and Application of Distribution Network Grid Planning System
Authors: Yaolei Wang , Rong Liang , Kuihua Wu , Ren Zhao , Zhao Liu
Published: October 2020

For the problems of complex structure, unclear network and fuzzy boundary of power distribution network, grid planning is carried out in the power distribution network according to the idea of “Divide it into small grids, and manage separately”. Firstly, according to the modularization idea and the rule of land use and time development, a three-level grid planning system of power supply functional area, power supply mesh and power supply unit is constructed. Secondly, in the grid planning system, the spatial load forecasting method is given at three levels from bottom to top: at the level of power supply unit, the recommended value of saturated load density index is given, and the calculation method of the simultaneity rate in power supply unit is put forward. At the level of power supply mesh, based on the land nature of each power supply unit, the simultaneity rate of different land is given. Finally, based on the actual planning case of a certain region, the effectiveness of the grid planning method is verified.
Wind Turbine Power Curve Modelling Based on Hybrid Relevance Vector Machine
Authors: Jing Bo , Qian Zheng , Wang Anqi , Chen Tianyang , Zhang Fanghong
Published: October 2020

Wind turbine power curve (WTPC) is important for energy assessment, condition monitoring and abnormal detection. In recent years, researchers proposed a number of WTPC modelling approaches to continuously improve the model performance. In this paper, Relevance Vector Machine (RVM) is applied for WTPC modelling for the first time. Combine single-input RVM and multi-input RVM, this paper proposes a hybrid RVM method (HRVM) to further improve the fitting accuracy. Firstly, we analyse the features of model outputs of both single-input RVM and multi-input RVM. According to the analysis, the confidence interval of single-input RVM is used to limit the power output range of multi-input RVM. At last, SCADA data collected from three wind turbines are used to test the model performance. The results show that, compared with typical WTPC model approaches, HRVM achieves a good balance between fitting accuracy and computation cost.
Demand Response Business Process Design Considering Ubiquitous Functionality
Authors: Chen Zhang
Published: October 2020

The reformation of the electric power market and the application of new technology of source network load storage provide the basic conditions for the user side resources to participate in the grid interaction. More and more flexible resources, such as air conditioner, water heater and other smart home appliances, as well as new resources such as electric vehicles, distributed power supplies and energy storage, can participate in the demand response market. It is necessary to systematically analyse and design the whole process of demand response business. This paper aims at the demand response business, this paper sorts out the correlation among the participants, and designs the static relations and dynamic relations business processes of demand response based on the functional tree decomposition model. Taking the business flow as the main line, the paper designs the four-link demand response business implementation system architecture that contains energy flow, information flow and business flow.
LDA Optimized Multi-scale Texture Features Based Diagnosis Method of Defects inside Insulated Tubular Busbars
Authors: Li S Q , Zhou L B , Liu J H , Zhou Y W
Published: October 2020

In this paper, a method for diagnosing defects inside insulated tubular busbars based on LDA optimized multi-scale texture features is proposed to help to guarantee stable operation of the tubular busbars and the whole power grid. Firstly, multi-scale PRPD spectrum space was built with the UHF discharge signals of different defects by image pyramid theory. Then first-order, second-order and higher-order texture statistics were extracted from each image in the multi-scale PRPD spectrum space to form multi-scale texture features and LDA algorithm was used to optimize the features. The method was used to make texture features contain more information about partial discharge and help to improve the accuracy of diagnosis. Experiments were conducted on a 40.5kV insulated tubular busbar and CART classification trees were established as a classifier to identify the types of defects. The results of experiments show that this method can identify the defects of insulated tubular busbars accurately.
Very Short-Term Solar Irradiance Forecasting at a Sub-Minute Scale Based on WT-Cnns
Authors: zhu Zengwei , Xu Guoan , Zhang Zhen , Jiang Yongfen , Liu Ming
Published: October 2020

with the increasing of penetration of photovoltaic power, the solar irradiance forecasting has become a concern for power management. Due to the non-stationary and stochastic feature of the solar irradiance in a very time-term scale, the solar irradiance is difficult to accurately forecast. In this paper, the second-level solar irradiance is predicted using a deep-learning-based convolutional neural network (CNN) with the help of wavelet transformation (WT) and bootstrap sampling methods in time-frequency domain. The proposed method extracts the useful information from the reconstructed solar irradiance ‘image’ matrix in the frequency domain to form the next second level solar irradiance. The results show that the proposed model can learn the past features comprehensively, indicating significant potential for further study.