Search

Delta ($\delta$) sunspots sometimes host fast photospheric flows along the central magnetic polarity inversion line (PIL). Here we study the strong Doppler shift signature in the central penumbral light bridge of solar active region NOAA 12673. Observations from the Helioseismic and Magnetic Imager (HMI) indicate highly sheared, strong magnetic fields. Large Doppler shifts up to 3.2 km s$^{-1}$ appeared during the formation of the light bridge and persisted for about 16 hours. A new velocity estimator, called DAVE4VMwDV, reveals fast converging and shearing motion along the PIL from HMI vector magnetograms, and recovers the observed Doppler signal much better than an old version of the algorithm. The inferred velocity vectors are largely (anti-)parallel to the inclined magnetic fields, suggesting that the observed Doppler shift contains significant contribution from the projected, field-aligned flows. High-resolution observations from the Hinode/Spectro-Polarimeter (SP) further exhibit a clear correlation between the Doppler velocity and the cosine of the magnetic inclination, which is in agreement with HMI results and consistent with a field-aligned flow of about 9.6 km s$^{-1}$. The complex Stokes profiles suggest significant gradients of physical variables along the line of sight. We discuss the implications on the $\delta$-spot magnetic structure and the flow-driving mechanism., Comment: 17 pages, 14 figures. Accepted for publication in ApJ

We study the extent to which Milne-Eddington inversions are able to retrieve and characterize the magnetic landscape of the solar poles from observations by the spectropolarimeter onboard Hinode. In particular, we evaluate whether a variable magnetic filling factor is an adequate modeling technique for retrieving the intrinsic magnetic properties from every pixel in the polar field of view. We first generate synthetic spectra emerging from a numerical simulation of a "plage" region at an inclined line of sight of 65$^{\circ}$, and degrade the data to emulate real observations. Then, we invert the synthetic spectra with two Milne-Eddington inversion codes that feature different treatments of the magnetic filling factor, and relate the retrieved magnetic quantities back to their original values in the simulation cube. We find that while the apparent retrieved magnetic properties map well the spatially-degraded simulation, the intrinsic magnetic quantities bear little relation to the magnetic field at the native resolution of the simulation. We discuss the systematic biases caused by line-of-sight foreshortening, spatial degradation, photon noise and modeling assumptions embedded in the inversion algorithm., Comment: Accepted for publication in ApJ

Despite investments in multiple space and ground-based solar observatories by the global community, the Sun's polar regions remain unchartered territory - the last great frontier for solar observations. Breaching this frontier is fundamental to understanding the solar cycle - the ultimate driver of short-to-long term solar activity that encompasses space weather and space climate. Magnetohydrodynamic dynamo models and empirically observed relationships have established that the polar field is the primary determinant of the future solar cycle amplitude. Models of solar surface evolution of tilted active regions indicate that the mid to high latitude surges of magnetic flux govern dynamics leading to the reversal and build-up of polar fields. Our theoretical understanding and numerical models of this high latitude magnetic field dynamics and plasma flows - that are a critical component of the sunspot cycle - lack precise observational constraints. This limitation compromises our ability to observe the enigmatic kilo Gauss polar flux patches and constrain the polar field distribution at high latitudes. The lack of these observations handicap our understanding of how high latitude magnetic fields power polar jets, plumes, and the fast solar wind that extend to the boundaries of the heliosphere and modulate solar open flux and cosmic ray flux within the solar system. Accurate observation of the Sun's polar regions, therefore, is the single most outstanding challenge that confronts Heliophysics. This paper argues the scientific case for novel out of ecliptic observations of the Sun's polar regions, in conjunction with existing, or future multi-vantage point heliospheric observatories. Such a mission concept can revolutionize the field of Heliophysics like no other mission concept has - with relevance that transcends spatial regimes from the solar interior to the heliosphere., Comment: This White Paper was submitted in 2022 to the United States National Academies Solar and Space Physics (Heliophysics) Decadal Survey

Using time-distance local helioseismology flow maps within 1 Mm of the solar photosphere, we detect inflows toward activity belts that contribute to solar cycle scale variations in near-surface meridional flow. These inflows stretch out as far as 30 degrees away from active region centroids. If active region neighborhoods are excluded, the solar cycle scale variation in background meridional flow diminishes to below 2~m~s$^{-1}$, but still shows systematic variations in the absence of active regions between Sunspot Cycles 24 and 25. We, therefore, propose that the near-surface meridional flow is a three component flow made up of: a constant baseline flow profile that can be derived from quiet Sun regions, variations due to inflows around active regions, and solar cycle scale variation of the order of 2~m~s$^{-1}$. Torsional oscillation, on the other hand, is found to be a global phenomenon i.e. exclusion of active region neighborhoods does not affect its magnitude or phase significantly. This non-variation of torsional oscillation with distance away from active regions and the three-component breakdown of the near-surface meridional flow serve as vital constraints for solar dynamo models and surface flux transport simulations., Comment: 14 pages, 9 figures, accepted for publication in the Astrophysical Journal

We study joint learning of network topology and a mixed opinion dynamics, in which agents may have different update rules. Such a model captures the diversity of real individual interactions. We propose a learning algorithm based on multi-armed bandit algorithms to address the problem. The goal of the algorithm is to find each agent's update rule from several candidate rules and to learn the underlying network. At each iteration, the algorithm assumes that each agent has one of the updated rules and then modifies network estimates to reduce validation error. Numerical experiments show that the proposed algorithm improves initial estimates of the network and update rules, decreases prediction error, and performs better than other methods such as sparse linear regression and Gaussian process regression.

Zirconia-coated Cr2O3 aggregates synthesized by mixing ZrO2 powders and Cr2O3 aggregates with a phenolic resin binder followed by thermosetting treatment were employed as modified Cr2O3 aggregates to obtain Cr2O3–Al2O3–ZrO2 bricks (high-chromia bricks). The elastic modulus (E) and cold modulus of rupture (CMOR) of these high-chromia bricks before and after thermal shock cycles were systematically investigated, and the residual ratios of CMOR and E were calculated. The thermal shock resistance of the high-chromia bricks was significantly improved by the factor of modification of Cr2O3 aggregates. The mechanism of the improved thermal shock resistance of these high-chromia bricks was studied via microstructure analysis, and the crack propagation behavior was analyzed by scanning electron microscopy (SEM). The fracture work (γWOF), thermal shock damage factor (R′′′′), and thermal stress crack stability parameter (Rst) were measured and calculated using the wedge splitting test (WST). The results indicate that the porous ZrO2 coating layer wrapped the Cr2O3 aggregates, forming modified Cr2O3 aggregates that can increase crack deflection, free path of crack propagation, and fracture work, thus improving the thermal shock resistance of high-chromia bricks. The thermal shock resistance of the fabricated high-chromia bricks was highly correlated with the thickness of the ZrO2 coating layer surrounding the Cr2O3 aggregates. The variation trend of Rst is well consistent with the experimental results, which is suitable to evaluate the thermal shock resistance of high-chromia bricks.

The common method to mitigate the slot-frequency vibration and noise of the permanent magnet (PM) motors is skewing the stator slot or skewing the PM. However, the limitation of this method in pole-frequency vibration reduction exists. In this article, one method by adopting the piecewise stagger PM pole with a continuous skew edge is proposed to reduce the pole-frequency and slot-frequency vibration for surface-mounted permanent magnet (SPM) synchronous machines. First, the analysis of the radial force acting on the stator teeth is conducted. Basing on this, the limitation of the skewing slot method in reducing the vibration is revealed. In order to solve this problem, a piecewise stagger PM pole with a continuous skew edge for pole-frequency and slot- frequency vibration reduction is proposed and its principle of vibration reduction is presented in detail. The finite element models of three 6-pole/36-slot SPM motors are built to simulate the radial force. Finally, two 6-pole/36-slot SPM motors are manufactured and the experimental results validate the effectiveness of the proposed method.

Designing Luenberger observers for nonlinear systems involves the challenging task of transforming the state to an alternate coordinate system, possibly of higher dimensions, where the system is asymptotically stable and linear up to output injection. The observer then estimates the system's state in the original coordinates by inverting the transformation map. However, finding a suitable injective transformation whose inverse can be derived remains a primary challenge for general nonlinear systems. We propose a novel approach that uses supervised physics-informed neural networks to approximate both the transformation and its inverse. Our method exhibits superior generalization capabilities to contemporary methods and demonstrates robustness to both neural network's approximation errors and system uncertainties., Proceedings of the 2023 American Control Conference (ACC)

Additional file1: Fig. S1. Relative expression levels of BrrWUS, BrrWUSa, and BrrWUSb in turnip. Fig. S2. Phenotype of 35S:BrrWUSa transgenic plants. Fig. S3. Schematic structure of three vectors. Fig. S4. Raw result for Fig. 1h. Fig. S5. Raw result of Fig. 4b. Table S1. Primers used in this study.

In this study, a type of micro-nano Si3N4 matrix ceramic cutting tool material was successfully prepared by controlling the addition amount of TiC0.7N0.3 and the hot-pressing sintering temperature. The effects of different volume fractions of TiC0.7N0.3 on the microstructure, mechanical properties, particle size distribution, and relative density of Si3N4 ceramic tools at the same temperature were investigated. The results show that the addition of TiC0.7N0.3 makes the β-Si3N4 grains with different diameters and aspect ratios interlaced and tightly bonded, thus achieving the strengthening and toughening effects. In addition, the effects of different hot-press sintering temperatures on the properties of Si3N4 ceramic tool materials were studied. It was concluded that the density of the material increased with the increase of the hot-pressing temperature. The relative density, flexural strength, and fracture toughness of the samples with 1 vol% TiC0.7N0.3 added at 1750 °C and 30 MPa pressure reached 99.22%, 993 MPa, and 9.81 MPa·m1/2, respectively.

Zirconia surface treatment using different techniques could be employed to overcome the delamination issue reported between veneering ceramic materials and the corresponding zirconia core. The study subjected yttria partially stabilized zirconia core material to grinding and sandblasting as a first surface treatment. An airbrush gun spraying technique was used to coat a uniform thin layer of veneering ceramic (IPS e.max Ceram and Noritake Cerabien ZR) on the core materials followed by performing laser scanning as a second surface treatment. The specimens were investigated for surface topography, interface analysis, elemental compositions, phase transformation, and fracture mode patterns using profilometer, SEM, EDS, XRD, and stereomicroscope. The impact of laser scanning on the shear bond strength before and after storage of specimens in artificial saliva was also studied. Transformable tetragonal phase to monoclinic was predominant in sandblasted specimens followed by laser scanning with observation of micro-cracks at interface. The grinding followed by laser scanning yielded satisfactory outcomes in terms of the presence of coating property. The phase transformation was not detected through the latter technique. The technique greatly enhanced the roughness property and bond strength values which might be considered as a reliable application to improve the longevity of fixed dental crowns in load bearing areas.

Nowadays, water pollution is a part of the major environmental problems. Industrial sewage that does not meet the emission standards will pollute the surface water and groundwater when it is discharged into water bodies, causing serious adverse impacts on human beings and environment. In view of industrial sewage privately discharged without properly monitoring, we present a method for detection of sewage discharge using clustering and differential analysis on image sequences derived from satellite photos taken when focusing on a certain place. The proposed method helps to indicate key images containing sewage, make the sewage area and leave evidence for the retrospective incident. Clustering based on the search of fast density peaks is used for detecting images containing sewage. In addition, two sample’s t-test and Fisher linear discriminant analysis are combined to extract the key pixels representing the area of sewage discharge. Experiments were made on 200 images corresponding to a certain area at different times of the day and 25 key frames with areas labelled to be sewage discharge were extracted, which indicated the effectiveness of this method.

Background Echinococcosis, which is caused by the larvae of cestodes of the genus Echinococcus, is a parasitic zoonosis that poses a serious threat to the health of humans and animals globally. Albendazole is the drug of choice for the treatment of echinococcosis, but it is difficult to meet clinical goals with this chemotherapy due to its low cure rate and associated side effects after its long-term use. Hence, novel anti-parasitic targets and effective treatment alternatives are urgently needed. A previous study showed that verapamil (Vepm) can suppress the growth of Echinococcus granulosus larvae; however, the mechanism of this effect remains unclear. The aim of the present study was to gain insight into the anti-echinococcal effect of Vepm on Echinococcus with a particular focus on the regulatory effect of Vepm on calcium/calmodulin-dependent protein kinase II (Ca2+/CaM-CaMKII) in infected mice. Methods The anti-echinococcal effects of Vepm on Echinococcus granulosus protoscoleces (PSC) in vitro and Echinococcus multilocularis metacestodes in infected mice were assessed. The morphological alterations in Echinococcus spp. induced by Vepm were observed by scanning electron microscopy (SEM), and the changes in calcium content in both the parasite and mouse serum and liver were measured by SEM-energy dispersive spectrometry, inductively coupled plasma mass spectrometry and alizarin red staining. Additionally, the changes in the protein and mRNA levels of CaM and CaMKII in infected mice, and in the mRNA levels of CaMKII in E. granulosus PSC, were evaluated after treatment with Vepm by immunohistochemistry and/or real-time quantitative polymerase chain reaction. Results In vitro, E. granulosus PSC could be killed by Vepm at a concentration of 0.5 μg/ml or higher within 8 days. Under these conditions, the ultrastructure of PSC was damaged, and this damage was accompanied by obvious calcium loss and downregulation of CaMKII mRNA expression. In vivo, the weight and the calcium content of E. multilocularis metacestodes from mice were reduced after treatment with 40 mg/kg Vepm, and an elevation of the calcium content in the sera and livers of infected mice was observed. In addition, downregulation of CaM and CaMKII protein and mRNA expression in the livers of mice infected with E. multilocularis metacestodes was found after treatment with Vepm. Conclusions Vepm exerted a parasiticidal effect against Echinococcus both in vitro and in vivo through downregulating the expression of Ca2+/CaM-CaMKII, which was over-activated by parasitic infection. The results suggest that Ca2+/CaM-CaMKII may be a novel drug target, and that Vepm is a potential anti-echinococcal drug for the future control of echinococcosis. Graphical Abstract

This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.

We address the task of domain generalization, where the goal is to train a predictive model such that it is able to generalize to a new, previously unseen domain. We choose a hierarchical generative approach within the framework of variational autoencoders and propose a domain-unsupervised algorithm that is able to generalize to new domains without domain supervision. We show that our method is able to learn representations that disentangle domain-specific information from class-label specific information even in complex settings where domain structure is not observed during training. Our interpretable method outperforms previously proposed generative algorithms for domain generalization as well as other non-generative state-of-the-art approaches in several hierarchical domain settings including sequential overlapped near continuous domain shift. It also achieves competitive performance on the standard domain generalization benchmark dataset PACS compared to state-of-the-art approaches which rely on observing domain-specific information during training, as well as another domain unsupervised method. Additionally, we proposed model selection purely based on Evidence Lower Bound (ELBO) and also proposed weak domain supervision where implicit domain information can be added into the algorithm., Comment: Presented at ICLR 2021 RobustML Workshop

Dark matter can decay via gravity portals and contribute a minor decay (MD) width. This paper proposes that nonminimal coupling between the Higgs field and gravity can lead to additional gravity-Higgs portals for gravitational dark matter to decay and contribute additional decay (AD) width. For scalar dark matter candidates with a mass less than 5 TeV, the decay branching ratios are almost independent of whether dark matter decay through gravity portals or gravity-Higgs portals; therefore, it is almost impossible to distinguish gravity portals from gravity-Higgs portals by observing decay products. For fermionic singlet dark matter candidates, their MD products are diverse, while their AD products are monotonous, only neutrinos and Higgs bosons. As for both dark matter candidates, gravity-Higgs portals prefer to dominate the decay at around several hundred GeV. Besides, for both TeV dark matter candidates, there is still a vast parameter space alive.

A machine learning system, including when used in reinforcement learning, is usually fed with only limited data, while aimed at training a model with good predictive performance that can generalize to an underlying data distribution. Within certain hypothesis classes, model selection chooses a model based on selection criteria calculated from available data, which usually serve as estimators of generalization performance of the model. One major challenge for model selection that has drawn increasing attention is the discrepancy between the data distribution where training data is sampled from and the data distribution at deployment. The model can over-fit in the training distribution, and fail to extrapolate in unseen deployment distributions, which can greatly harm the reliability of a machine learning system. Such a distribution shift challenge can become even more pronounced in high-dimensional data types like gene expression data, functional data and image data, especially in a decentralized learning scenario. Another challenge for model selection is efficient search in the hypothesis space. Since training a machine learning model usually takes a fair amount of resources, searching for an appropriate model with favorable configurations is by inheritance an expensive process, thus calling for efficient optimization algorithms. To tackle the challenge of distribution shift, novel resampling methods for the evaluation of robustness of neural network was proposed, as well as a domain generalization method using multi-objective bayesian optimization in decentralized learning scenario and variational inference in a domain unsupervised manner. To tackle the expensive model search problem, combining bayesian optimization and reinforcement learning in an interleaved manner was proposed for efficient search in a hierarchical conditional configuration space. Additionally, the effectiveness of using multi-objective bayesian optimization for model search in a decentralized learning scenarios was proposed and verified. A model selection perspective to reinforcement learning was proposed with associated contributions in tackling the problem of exploration in high dimensional state action spaces and sparse reward. Connections between statistical inference and control was summarized. Additionally, contributions in open source software development in related machine learning sub-topics like feature selection and functional data analysis with advanced tuning method and abundant benchmarking were also made.

Although many achievements have been made since Google threw out the paradigm of federated learning (FL), there still exists much room for researchers to optimize its efficiency. In this paper, we propose a high efficient FL method equipped with the double head design aiming for personalization optimization over non-IID dataset, and the gradual model sharing design for communication saving. Experimental results show that, our method has more stable accuracy performance and better communication efficient across various data distributions than other state of art methods (SOTAs), makes it more industry-friendly.

Homogeneous and dispersed Y3Al5O12 (yttrium aluminum garnet, YAG) nanopowders were synthesized via a homogeneous co-precipitation method from the mixed solutions of yttrium nitrate, aluminum nitrate and a small amount of ammonium sulfate using hot urea as the precipitant. The method has the superiorities that co-precipitation of cations is ensured and continuous decomposition of the hot urea is achieved to obtain the narrow size distribution particles. The addition of small amount of ammonium sulfate surfactant, although has no influence on YAG garnet phase formation, has significant effect on dispersion, particles distribution and sinterability of the resultant YAG and Yb:YAG powders. Compared with the undoped sample, the green body of Yb:YAG doped with ammonium sulfate has higher total shrinkage, linear shrinkage rate and relative density through sintering at 1600 °C. The resultant Yb:YAG powders can be sintered into transparent ceramics at 1700 °C through vacuum sintering. The influence of the sulfate ions on characteristics of the resultant powders was thoroughly studied.

BACKGROUND Quercetin is a natural bioactive flavonoid that is present in a wide variety of vegetables and fruits and exhibits a promising anti-metastasis property in various human cancer cells. However, the effect of quercetin on human HCCLM3 cells is unclear. MATERIAL AND METHODS In the current study, a wound-healing assay was performed using quercetin-treated HCCLM3 cells to further explore whether quercetin affects the motility of human HCCLM3 cells. Transwell assay was used to explore the potential effect of quercetin in HCCLM3 cells on cell migration and cell invasion. Western blotting analysis was used to explore the expression of p-Akt1, MMP-2, and MMP-9 in quercetin-treated HCCLM3 cells. RESULTS The wound-healing time was delayed in quercetin-treated HCCLM3 cells, and the ability to migrate and invade was inhibited in quercetin-treated human HCCLM3 cells. Moreover, the protein levels of p-Akt1, MMP-2, and MMP-9 were down-regulated in quercetin-treated HCCLM3 cells, as detected by Western blotting. CONCLUSIONS Our data show that quercetin attenuated cell migration and invasion by suppressing the protein levels of p-Akt1, MMP-2, and MMP-9 in HCCLM3 cells.

The purpose of our research is to explore the effect of ultrasound applied on the quantitative analysis of wool in blended fabric by chemical reaction with a mechanical device. One cylindrical container for blended fabric for the whole process is designed, and detailed observations are made of the step of wool-dissolving assisted by ultrasound. A ventilation hole is essential for the fabric container or else the solvent will be unable to transcend in and out of the fabric container smoothly. For fabrics that cannot be untwisted into yarns, shredding into strips is a reasonable alternative. The fabric piece in the fabric container weighed 0.5 g and the appropriate time for dissolving was 40 minutes. As compared to the traditional manual method, dissolving in the fabric container assisted by ultrasound is equivalent, but is more convenient for mechanization and automation of the whole quantitative analysis.

In Multi-Goal Reinforcement Learning, an agent learns to achieve multiple goals with a goal-conditioned policy. During learning, the agent first collects the trajectories into a replay buffer, and later these trajectories are selected randomly for replay. However, the achieved goals in the replay buffer are often biased towards the behavior policies. From a Bayesian perspective, when there is no prior knowledge about the target goal distribution, the agent should learn uniformly from diverse achieved goals. Therefore, we first propose a novel multi-goal RL objective based on weighted entropy. This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals. Secondly, we developed a maximum entropy-based prioritization framework to optimize the proposed objective. For evaluation of this framework, we combine it with Deep Deterministic Policy Gradient, both with or without Hindsight Experience Replay. On a set of multi-goal robotic tasks of OpenAI Gym, we compare our method with other baselines and show promising improvements in both performance and sample-efficiency., Published in International Conference on Machine Learning (ICML 2019), Long Beach, USA. arXiv admin note: text overlap with arXiv:1902.08039

Machine learning pipeline potentially consists of several stages of operations like data preprocessing, feature engineering and machine learning model training. Each operation has a set of hyper-parameters, which can become irrelevant for the pipeline when the operation is not selected. This gives rise to a hierarchical conditional hyper-parameter space. To optimize this mixed continuous and discrete conditional hierarchical hyper-parameter space, we propose an efficient pipeline search and configuration algorithm which combines the power of Reinforcement Learning and Bayesian Optimization. Empirical results show that our method performs favorably compared to state of the art methods like Auto-sklearn , TPOT, Tree Parzen Window, and Random Search.

Laser scanning is one of the methods that can be used for surface treatments of zirconia. Application of the laser to the surface of zirconia has diverse effects, depending on the type of laser. A carbon dioxide (CO2) laser has high irradiation power and can alter the surface properties. This study investigated the surface coating of zirconia as a core material that subsequently coated with a veneering ceramic (v-c) material. This study compared laser scanning and conventional sintering processes. Various properties including surface topography, interface evaluation, phase transformation, elemental compositions, failure mode patterns, and contact angle were examined through X-ray diffractometry (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analyses. Results were confirmed that the bond strength between the v-c and the substrate recorded through laser scanning was higher than that determined through conventional sintering.

Time series classification problems have drawn increasing attention in the machine learning and statistical community. Closely related is the field of functional data analysis (FDA): it refers to the range of problems that deal with the analysis of data that is continuously indexed over some domain. While often employing different methods, both fields strive to answer similar questions, a common example being classification or regression problems with functional covariates. We study methods from functional data analysis, such as functional generalized additive models, as well as functionality to concatenate (functional-) feature extraction or basis representations with traditional machine learning algorithms like support vector machines or classification trees. In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners. Additionally, we provide a software framework in R for functional data analysis for supervised learning, including machine learning and more linear approaches from statistics. This allows convenient access, and in connection with the machine-learning toolbox mlr, those methods can now also be tuned and benchmarked.

Phase-pure eulytite-type Sr3Y0.88(PO4)3:0.10Yb3+,0.02Ln3+ upconversion (UC) phosphors (Ln = Ho, Er, Tm) were synthesized via gel-combustion and subsequent calcination at 1250°C. Their UC luminescence, temperature-dependent fluorescence intensity ratio of thermally and/or non-thermally coupled energy levels, and performance of optical temperature sensing were systematically investigated. The phosphors typically exhibit green, orange-red and blue luminescence under 978 nm NIR laser excitation for Ln = Er, Ho and Tm, respectively, which were discussed from two- and three-photon processes. The 524 nm green (Er3+), 657 nm red (Ho3+) and 476 nm blue (Tm3+) main emissions were analyzed to have average decay times of ~52 ± 2, 260.6 ± 0.7 and 117 ± 1 μs, respectively. It was shown that (1) the Er3+ doped phosphor has a better overall performance of temperature sensing with thermally coupled 2H11/2 and 4S3/2 energy levels, whose maximum absolute (S A) and relative (SR ) sensitivities are ~5.07 × 10−3 K−1 at 523 K and ~1.16% at 298 K, respectively; (2) the Ho3+ doped phosphor shows maximum S A and SR values of ~0.019 K−1 (298–573 K) and 0.42% at 573 K for the non-thermally coupled energy pairs of 5F5/(5F4,5S2) and 5I4/5F5, respectively; (3) the Tm3+ doped phosphor has a maximum S A of ~12.74 × 10−3 K−1 at 573 K for the non-thermally coupled 3F2,3/1G4 energy levels and a maximum SR of ~1.74% K−1 at 298 K for the thermally coupled 3F2,3/3H4 levels. Advantages of the current phosphors in optical temperature sensing were also revealed by comparing with other typical UC phosphors. Phase-pure eulytite-type Sr3Y0.88(PO4)3:0.10Yb3+,0.02Ln3+ upconversion (UC) phosphors (Ln=Ho, Er, Tm) were synthesized via gel-combustion, whose property and mechanism of UC luminescence and the performance of optical temperature sensing with thermally and/or non-thermally coupled energy levels were thoroughly investigated.

A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can not leave the site it is situated on and it is forbidden to carry out training algorithms on remote data sites due to either technical or privacy and trust concerns. To carry out a clinical research under this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper. Compared to federated learning, which is optimizing the model parameters directly by carrying out training across all data sites, RFMS trains model parameters only on one local data site but optimizes hyper-parameters across other data sites jointly since hyper-parameters play an important role in machine learning performance. The aim is to get a Pareto optimal model with respective to both local and remote unseen prediction losses, which could generalize well across data sites. In this work, we specifically consider high dimensional data with shifted distributions over data sites. As an initial investigation, Bayesian Optimization especially multi-objective Bayesian Optimization is used to guide an adaptive hyper-parameter optimization process to select models under the RFMS scenario. Empirical results show that solely using the local data site to tune hyper-parameters generalizes poorly across data sites, compared to methods that utilize the local and remote performances. Furthermore, in terms of dominated hypervolumes, multi-objective Bayesian Optimization algorithms show increased performance across multiple data sites among other candidates.

Consumption demands are final drivers for the extraction and allocation of natural resources. This paper investigates demand-driven natural resource requirements and spatial outsourcing within China in 2012 by using the latest multi-regional input-output model. Exergy is adopted as a common metric for natural resources input. The total domestic resource exergy requirements amounted to 125.5 EJ, of which the eastern area contributed the largest share of 44.5%, followed by the western area (23.9%), the central area (23.0%) and the northeastern area (8.6%). Investment was the leading final demand category, accounting for 52.9% (66.4 EJ) of national total embodied resource use (ERU). The total trade volumes of embodied resource were equivalent to 69.6% of the total direct resource input (DRI), mostly transferred from the central and western regions such as Inner Mongolia, Shanxi, Shaanxi and Xinjiang to the eastern regions such as Jiangsu, Zhejiang, Guangdong and Shanghai. The northeastern and eastern areas had physical net imports of 1213.5 PJ and 38452.6 PJ, while the central and western inland areas had physical net exports of 6364.5 PJ and 33301.5 PJ, respectively. Shanghai, Beijing, Zhejiang, Jiangsu and Guangdong had prominent ERUs which respectively were 101.6, 12.6, 11.7, 8.4 and 4.3 times of their DRIs. The ERUs of Inner Mongolia, Shaanxi, Shanxi, Ningxia and Guizhou were equal to only 17.6%, 25.3%, 27.9%, 46.0% and 50.2% of their DRIs, respectively. Regional uneven development resulted in imbalanced resource requirements across China. The findings can provide a deep understanding of China's resource-driven economic development mode, and contribute to reducing regional resource footprints and their environment outcomes under the "new normal economy".

Protein S-nitrosylation, which refers to the redox-based posttranslational modification of a cysteine thiol by the attachment of a nitric oxide (NO) group, modulates a variety of enzyme activities. Monodehydroascorbate reductase (MDHAR) is essential for ascorbic acid (AsA) regeneration, which protects plant cells against damage by detoxifying reactive oxygen species (ROS). However, the relationship between S-nitrosylation and the role of tomato MDHAR (SlMDHAR) under salt stress remains unclear. In this paper, we show that the SlMDHAR mRNA expression, enzyme activity, protein level, total S-nitrosylated proteins and S-nitrosylated SlMDHAR protein level in tomato leaves significantly increase after NaCl treatment. To further evaluate the function of SlMDHAR under salt stress, overexpressed transgenic tobacco plants were used. The germination rate and root length of the overexpressed plants under NaCl stress were significantly higher than those of wild-type (WT) plants. Meanwhile, the transgenic plants had lower ROS accumulation, higher antioxidant enzyme activities and AsA-DHA ratio, more proline and soluble sugar contents than those in WT plants under salt stress. With a higher expression of stress-related genes, the transgenic plants demonstrated lower Na+ and higher K+ accumulation compared with WT plants. The NO accumulation and S-nitrosylated MDHAR level were higher in transgenic plants than in WT plants after NaCl treatment. In contrast, virus-induced gene silencing (VIGS) of SlMDHAR tomato plants showed enhanced sensitivity to salt stress and have lower S-nitrosylated MDHAR protein. These results suggested that SlMDHAR confers salt stress tolerance by alleviating oxidative damage probably involving the S-nitrosylation of MDHAR.

Statement of problem Delamination failure may occur between ceramic frameworks and veneering ceramics, shortening the lifetime of fixed dental prostheses in load-bearing areas. Purpose The purpose of this in vitro study was to compare the effect of different repeating CO2 laser treatment methods and conventional approaches on the shear bond strength of zirconia frameworks and veneering ceramics. Material and methods Zirconia disks (N=110) were prepared and divided into 5 groups: milling without surface treatment (group M), airborne-particle abrasion (group APA), single laser treatment (group LX1), 2 laser treatments (group LX2), and 3 laser treatments (group LX3). The specimens in the first 2 groups were treated before the framework was coated using the spraying technique. Specimens in the remaining groups were coated with veneering ceramic using the spraying process, and then subjected to laser treatment. Surface roughness and topography, interface properties, phase transformation, shear bond strength, and fracture modes were investigated. Outcomes were analyzed using a profilometer, a scanning electron microscope (SEM) equipped with energy dispersive spectroscopy (EDS), X-ray diffractometry (XRD), a stereomicroscope, and a universal testing machine for mechanical testing. Results The XRD showed that phase transformation from tetragonal to monoclinic occurred after airborne-particle abrasion. This phenomenon was not observed in laser-treated specimens. Groups LX2 and LX3 had the highest surface roughness values, 1.18 ±0.23 μm and 1.21 ±0.22 μm, among all groups, and group LX3 had the highest shear bond strength values for unaged and aged conditions, 32.08 ±2.45 MPa and 31.43 ±2.07 MPa. The mixed-fracture mode was the most common type of fracture observed. Conclusions The results indicated that the shear bond strength between the zirconia framework and veneering ceramic was higher after laser surface treatments than after milling alone or after airborne-particle abrasion. Laser treatment methods, particularly LX2 and LX3, could be considered reliable approaches for zirconia surface treatment.

We investigate two sympathetic filament eruptions observed by the New Vacuum Solar Telescope (NVST) on 2015 October 15. The full picture of the eruptions is obtained from the corresponding SDO/AIA observations. The two filaments start from active region NOAA 12434 in the north and end in one large quiescent filament channel in the south. The left filament erupts firstly, followed by the right filament eruption about 10 minutes later. Clear twist structure and rotating motion are observed in both filaments during the eruption. Both eruptions are failed, since the filaments firstly rise up, then flow towards the south and merge into the southern large quiescent filament. We also observe repeating activations of mini filaments below the right filament after its eruption. Using magnetic field models constructed based on SDO/HMI magnetograms by flux rope insertion method, we find that the left filament eruption is likely to be triggered by kink instability, while weakening of overlying magnetic fields due to magnetic reconnection at an X-point between the two filament systems might play an important role in the onset of the right filament eruption.

This document provides some technical notes on the polar field correction scheme for the HMI synoptic maps and daily updated synchronic frames. It is intended as a reference for the new data products and for some minor updates on our previous scheme for MDI (Sun et al. 2011)., Tech notes for HMI synoptic data product

A variable pole pitch linear induction motor (VPPLIM) is proposed as a new member of the family of electromagnetic aircraft launch system (EMALS). With proper designing of the variation of the pole pitch, the VPPLIM could accelerate the aircraft almost uniformly at the condition of the usual ac supply, avoiding the use of large-scale power electronic converters. Special considerations in the design of VPPLIM, such as the structure topology, the variation of the pole pitch, and the winding arrangement are discussed in detail in this paper. The acceleration performance of the VPPLIM fed by a constant-voltage constant-frequency supply is analyzed by means of the quasi-steady-state 2-D magnetic field model and the difference model of mover's dynamics. A two-stage method is proposed to simulate the acceleration performance of the VPPLIM based on a 2-D thrust table of the motor achieved by the finite-element method. The calculated features of the prototype VPPLIM consist well with those expected for EMALS.

When beginners practice Chinese calligraphy, they often copy from ancient calligraphic works and try to imitate the style as closely as possible. However there are inevitably some characters whose styles are not correctly followed. Thus we are motivated to detect the style consistency of all written characters in one practice. With the styles extracted by using stacked autoencoders of deep neural network model, we discriminate correctly styled and alien styled characters using a trained one-class support vector machine. Thus we can pick out those outliers. The proposed algorithm reaches satisfactory results. The algorithm can also be applied to other image style detection problems.

This paper reports the synthesis and luminescence properties of rare earth doped CaYAI₃O₇ prepared via a sol-gel process. The phosphors have intense luminescence. In Ce³⁺/Tb³⁺ co-doped materials, there is energy transfer from Ce³⁺ to Tb³⁺ that enhances the luminescence of Tb³⁺. These materials may find applications in solid state lighting and radiation detection.

Positron age-momentum correlation (AMOC) spectroscopy is applied to study the associations between the momentum distributions and positron annihilation in the toughened CE networks. The Doppler profile of the positron annihilation process is much broader for the toughened CE than pure CE network. This is due to positron trapping on high polar groups of the added elastomers or nano-CaCO3 and annihilation with the electrons of high momenta. The broadening momentum distribution of ortho-positronium annihilation process indicates that the pick-off annihilation process is strongly influenced by the added high polar elastomers or nano-CaCO3. The potential of AMOC spectroscopy for free volume analysis in polymer blends was demonstrated. (C) 2012 Elsevier Ltd. All rights reserved.

The toughness of cyanate ester resin (CE) matrix is improved significantly with addition of hydroxyl-terminated liquid butadiene-acrylonitrile rubber (HTBN). The impact strength increased from 4.4 KJ/m2 (pure CE) to 13.3 KJ/m2 (CE/HTBN, HTBN 10 wt %). The curing behavior of the system is studied by differential scanning calorimetric and Fourier transform infrared spectroscope. The results showed that hydroxyl groups on the HTBN chains have slight activation effect to CE curing reaction at the beginning of the cure process. The toughening mechanism is mainly caused by the flexibilizing effects of the homogeneously dispersed HTBN molecules in the CE matrix. The toughening mechanism was demonstrated from the aspect of free volume using positron annihilation lifetime spectroscopy. With addition of HTBN, the mean free-volume size of the composite is smaller than pure CE. The decrease in the mean free-volume size of the system is mainly related to the partition effects of the finely dispersed HTBN molecules to the free-volume holes of CE matrix. A dramatic increase in the interfacial area occurs in this highly miscible system. Good interfacial adhesion is also reflected from the higher I2 of the composite. Therefore, more positrons annihilation in their free state occurs in the composites containing HTBN than pure CE. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010

The miscibility of chitosan (CS)/polar polymer blend membranes has been studied by positron annihilation and other methods. The miscibility of these two blend systems (CS/polyvinyl pyrrolidone (PVP) and CS/polyethylene glycol (PEG)) is good in the solution state due to the hydrogen interaction between the functional groups of the studied polymers. However, the miscibility of these two blend systems in the solid state is better in the CS/PVP system than in the CS/PEG system. The differences in miscibility of such two blend systems in the solid state were powerfully demonstrated with positron annihilation lifetime spectroscopy (PALS) methods. The CS/PEG blend system had much larger free-volume size and lower free-volume concentration. For their poorer interaction and phase separation fact, the molecules in the interfacial zone of the CS/PEG blend are less compact than the CS matrix. Therefore, the free-volume size in the interfacial zone was much larger than it in the CS matrix.

An epoxy resin (EP) matrix has been modified with carboxyl-randomized butadiene-acrylonitrile (CRBN) rubber and hydroxyl-terminated butadiene-acrylonitrile rubber (HTBN). When the rubber content was low (⩽5%), the free volume size varied slowly, meanwhile, the free volume size of EP/HTBN system was smaller than EP/CRBN system. With further addition of rubber component (⩾7%), the free volume size of the composite increased quickly. In this case, the free volume size of EP/HTBN system was larger than EP/CRBN system. The intermediate positron lifetime component (I2) was affected by the structure of the interface layer between rubber particles and EP matrix. The different free volume properties could be attributed to the compatibility between rubber and EP.

The feasibility of non‐destructive measurement of internal quality indices of intact pear fruit using visible and near‐infrared (Vis/NIR) spectrometric techniques was studied. In the wavelength region from 350 to 1800 nm, calibration models relating Vis/NIR spectra to soluble solids content (SSC) and firmness were developed based on multilinear regression (MLR), principal component regression (PCR) and partial least squares regression (PLSR) with respect to the logarithms of the reflectance reciprocal log (1/R), its first derivative D1 log (1/R) and its second derivative D2 log (1/R). The best combination, based on the reliable and robust models obtained, was the PLS models with respect to log (1/R) at an equatorial position of the fruit. Prediction with the PLS model with respect to log (1/R) resulted in correlation coefficients (rp ) of 0.9121 and 0.8541, and root mean standard error of prediction (RMSEP) of 0.6619 and 1.2324 for SSC and firmness, respectively. The experimental results indicate...

This paper proposes a virtual synchronous control strategy for Doubly-Fed Induction Generator (DFIG)-based wind turbines. On the basis of the proposed excitation control model orientated by rotor flux, and the P-f, Q-V droop control models which emulate the governor and exciter of synchronous machine, this paper established the basic virtual synchronous control structure for DFIG. This control strategy can provide DFIGbased wind turbines with intrinsic regulation ability of frequency and voltage without using the phase-locked loop (PLL) of the grid. Considering the power limitation under certain wind speed, the proposed adaptive droop control method realized the maximum power point tracking (MPPT) of wind turbines in steady state. Simulation results demonstrate the feasibility and effectiveness of the presented virtual synchronous control for grid-connected DFIG. This control strategy can not only provide the possibility of unifying the grid-connected wind turbines as the standard synchronous power, but also decrease the negative effect on the grid frequency and voltage with high penetration of wind energy.

A poly-sulfone hollow-fiber membrane bioreactor system was designed and used in the bioconversion process of acrylonitrile to acrylamide, in which a new strategy of membrane separation coupled with bioconversion was presented. The characteristics of filtration of the membrane was evaluated and the bioconversion of arcylamide from acrylnitrile in the membrane bioreactor was carried out using the free resting cells as a biocatalyst to replace the immobilized cells in the original reacting system. The results showed that the bioconversion process continued over 5 h without renewing the cells in the system containing 10% (v/v) fermentation broth at 20 °C. The conversion rate of acrylnitrile to acrylamide and the productivity of acrylamide were 99.9% and 20.67 g/gcell h, respectively, which were much higher than those of the original bioconversion system in which the immobilized cells were used as a biocatalyst. Therefore, this membrane bioreactor has a favorable foundation for industrial application due to its high efficiency and ability to continuously produce acylamide using free resting cells.