Bidirectional outflows as evidence of magnetic reconnection leading to a solar microflare

Magnetic reconnection is a rapid energy release process that is believed to be responsible for flares on the Sun and stars. Nevertheless, such flare-related reconnection is mostly detected to occur in the corona, while there have been few studies concerning the reconnection in the chromosphere or photosphere. Here we present both spectroscopic and imaging observations of magnetic reconnection in the chromosphere leading to a microflare. During the flare peak time, chromospheric line profiles show significant blueshifted/redshifted components on the two sides of the flaring site, corresponding to upflows and downflows with velocities of $\pm$(70--80) km s$^{-1}$, comparable with the local Alfv\'{e}n speed as expected by the reconnection in the chromosphere. The three-dimensional nonlinear force-free field configuration further discloses twisted field lines (a flux rope) at a low altitude, cospatial with the dark threads in He I 10830 \r{A} images. The instability of the flux rope may initiate the flare-related reconnection. These observations provide clear evidence of magnetic reconnection in the chromosphere and show the similar mechanisms of a microflare to those of major flares.Comment: 16 pages, 5 figures, accepted for publication in ApJ

The Quantum Geometric Phase between Orthogonal States

We show that the geometric phase between any two states, including orthogonal states, can be computed and measured using the notion of projective measurement, and we show that a topological number can be extracted in the geometric phase change in an infinitesimal loop near an orthogonal state. Also, the Pancharatnam phase change during the passage through an orthogonal state is shown to be either $\pi$ or zero (mod $2\pi$). All the off-diagonal geometric phases can be obtained from the projective geometric phase calculated with our generalized connection

Electrically Switchable and Permanently Stable Light Scattering Modes by Dynamic Fingerprint Chiral Textures.

Negative dielectric nematic liquid crystals (LCs) doped with two azobenzene materials provide electrically switchable and permanently stable scattering mode light modulators based on dynamic fingerprint chiral textures (DFCT) with inhomogeneously helical axes. These light modulators can be switched between transparent (stable large domains of DFCT) states and scattering (stable small domains of DFCT) states by applying electric fields with different frequencies. The generation of DFCT results from the long flexible side chains of the doped chiral dopant. That is, if the DFCT can be obtained, then the large domains of DFCT reflect an intrinsically stable state. Moreover, the stabilization of the small domains of DFCT are caused by the terminal rigid restricted side chains of the other doped chiral dopant. Experimentally, the required amplitude to switch the light modulator from a scattering (transparent) state to a transparent (scattering) state decreases as the frequency of the applied electric field increases (decreases) within the set limits. This study is the first report on the advantages of the light scattering mode of DFCT, including low operating voltage, permanently stable transmission, wide viewing angle, high contrast, and polarization-independent scattering and transparency.The authors would like to thank the Ministry of Science and Technology (MOST) of Taiwan for financially supporting this research under Grant No. MOST 103-2112-M-008-018-MY3. We are also grateful to Prof. Jy-Shan Hsu (Chung Yuan Christian University, Taiwan) for allowing full equipment usage

Unified nonequilibrium dynamical theory for exchange bias and training effects

We investigate the exchange bias and training effects in the FM/AF heterostructures using a unified Monte Carlo dynamical approach. This real dynamical method has been proved reliable and effective in simulating dynamical magnetization of nanoscale magnetic systems. The magnetization of the uncompensated AF layer is still open after the first field cycling is finished. Our simulated results show obvious shift of hysteresis loops (exchange bias) and cycling dependence of exchange bias (training effect) when the temperature is below 45 K. The exchange bias fields decrease with decreasing the cooling rate or increasing the temperature and the number of the field cycling. With the simulations, we show the exchange bias can be manipulated by controlling the cooling rate, the distributive width of the anisotropy energy, or the magnetic coupling constants. Essentially, these two effects can be explained on the basis of the microscopical coexistence of both reversible and irreversible moment reversals of the AF domains. Our simulated results are useful to really understand the magnetization dynamics of such magnetic heterostructures. This unified nonequilibrium dynamical method should be applicable to other exchange bias systems.Comment: Chin. Phys. B, in pres

Absorption Cross Sections of NH_3, NH_2D, NHD_2, and ND_3 in the Spectral Range 140-220 nm and Implications for Planetary Isotopic Fractionation

Cross sections for photoabsorption of NH_3, NH_2D, NHD_2, and ND_3 in the spectral region 140-220 nm were determined at ~298 K using synchrotron radiation. Absorption spectra of NH_2D and NHD_2 were deduced from spectra of mixtures of NH_3 and ND_3, of which the equilibrium concentrations for all four isotopologues obey statistical distributions. Cross sections of NH_2D, NHD_2, and ND_3 are new. Oscillator strengths, an integration of absorption cross sections over the spectral lines, for both A ← X and B ← X systems of NH_3 agree satisfactorily with previous reports; values for NH_2D, NHD_2, and ND_3 agree with quantum chemical predictions. The photolysis of NH_3 provides a major source of reactive hydrogen in the lower stratosphere and upper troposphere of giant planets such as Jupiter. Incorporating the measured photoabsorption cross sections of NH_3 and NH_2D into the Caltech/JPL photochemical diffusive model for the atmosphere of Jupiter, we find that the photolysis efficiency of NH_2D is lower than that of NH_3 by as much as 30%. The D/H ratio in NH_2D/NH_3 for tracing the microphysics in the troposphere of Jupiter is also discussed

Developing Prognostic Systems of Cancer Patients by Ensemble Clustering

Accurate prediction of survival rates of cancer patients is often key to stratify patients for prognosis and treatment. Survival prediction is often accomplished by the TNM system that involves only three factors: tumor extent, lymph node involvement, and metastasis. This prediction from the TNM has been limited, because other potential prognostic factors are not used in the system. Based on availability of large cancer datasets, it is possible to establish powerful prediction systems by using machine learning procedures and statistical methods. In this paper, we present an ensemble clustering-based approach to develop prognostic systems of cancer patients. Our method starts with grouping combinations that are formed using levels of factors recorded in the data. The dissimilarity measure between combinations is obtained through a sequence of data partitions produced by multiple use of PAM algorithm. This dissimilarity measure is then used with a hierarchical clustering method in order to find clusters of combinations. Prediction of survival is made simply by using the survival function derived from each cluster. Our approach admits multiple factors and provides a practical and useful tool in outcome prediction of cancer patients. A demonstration of use of the proposed method is given for lung cancer patients

Design of a five-axis ultra-precision micro-milling machine—UltraMill. Part 2: Integrated dynamic modelling, design optimisation and analysis

Using computer models to predict the dynamic performance of ultra-precision machine tools can help manufacturers to substantially reduce the lead time and cost of developing new machines. However, the use of electronic drives on such machines is becoming widespread, the machine dynamic performance depending not only on the mechanical structure and components but also on the control system and electronic drives. Bench-top ultra-precision machine tools are highly desirable for the micro-manufacturing of high-accuracy micro-mechanical components. However, the development is still at the nascent stage and hence lacks standardised guidelines. Part 2 of this two-part paper proposes an integrated approach, which permits analysis and optimisation of the entire machine dynamic performance at the early design stage. Based on the proposed approach, the modelling and simulation process of a novel five-axis bench-top ultra-precision micro-milling machine tool—UltraMill—is presented. The modelling and simulation cover the dynamics of the machine structure, the moving components, the control system and the machining process and are used to predict the entire machine performance of two typical configurations

Can social inclusion be evaluated? Investigating the psychometric properties of the social inclusion intervention scale

The present study aims to validate a newly developed Social Inclusion Intervention Scale (SIIS) using Exploratory Factor Analysis and Confirmatory Factor Analysis. The participants were 128 children aged 45-84 month-old from local integrated preschools in Hong Kong. The factor structure of the SIIS fit the data well (RMSEA = .08, NFI = .92, and TLI = .95, CFI = .96, SRMR = .04), with good convergent validity (all CR values > .92, all AVE values > .61). The internal consistency was good across items (all α values > .91) and factors (all CR values > .92). Hence, the sample obtained from the clinical trials of this study showed a good model fit, which suggested that the SIIS is adequate in measuring social inclusion among preschool children in social inclusion intervention programmes. The implications of the two emerged themes of social inclusion from the findings, Relationships and Acceptance, are further discussed to ascertain how they shed light on the design of social inclusion intervention

Isotopic Fractionation of Nitrogen in Ammonia in the Troposphere of Jupiter

Laboratory measurements of the photoabsorption cross section of ^(15)NH_3 at wavelengths between 140 and 220 nm are presented for the first time. Incorporating the measured photoabsorption cross sections of ^(15)NH_3 and ^(14)NH_3 into a one-dimensional photochemical diffusive model, we find that at 400 mbar, the photolytic efficiency of ^(15)NH_3 is about 38% greater than that of ^(14)NH_3. In addition, it is known that ammonia can condense in the region between 200 and 700 mbar, and the condensation tends to deplete the abundance ratio of ^(15)NH_3 and ^(14)NH_3. By matching the observed ratio of ^(15)NH_3 and ^(14)NH_3 at 400 mbar, the combined effect of photolysis and microphysics produces the ratio of (2.42 ± 0.34) × 10^(-3) in the deep atmosphere, in excellent agreement with the Galileo spacecraft measurements. The usefulness of the isotopic composition of ammonia as a tracer of chemical and dynamical processes in the troposphere of Jupiter is discussed

Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate 15 (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demon- 20 strate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architec- 25 ture is modulated by local blood oxygen level-dependent activity and a-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. 30 Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be 35 potentially useful as a predictor for learning and neural rehabilitation