Miracle-dz/UniFAR-FTUs huggingface dataset:
GENETIC VARIATION OF PALIURUS RAMOSISSIMUS (LOUR.) POIRET (RHAMNACEAE): IMPLICATION FOR CONSERVATION STRATEGIES

Paliurus ramosissimus (Rhamnaceae) is a deciduous shrub found on the seashore of Jeju Island, located at the southern tip of the Korean peninsula, and has been designated a rare plant species by the Korea Forest Service. Random amplified polymorphic DNA was used to investigate the genetic variation within and among P. ramosissimus populations on Jeju Island. Populations of P. ramosissimus showed a relatively low genetic diversity. The percentage of polymorphic bands (PPB) ranged from 33.3% to 63.6% (average = 51.4%), Shannon’s information index (I) ranged from 0.213 to 0.352 (average = 0.316), and Nei’s gene diversity (H) ranged from 0.149 to 0.239 (average = 0.218). Analysis of molecular variance and Nei’s gene differentiation coefficient showed low genetic differentiation among populations (Φst = 0.207 and Gst = 0.186). The level of gene flow was sufficient to counter population divergence due to genetic drift (Nm = 0.109). Population genetic information obtained from this study could provide a valuable baseline for conservation and management plans for this species; P. ramosissimus on Jeju Island have to be protected through in situ conservation. The populations Pyo-sun and Il-gwua with high genetic diversity have conservation priority. In particular, Pyo-sun population protection must be the highest priority, as this population was revealed to have a high genetic diversity despite its small size.
Local mean decomposition for source separation in underdetermined mixing model

Most of the existing underdetermined blind source separation (BSS) approaches assume that the source signals are strictly or partially sparse. This paper, however, presents a BSS method in underdetermined mixing situation for non-sparse signals based on the local mean decomposition (LMD) algorithm. The BSS method firstly introduces LMD into the BSS problem to rebuild a few extra mixing signals. Such signals are then combined with the initial mixtures such that the underdetermined BSS problem is transformed into a determined one and the difficulty of the deficiency of the mixtures is overcome. For the rebuilt mixtures and the newly formed determined BSS problem, two BSS algorithms are proposed to recover the sources. One algorithm uses singular value decomposition on the second-order statistics matrix of the new mixtures to realize the separation, and the other employs an independent component analysis (ICA) type BSS algorithm with stable Frobenius norm constraint on the separating matrix. The simulation results have demonstrated that the proposed underdetermined BSS algorithms can process non-sparse signals, and can acquire a nearly 3dB lower mean square error than the previous non-sparse BSS algorithm.
Design and implementation of a dynamic healthcare system for weight management and health promotion

Overweight and obesity have become the major public health challenges globally, since late 20th century and they are apparently on the rise in some upper middle- and high-income countries. Over past few years, the obesity-related research has been turning to use mobile wireless device to deliver the weight management intervention, with the tremendously increased use of mobile phones. Mobile health (mHealth)-related smartphone application (app) is brought to facilitate user to self-monitor the diet, physical activity and body weight for improving accuracy and reducing burdens. A dynamic healthcare solution called “Intelligent Weight Management System” (iWMS) has been developed to support user to collect and transmit the objective data in real time. User can follow the advice from healthcare professionals to not only make the changes in lifestyle and behavior, but also communicate easily with healthcare professionals. This paper discusses the development of iWMS and contours the future plan for clinical trial.
Performance optimization of ultrasonic needle actuating device for insertion operation into tissue mimics

Needle insertion with ultrasound actuation has been proven as an effective way to reduce the penetration force and needle deflection within soft tissue, facilitating operational manoeuverability and targeting accuracy in cancer biopsy and regional anaesthesia. In such percutaneous procedures, loading conditions from adjacent soft tissue alter the needle transducer's impedance and cause deterioration of its vibration performance. In this paper, we report the needle transducer's structure and working principle; we investigate the loading effects from tissue mimics on the transducer's resonant impedance and vibration performance; and we show how to enhance the needle transducer's vibration performance through optimization of geometrical dimensions and the electrical driving method. Structural optimization was carried out with different transversal dimensions of the needle. Electrical driving optimization was done by tracking the working resonant frequency and stabilizing the driving signal during operation. Experimental trials confirmed a force reduction of 70.2% with the structural optimization and 73.7% with the electrical driving optimization for the insertion operation into gelatin phantom, compared with a static needle.
Spiral metamaterial for tunable circular dichroism

We propose an electrostatically actuated spiral structures as a metamaterial for circularly polarized light in Terahertz (THz) frequency. Actuation of the spiral structures changes their geometry, and produces a tunable chirality resulting in circular dichroism, which is the differential absorption between the left and right circularly polarized light. Results of the THz-spectroscopy confirmed that the ON/OFF of circular dichroism was achieved with the proposing structures.
Robust and adaptive stabilization of a class of uncertain non-minimum-phase nonlinear systems in generalized output feedback canonical form

In this paper, a global robust stabilizer for a class of uncertain non-minimum-phase nonlinear systems in generalized output feedback canonical form is designed. The system contains unknown parameters multiplied by output-dependent nonlinearities and output-dependent nonlinearities enter such a system both additively and multiplicatively. The proposed method relies on a novel parameter estimator and state observer design methodology together with combined standard backstepping and small-gain approach. Our design has three distinct features. Firstly, the parameter estimator and state observer does not follow the classical certainty-equivalence principle. Second, the design treats in a unified way unknown parameters and unmeasured states. Third, the technique by combining backstepping technique and small-gain theorem ensures robustness with respect to dynamic uncertainties. Finally, a numerical example is given to show that the proposed method can be applied to more general systems which do not satisfy the cascading upper diagonal dominance (CUDD) conditions developed in recent papers
Reframing anxiety to encourage interracial interactions.

Racial bias against Black Americans is a continuing problem in the United States. Although interracial dialogue is generally accepted as an effective method to combat racial bias, such dialogues are often avoided because they can make White Americans anxious and are accompanied by negative outcomes. Therefore, in the present study, we tested a simple intervention designed to (a) increase Whites’ willingness to choose to enter a situation in which they would interact with a Black partner and (b) reduce the negative outcomes typically found in these interactions. One hundred fifty-seven White participants were randomly assigned to have a conversation with an interaction partner on one of two topics, racial discrimination or sexual identity discrimination. They were further assigned to one of two groups: an intervention group or a control group. The intervention group was told that people tend to avoid interracial interactions because these interactions are anxiety provoking but that choosing instead to approach interracial interactions may help to reduce future feelings of anxiety. The control group received no such guidance. Results demonstrated that, compared to the control group, participants in the intervention group were more likely to choose to interact with a Black partner over a White partner and showed more positive nonverbal engagement during the interracial interaction. We discuss the implications of this work for the development of strategies to increase Whites’ engagement in interracial dialogue.
Size Class Dependent Relationships between Temperature and Phytoplankton Photosynthesis-Irradiance Parameters in the Atlantic Ocean

Over the past decade, a number of methods have been developed to estimate size-class primary production from either in situ phytoplankton pigment data or remotely-sensed data. In this context, the first objective of this study was to compare two methods of estimating size class specific (micro-, nano- and pico-phytoplankton) photosynthesis-irradiance (PE) parameters from pigment data. The second objective was to analyse the relationship between environmental variables (temperature, nitrate and PAR) and PE parameters in the different size-classes. A large dataset was used of simultaneous measurements of the PE parameters (n=1260) and phytoplankton pigment markers (n=2326), from 3 different institutes was used. There were no significant differences in mean PE parameters of the different size classes between the chemotaxonomic method of Uitz et al. (2008) and the pigment markers and carbon-to-Chl a ratios method of Sathyendranath et al. (2009). For both methods, mean maximum photosynthetic rates ( ) for micro-phytoplankton were significantly lower than those for pico-phytoplankton and nano-phytoplankton. The mean light limited slope (αB) for nano-phytoplankton were significantly higher than for the other size taxa. For micro-phytoplankton dominated samples identified using the Sathyendranath et al. (2009) method, both and αB exhibited a significant, positive linear relationship with temperature, whereas for pico-phytoplankton the correlation with temperature was negative. Nano-phytoplankton dominated samples showed a positive correlation between and temperature, whereas for αB and the light saturation parameter (Ek) the correlations were not significant. For the Uitz et al. (2008) method, only micro-phytoplankton , pico-phytoplankton αB, nano- and pico-phytoplankton Ek. The temperature ranges occupied by the size classes derived using these methods differed. The Uitz et al. (2008) method exhibited a wider temperature range compared to those derived from the Sathyendranath et al. (2009) method. The differences arise from the classification of mixed populations. Based on these patterns, we therefore recommend using the Sathyendranath et al. (2009) method to derive micro-phytoplankton PE parameters at sea water temperatures up to 8 oC during monospecific blooms and the Uitz et al. (2008) method to derive PE parameters of mixed populations over the temperature range from 8 to 18 oC.
Fault diagnosis method based on diffusion maps and hidden Markov model for TE process

To reduce data-storage costs and enhance high accuracy of industrial process fault detection, a data driven fault diagnosis method is proposed based on diffusion maps and hidden Markov model. Firstly, the correlation dimension of sample data is calculated. Secondly, the high-dimensional eigenvectors are extracted into low-dimensional manifold space by diffusion maps. Finally, the low-dimensional eigenvectors are regarded as the observation sequence of hidden Markov model for fault pattern recognition. The effectiveness of this new method is proved by TE process simulator. In addition, the fault identification results are demonstrated to be superior to the traditional identification approach of hidden Markov model.
Spatial-Temporal Source Reconstruction of MEG via Variational EM Algorithm

Magnetoencephalography(MEG) is a new noninvasive brain imaging technique reconstructed electronic activities of brain by measured the magnetic field surrounding scalp. The aim of this paper is to explore a new method of MEG source spatio-temporal reconstruction based on optimizing the reconstructed MEG source model. We make the assumption that the stimulated electronic activities of the brain are located on a particular part of cortex where we partition it with multiple even voxels. In terms of Biot-Savart Law of electromagnetism, the spatial source model is built with multiple unknown parameters which reflect the information of the source location. Then, we try to solve this parameters optimization as a Maximum-likelihood estimation (MLE) using variational EM algorithm. According to the application of this approach, this paper also addresses that the solution of MEG signal reconstruction should be considered to avoid overlapping the calculation complexity, which may result in too expensive calculation to practice. Whereas, this approach also provides a new possibility and the new angle to solve MEG source reconstruction.
Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber (Apostichopus japonicus) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China.
In Silico Methods for Prioritizing DNA Markers for Genetic Association Studies.

The low density lipoprotein (LDL) receptor gene codes for a 160 kDa transmembrane glycoprotein ubiquitously distributed that is playing a major role in cholesterol homeostasis. This gene is therefore an interesting candidate gene in genetic association studies. In these studies one seek for an association between the different kind of genetic variation in the gene and complex diseases, which in this case is related to cholesterol homeostasis. At present, it is preferable to reduce the number of genetic markers for a candidate gene to a manageable set for an association study due to cost-benefit reasons. Here, a way of prioritizing markers for this purpose in the LDLR gene is presented and it is basically is built on the hierarchy: 1. Selection of markers in gene exons with potential functional effects, i.e. amino acid changing (non-synonymous) variants. 2. Markers that occur in highly conserved regions of the genome (most often in exons, introns, untranslated regions, promoters and enhancers). 3. Markers that affect the binding of regulatory factors. 4. Markers that may not affect amino acids (synonymous) but can affect messenger RNA splicing. 5.Elimination of redundant markers due to tight linkage disequilibrium. A number of in silico methods for this endeavour are used. Each of these methods yields a result that is summarized and presented as a prioritized list of single nucleotide polymorphisms of the gene LDLR.
FaultCoverageModeling inNonlinearDynamical Systems ⋆

In this paper, we propose an approach for modeling fault coverage in nonlinear dynamical systems. Fault coverage gives a measure of the likelihood that a system will be able to recover after a fault occurrence. In our setup, the system dynamics are described by a standard state-space model. The system input (disturbance) is considered to be unknown but bounded at all times. Before any fault occurrence, the vector field governing the system dynamics is such that, for any possible input signal, the corresponding system reach set is contained in some region of the state space defined by the system performance requirements. When a fault occurs, the vector field that governs the system dynamics might be altered. Fault coverage is defined as the probability that, given a fault occurred, the system trajectories remain, at all times, within the region of the state-space defined by the performance requirements. Input-to-State Stability (ISS) concepts are used to compute estimates of the proposed coverage model. Several examples are discussed in order to illustrate the proposed modeling approach.
Volume Data Visualization Using Fractal Interpolation Surfaces

Visualization of medical or experimental data is often achieved by extracting an intermediate geometric representation of the data. One such popular method for extracting an isosurface from volume data is the Marching Cubes (MC) algorithm, which creates a polygon mesh by sampling the data at the vertices of the cubes of a 3D grid. We present a method for isosurface approxima- tion that uses the vertex extraction phase of the MC algorithm, but subsequently represents the data by fractal interpolation surfaces instead of a polygon mesh. The proposed method is appropriate for isosurfaces that are not locally flat, such as natural structures. Another advantage is that a coarser grid resolution can typically be used, since fractal interpolation surfaces are particularly good at representing detailed, irregular or self-similar structures. Thus for many cases the resulting isosurface is more accurate and more compact. The multiresolution extension of the method is also straightforward. Experimental data verify the practical usefulness of the proposed method. Index Terms—Volume data visualization, marching cubes, fractal interpolation surfaces, isosurfaces.
A fuzzy Bi-objective multi-commodity minimum cost flow-based perishable food transportation problem and its harmony search algorithm

In this paper, a multi-commodity minimum cost flow-based optimization model for perishable food transportation is proposed and the corresponding mathematical model is constructed. This model takes many characteristics of perishable food transportation into consideration, including the perishability, the imbalance between supply and demand, and the fuzziness of information. It establishes two objective functions according to the trade-off relation of cold chain logistics. Fuzzy parameters are defuzzified by using the graded mean integration. To solve the problem, a harmony search-based algorithm is proposed and presented in this paper. Computational results indicate our algorithm is effective.
Management of Specific Complications after Congenital Heart Surgery (I)

In addition to the general consequences of surgery and cardiopulmonary by-pass, lesion-specific complications can occur after surgery for congenital heart disease. It is important for the pediatric intensive care specialist to fully understand the preoperative anatomy and the intraoperative details of these patients. This allows a timely and appropriate treatment of general and lesion-specific complications. In this article we provide a list of commonly-performed surgical procedures and possible associated problems to be anticipated in the early postoperative period. Then it follows a discussion about the diagnosis and management of these complications, based on their pathophysiological features.
Does ‘ Performance Pay ’ Pay ? Wage Flexibility over the Great Recession

Using the National Compensation Survey between 2004-2014, we document substantial cyclical heterogeneity among performance pay and fixed wage jobs. We find that employment growth in performance pay (fixed wage) jobs is countercyclical (procyclical), whereas compensation per employee growth is procyclical (countercyclical). Our estimates are identified off the response of similar work levels within the same establishment to local area shocks. We provide two mechanisms: (i) procyclical (countercyclical) effort in performance pay (fixed wage) jobs, and (ii) high pay-sensitivity in performance pay jobs. Our results reconcile competing claims about the wage stickiness of new hires versus incumbents in search models.
Financial and strategic impact of VCs on start-up development: Silicon valley decacorns vs. Northern-European experience

Venture capitalists financing is the main source to keep start-ups up and running. Financial support is not the only thing VCs provide, sometimes they are asked to provide advice and sometimes they force their advice. When and what type of help VCs can give were researched in qualitative multiple-case study setting in top Silicon Valley unicorns - Uber, Airbnb, Snapchat, Pinterest and Dropbox. Their results were contrasted with Northern-European success experience to find similar patterns and context-specific differences. Biggest difference between US and European start-ups are governmental funding at early stage in Europe (it used to be so also in Silicon Valley during its emergence in last century), the role and use of accelerators and level of VC involvement.