Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Mycorrhizal Inoculation on Root Morphology and Nursery Production of Three Grapevine Rootstocks

Grapevines form mutualistic symbioses with arbuscular mycorrhizal (AM) fungi that have been shown to enhance plant growth and nutrition. In the field, AM fungal populations may be low or nonexistent (in fumigated soils), suggesting the need for AM inoculation of grapevine plants at the nursery. Addition of AM fungal inoculum to rooting substrate could be an effective strategy for the nursery production of mycorrhizal plants. The effects of inoculation of three grapevine rootstocks on root morphology and growth were tested. Results indicated that inoculation with the AM fungus Glomus aggregatum in rooting beds of grapevine cuttings changed root morphology, increasing branching of first-order lateral roots. When rooted cuttings were transplanted to pots, with soil sufficient in P and including indigenous AM fungi, and grown for nine months, a significant growth enhancement was found in two of the inoculated rootstocks. Glomus aggregatum, alone or in synergy with the indigenous AM fungi, seemed to have a higher affinity for 161-49 Couderc, the roots of which were more extensively colonized and exhibited a greater positive growth response.
Direct Measurement of a Secular Pulsation Period Change in the Pulsating Hot Pre-White Dwarf PG 1159–035

GW Vir (PG 1159-035) is the prototype of the class of multiperiodic, nonradially pulsating hot white dwarfs, and shows a strong pulsation mode at 516 s. All measurements to date of the secular variation of the 516 s pulsation quote as best value Ṗ = (-2.49 ± 0.06) × 10-11 s s-1. The original measurement gave two best solutions, and a χ2 analysis indicated that the quoted value was preferred at the level of 0.97 probability. On other hand, the best-developed models for planetary nebula nuclei (PNNs), using models from the asymptotic giant branch as starting points and simulating the observed mass loss, provide positive values for any model with log(L/L☉) ≲ 3, as PG 1159-035. This conflict between the measurement and the theoretical models has been a challenge to stellar evolution theory. Exploiting a much larger data set and computational techniques previously unavailable, we show that the earlier analysis of the data grossly underestimated the true uncertainties due to interferences between frequencies. Using new data along with the old, and more accurate statistical methods, we calculated the secular period change of the 516 s pulsation, and obtained a positive value: Ṗ = (+13.07 ± 0.03) × 10-11 s s-1. We show that three additional methods yield the same solution. This new value was the second best of the original possible solutions; it was eliminated on the basis of statistical arguments that we show to be invalid. It is an order of magnitude larger than the theoretical predictions. Additionally, from rotational splitting analysis, we were able to estimate, for the first time, a limit to the secular variation of the rotational period Ṗrot = (-1.0 ± 3.5) × 10-11 s s-1, leading to a contraction timescale upper limit of |τ-1R| = |Ṙ/R| < 48 × 10-11 s-1 with 99.5% probability.
From "Traditional" to Research-Based Instructional Strategies: An Assessment of Learning Gains

Cognitive science literature reveals researchbased instructional strategies (RBIS) that are proven to help students learn, retain, and transfer skills and knowledge (Brown, Roediger III, and McDaniel 2014). Still, many economics professors are not using these strategies. Faculty cite additional preparation time and the inability to cover all of the necessary topics if class time is devoted to active learning (Goffe and Kauper 2014). Flipping the classroom helps to solve the latter problem. Students in a flipped course familiarize themselves with material before class. Class time is used for activities that help students achieve higher levels of learning. We conduct an experiment to illustrate what average instructors can expect in terms of student learning gains should they change their teaching method. Using a pre/post analysis, we compare students’ learning gains in a flipped class that uses refined RBIS to the gains in a “traditional” class and estimate the differential gains in assessment scores for students in the RBIS course. Our results indicate significant, positive differential learning gains for students in the RBIS class, which are more prevalent in content presented earlier in the semester. To our knowledge, this study is the first to compare outcomes from a flipped-RBIS economics class to a traditional economics class. Moreover, in addition to traditional OLS estimation, we estimate the treatment effect using inverse probability weighting regression adjustment, providing comparable treatment and control groups, and accounting for potential selection into treatment. This method allows us to estimate the effect of participating in an RBIS course on learning gains.
A Real-Time Convolutional Approach To Speech Emotion Recognition

In the task of Speech Emotion Recognition, the speakers’ emotion is classified based on audio recordings of their voice. This research explores the use of machine learning models to recognize emotions in speech in real-time. For this purpose, this research presents the DeepEmoNet architecture. DeepEmoNet uses a CNN based architecture to classify emotion on a raw audio input. The experiments show that this architecture achieves a score that can compete with the current state-of-the-art approaches. Furthermore, the experiments show that the model is able to achieve those score in real-time.
The Basis and Potentials of Ogunlade Sex Determination Electrocardiographic Score (OSDES) in Young Adults

The potentials of electrocardiogram (ECG) in sex determination in young adults have not been extensively explored despite the fact that ECG is one of the most frequently performed medical procedures. This study aimed at developing a simple electrocardiographic score system that could assist in sex determination in young adults. Patterns of ECG in male and female young adults were carefully analyzed and features that could be used to delineate the ECG into male and female patterns were selected as criteria for formulation of a simple scoring system called Ogunlade sex determination electrocardiographic score (OSDES). Components of OSDES were point scores in four electrocardiographic parameters; T wave configuration in V1 (1-3), ST segment in V2 or V3 (1-3), QRS rotation (1-3) and heart rate (1-3). The total minimum and maximum scores were 4 and 12 respectively. An individual with a total score of < 7 was identified as a female while an individual with a score ≥ 7 was identified a male. Thus, with OSDES, ECG can be classified into two; male pattern and female pattern for male and female respectively. This unique scoring system was tested among 474 healthy young adults (294 males and 180 females). Two hundred and eighty eight (288) of the 294 males were correctly identified by OSDES as males while 174 of the 180 females were correctly identified as females (k = 0.946, p < 0.001). The study defined male and female ECG patterns and concluded that ECG can be used to determine sex among young adult population.
The Case for a Virtualization-Based Trusted Execution Environment in Mobile Devices

ARM processors used in modern mobile devices, such as smartphones and tablets, use TrustZone to implement a trusted execution environment (TEE). In this paper, we argue that virtualization hardware, already available on many ARM processors, should be used for this purpose instead. Virtualization hardware can be used to implement multiple isolated trusted environments, as opposed to a single such environment provided by TrustZone. This can prevent the bloat of the Trusted Computing Base (TCB) of the TEE and support new security services not currently possible, such as sandboxing of untrusted operating system kernel components. We also address the concerns for the use of virtualization for the aforementioned purpose. Most notably, through extensive experiments, we show that, unlike widespread belief, virtualization overhead is small if the hypervisor is carefully designed to minimize its interpositions into the operating system activity. In addition, we discuss and address the concerns on supported features, backward-compatibility, and hypervisor's TCB size. Going forward, given that virtualization provides a viable (if not superior) TEE solution, we suggest that ARM TrustZone hardware components should be mostly removed from future ARM SoCs. This can simplify the processor and SoC design and save some die space.
Investigation of a moiré based crack detection technique for propulsion health monitoring

The development of techniques for the health monitoring of the rotating components in gas turbine engines is of major interest to NASA's Aviation Safety Program. As part of this on-going effort several experiments utilizing a novel optical Moiré based concept along with external blade tip clearance and shaft displacement instrumentation were conducted on a simulated turbine engine disk as a means of demonstrating a potential optical crack detection technique. A Moiré pattern results from the overlap of two repetitive patterns with slightly different periods. With this technique, it is possible to detect very small differences in spacing and hence radial growth in a rotating disk due to a flaw such as a crack. The experiment involved etching a circular reference pattern on a subscale engine disk that had a 50.8 mm (2 in.) long notch machined into it to simulate a crack. The disk was operated at speeds up to 12 000 RPM and the Moiré pattern due to the shift with respect to the reference pattern was monitored as a means of detecting the radial growth of the disk due to the defect. In addition, blade displacement data were acquired using external blade tip clearance and shaft displacement sensors as a means of confirming the data obtained from the optical technique. The results of the crack detection experiments and its associated analysis are presented in this paper.
Software reliability and system reliability

This chapter is mainly aimed at showing that, by using deliberately simple mathematics, the classical reliability theory can be extended in order to be interpreted from both hardware and software viewpoints. This is referred to as X-ware [Lapr89, Lapr92b] throughout this chapter. It will be shown that, even though the action mechanisms of the various classes of faults may be different from a physical viewpoint according to their causes, a single formulation can be used from the reliability modeling and statistical estimation viewpoints. A single formulation has several advantages, both theoretical and practical, such as (1) easier and more consistent modeling of hardware-software systems and of hardware-software interactions, (2) adaptability of models for hardware dependability to software systems and vice versa, and (3) mathematical tractability. Section 2.2 gives a general overview of the dependability concepts. Section 2.3 is devoted to the failure behavior of an X-ware system, disregarding the effect of restoration actions (the quantities of interest are thus the time to the next failure or the associated failure rate), considering in turn atomic systems and systems made up of components. In Sec. 2.4, we deal with the behavior of an X-ware system with service restoration, focusing on the characterization of the sequence of the times to failure (i.e., the failure process); the measures of interest are thus the failure intensity, reliability, and availability. Section 2.5 outlines the state of art in dependability evaluation and specification. Finally, Sec. 2.6 summarizes the results obtained.
Numerical techniques for shaped beam scattering by large and absorbing particle using variable separation methods

Shaped beam scattering and numerical computations are two very active domains of research in light interaction with particles. The shaped beams may invoke special effects when interacting with particles which are essence of many applications. The variable separation methods, typically the Generalized Lorenz-Mie theory, solve the problem rigorously, but the numerical evaluation of relevant physical quantities is always a challenge for practical applications. To deal with these difficulties it is often necessary to combine physical understandings, mathematical skills and algorithmic faculties. Sophisticated techniques will be presented to solve numerical problems arising in the calculation of the scattering and absorption properties, the internal and near fields, the radiation pressure and torque exerted on very large and strong absorbing particles illuminated by circular and elliptical Gaussian beams, Bessel and Doughnut beams. Software integrating these experiences on the prediction of all the physical quantities of any shaped beam by a homogeneous or stratified sphere will be presented.
Light intensity influences variations in the structural and physiological traits in the leaves of Iris pumila L.

Ambient light significantly influences the structural and physiological characteristics of Iris pumila leaves. A random sample of Iris clones native to an exposed site at the Deliblato Sands, Serbia was partially covered with a neutral screen that transmitted 35% of daylight, so that each clone experienced reduced and full sunlight at the same time. The sun-exposed leaves were significantly thicker, had greater stomatal density, exhibited higher lipid peroxidation, increased activities of SOD, APX, CAT enzymes and higher contents of non-enzymatic antioxidants (anthocyanins and phenols) and water deficit relative to shade-leaves. The activities of GR, GPX, and GST enzymes was unaffected by the irradiance level.
Hopf bifurcation analysis and control of three-dimensional Prescott neuron model

Neurons generate different firing patterns because of different bifurcations in the dynamical viewpoint. Various nerve diseases are relevant to the bifurcation of nervous system. Therefore, it is vital to control bifurcation since it may be potential ways of treating nerve diseases. This paper focuses on the critical Hopf bifurcation analysis and the problem of Hopf bifurcation control. We investigate the effects of key parameters on critical Hopf bifurcation and obtain the Hopf bifurcation occurrence region on parameter plane. With the theory of high-dimensional Hopf bifurcation, we analytically deduce the judgement criteria of Hopf bifurcation type for the three-dimensional models and judge the Hopf bifurcation type of Prescott model by using it. With application of the Washout filter, the subcritical Hopf bifurcation of Prescott model is controlled and converted to supercritical Hopf bifurcation. In addition, we make some discussions on Hopf bifurcation analysis of a coupled neural network. The results provided in this paper could bring new ways to controlling neurological diseases.
HydroRing: Supporting Mixed Reality Haptics Using Liquid Flow

Current haptic devices are often bulky and rigid, making them unsuitable for ubiquitous interaction and scenarios where the user must also interact with the real world. To address this gap, we propose HydroRing, an unobtrusive, finger-worn device that can provide the tactile sensations of pressure, vibration, and temperature on the fingertip, enabling mixed-reality haptic interactions. Different from previous explorations, HydroRing in active mode delivers sensations using liquid travelling through a thin, flexible latex tube worn across the fingerpad, and has minimal impact on a user's dexterity and their perception of stimuli in passive mode. Two studies evaluated participants' ability to perceive and recognize sensations generated by the device, as well as their ability to perceive physical stimuli while wearing the device. We conclude by exploring several applications leveraging this mixed-reality haptics approach.
The Nature of Digital Technologies - Development of a Multi-Layer Taxonomy

Digitalization disrupts business models and smartifies products and services. Based on the ever-faster emergence and adoption of digital technologies such as the Internet of Things, blockchain, or augmented reality, digitalization irreversibly changes our private lives and organizational routines from all industries on a global scale. Thereby, digitalization develops unlimited potential in terms of innovation, connectivity, efficiency and productivity improvements. However, research and practice still lack a fundamental understanding of the nature of digital technologies. To address this gap, we developed a multilayer taxonomy of digital technologies that includes eight dimensions structured along the layers of established modular architectures, i.e. service, content, network, and device. Based on our taxonomy, we also identified seven archetypes of digital technologies by means of a cluster analysis. To revise and evaluate our artefacts, we classified 45 digital technologies from the Gartner Hype Cycle of Emerging Technologies and conducted evaluation rounds with other researchers. Our results contribute to the descriptive knowledge on digital technologies. They enable researchers and practitioners to classify digital technologies on two levels of aggregation and to make informed decisions about their adoption.
Inference attack prevention of private information on social Networks

Online social networks, such as Facebook, Linked In are increasingly use by many people. These networks allow users to give details about themselves and allow connecting to their friends. Some of the information visible inside these networks is meant to be private. It is possible to use learning algorithms on released data to predict private information. We explore how to launch inference attacks using released social networking data to predict private information. We devise three possible sanitization techniques that could be used in various situations. Then, we define the effectiveness of these techniques and attempt to use methods of collective inference to discover sensitive attributes of the data set. We also show that we can decrease the effectiveness of both local and relational classification algorithms by using the sanitization methods we described.
Effect of Body Position on a Mobile, Vector-Derived, 12-Lead Electrocardiogram

Citation: Péter Kenedi. , et al. “Effect of Body Position on a Mobile, Vector-Derived, 12-Lead Electrocardiogram”. EC Cardiology 5.8 (2018): 589-595. Abstract Background: The electrocardiogram (ECG) is a critical component of cardiovascular diagnosis. ECGs are standardly recorded in the supine position; however, due to time and space constraints as well as patient limitations, they are often performed in other positions (sitting, standing). Several studies have examined the effect of body position on electrocardiograms using various methods, body positions, and parameters, with varied results reported. This study’s aim was to further evaluate the effect of body position on a mobile, vector-derived, 12-lead ECG, to determine if body position should be considered when performing an ECG. Methods: Electrocardiograms from 39 patients were examined in the lying, sitting, and standing positions. Heart rate, PQ interval, QRS duration, QTc interval, P-, QRS-, and T-vectors, and R and S amplitudes were statistically evaluated using correlation coefficient and one-factorial Analysis of Variance (ANOVA). Changes of the Q waves, ST segments, and T waves were qualitatively evaluated. Results: No changes of statistical or clinical significance were detected. No notable differences were seen in regard to intervals. For the vectors, a -9 degree change in the P axis with sitting, a -4 degree change in the QRS axis when standing, and a 3.8 degree increase in the T vector with change of position were seen. Negligible changes were seen in the wave amplitudes. 5/39 patients (13%) demonstrated T wave changes with change in position. Conclusion: The results suggest that a vector-derived, 12-lead electrocardiogram can be used in different body positions without impacting key ECG parameters.
Music genre classification using a hierarchical long short term memory (LSTM) model

This paper examines the application of Long Short Term Memory (LSTM) model in music genre classification. We explore two different approaches in the paper. (1) In the first method, we use one single LSTM to directly classify 6 different genres of music. The method is implemented and the results are shown and discussed. (2) The first approach is only good for 6 or less genres. So in the second approach, we adopt a hierarchical divide- and-conquer strategy to achieve 10 genres classification. In this approach, music is classified into strong and mild genre classes. Strong genre includes hiphop, metal, pop, rock and reggae because usually they have heavier and stronger beats. The mild class includes jazz, disco, country, classic and blues because they tend to be softer musically. We further divide the sub-classes into sub-subclasses to help with the classification. Firstly, we classify an input piece into strong or mild class. Then for each subclass, we further classify them until one of the ten final classes is identified. For the implementation, each subclass classification module is implemented using a LSTM. Our hierarchical divide-and-conquer idea is built and tested. The average classification accuracy of this approach for 10-genre classification is 50.00%, which is higher than the state-of-the-art approach that uses a single convolutional neural network. From our experimental results, we show that this hierarchical scheme improves the classification accuracy significantly.
Improved Detection and Classification of Convulsive Epileptic and Psychogenic Non-epileptic Seizures Using FLDA and Bayesian Inference

A high number of patients with epileptic seizures (ES) are misdiagnosed due to prevalence of mimic conditions. The clinical characteristics of mimics are often similar to ES. The events mostly misdiagnosed are of psychogenic origin and are termed as psychogenic non-epileptic seizures (PNES). The gold standard for diagnosis of PNES is video-electroencephalography monitoring (VEM), which is a resource demanding process. Hence, need for a more object method of PNES diagnosis is created. Accelerometer sensors have been used previously for the diagnosis of ES. In this work, we present a new approach for detection and classification of PNES using wrist-worn accelerometer device. Various time, frequency and wavelet space features are extracted from the accelerometry signal. Feature compression is then performed using Fisher linear discriminant analysis (FLDA). A Bayesian classifier is then trained using kernel estimator method. The algorithm was trained and tested on data collected from 16 patients undergoing VEM. When tested, the algorithm detected all seizures with 20 false alarms and correctly classified 100% PNES and 75% ES, respectively of the detected seizures.
Pseudoperonospora urticae Occurring on Urtica angustifolia in Korea

The genus Pseudoperonospora (Peronosporales, Oomycota) comprises six accepted species, including Ps. cubensis, which causes downy mildew on many economically important cucurbitaceous crops, and Ps. humuli, which occurs on hops. During a survey of downy mildew flora in Korea, a previously unreported species of Pseudoperonospora was found on Urtica angustifolia. Based on molecular phylogenetic and morphological analyses, the causal agent was identified as Pseudoperonospora urticae. This is the first report of Pseudoperonospora urticae occurring on Urtica angustifolia in Korea.