Miracle-dz/UniFAR-FTUs huggingface dataset:
The Frequency Dependent Permittivity and AC Conductivity of Random Electrical Networks

It has been shown that the universal dielectric response (UDR) of heterogeneous materials can be reproduced by electrical networks consisting of randomly positioned resistor and capacitors. The random network represents a microstructure that contains both insulating (the capacitor) and conductive regions (the resistor). This paper presents an investigation into the frequency dependent properties of large numbers of resistor-capacitor (R-C) networks. Parameters investigated include permittivity, conductivity and phase angle, with particular emphasis on observed power-law behavior and a comparison with previous work in this area.
An Efficient Collaborative Communication Mechanism for MPI Neighborhood Collectives

Neighborhood collectives are introduced in MPI-3.0 standard to provide users with the opportunity to define their own communication patterns through the process topology interface of MPI. In this paper, we propose a collaborative communication mechanism based on common neighborhoods that might exist among groups of k processes. Such common neighborhoods are used to decrease the number of communication stages through message combining. We show how designing our desired communication pattern can be modeled as a maximum weighted matching problem in distributed hypergraphs, and propose a distributed algorithm to solve it. Moreover, we consider two design alternatives: topology-agnostic and topology-aware. The former ignores the physical topology of the system and the mapping of processes, whereas the latter takes them into account to further optimize the communication pattern. Our experimental results show that we can gain up to 8x and 5.2x improvement for various process topologies and a SpMM kernel, respectively.
Neuromechanical characterization of brain damage in response to head impact and pathological changes

Traumatic injuries to the central nervous system (brain and spinal cord) have received special attention because of their devastating socio-economical cost. Functional and morphological damage of brain is the most intricate phenomenon in the body. It is the major cause of disability and death. The paper involves constitutive modeling and computational investigations towards an understanding the mechanical and functional failure of brain due to the traumatic (head impact) and pathological (brain tumor) events within the framework of continuum damage mechanics of brain. Development of brain damage has been analyzed at the organ scale with the whole brain, tissue scale with white and gray tissue, and cellular scale with an individual neuron. The mechanisms of neurodamage growth have been specified in response to head impact and brain tumor. Swelling due to electrical activity of nervous cells under electrophysiological impairments, and elastoplastic deformation and creep under mechanical loading of the brain have been analyzed. The constitutive laws of neuromechanical behavior at large strains have been developed, and tension-compression asymmetry, as well as, initial anisotropy of brain tissue was taken into account. Implementation details of the integrated neuromechanical constitutive model including the Hodgkin-Huxley model for voltage into ABAQUS, ANSYS and in-house developed software have been considered in a form of the computer-based structural modeling tools for analyzing stress distributions over time in healthy and diseased brains, for neurodamage analysis and for lifetime predictions of diseased brains. The outcome of this analysis will be how the neuromechanical simulations applied to the head impact and brain tumor therapies may assist medical specialists with their decisions during planning and application of medical surgeries.
Reliability and validity of the Adult Learning Inventory among medical students

Objective : To determine the internal consistency and construct validity of the Adult Learning Inventory (AL-i) among first year medical students in a Malaysian medical school. Methods : Cross sectional study was done on 196 first year medical student in Universiti Sains Malaysia (USM). The Cronbach’s alpha reliability analysis, exploratory factor analysis and confirmatory factor analysis were applied to measure internal consistency and construct validity of the AL-i respectively. These analyses were done using Predictive Analytics SoftWare (PASW) version 18 and Analysis of Moment Structure (AMOS) version 19. Result : A total of 196 medical students responded to this study. Exploratory factor analysis showed that two potential constructs would be extracted from the inventory. The confirmatory factor analysis showed the two factor model with six items had a good fit with the latent constructs (X 2 (df) = 25.63 (8), p = 0.048, RMR = 0.045, GFI = 0.974, AGFI = 0.933, NFI = 0.974, RFI = 0.951, IFI = 0.987, TLI = 0.975, CFI = 0.987, RMSEA = 0.07). Each domain of the final model of the AL-i has three items. The Cronbach’s alpha value of the AL-i was 0.72. The Cronbach’s alpha values of andragogy and pedagogy domains were 0.87 and 0.86 respectively. Composite Reliability and Average Variance Extracted values were more than 0.6 and 0.5 respectively indicating good construct reliability and adequate convergent validity. Conclusion : This study suggested that the two factor model with 6 items of the AL-i has a good fit and shown good psychometric values. It is a valid and reliability measurement to determine types of leaner among first year medical students.
Finite-time stability of fractional-order delayed Cohen–Grossberg memristive neural networks: a novel fractional-order delayed Gronwall inequality approach

This article is dedicated to the improved approach for the finite-time stability (FTS) of fractional-order delayed Cohen–Grossberg memristive neural networks (FDCGMNNs). First, a novel delayed integer-order Gronwall inequality is established. Second, on the basis of this inequality, a novel fractional-order delayed Gronwall inequality is developed. Third, a novel FTS criterion of FDCGMNNs is derived by virtue of the novelly developed fractional-order delayed Gronwall inequality. Eventually, the effectiveness and less conservativeness of the proposed results are shown by two numerical examples.
Rheological evaluation of high temperature polymers to identify successful extrusion parameters

With the advancements in additive manufacturing (AM), several high temperature thermoplastics are being explored as potential AM feedstocks. Some of these high-performance thermoplastics include; polyetherimides (PEI), polyphenylsulfones (PPSU/F), poly (ether ketone ketone)s (PEKK) and polyphenylene sulfide (PPS) as well as their reinforced composites. Most of these advanced resins tend to be more expensive than commodity plastics such as acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA), and their processing parameters have not been determined for most AM systems. This paper demonstrates a method for identifying the appropriate processing conditions for extrusion-based AM deposition systems, in which a material is forced through an orifice at a given flow rate. The pressure required to extrude a shear-thinning thermoplastic at a given shear rate is calculated based on viscoelastic properties of the polymer melt and compared against maximum system pressure to predict successful extrusion. An evaluation of several candidate materials is presented on the Big Area Additive Manufacturing extrusion-based platform.
DNA Repair Gene ERCC 1 and ERCC 2 / XPD Polymorphisms and Risk of Squamous Cell Carcinoma of the Head and Neck

Methods: After informed consent was obtained, blood was drawn for genotyping. The ERCC1 C8092A polymorphism was typed by single-strand conformational polymorphism analysis. The ERCC2/XPD G23591A polymorphism was typed by polymerase chain reaction– based restriction fragment length polymorphism analysis with the enzyme StyI. The 2 analysis was used to assess differences in genotype and allele frequencies. Multivariate logistic regression analysis was performed to estimate the risk of SCCHN for individuals having these genotypes after adjustment for age, sex, tobacco smoking, and alcohol use.
Mycorrhizal efficacy of trifoliate orange seedlings on alleviating temperature stress

Citrus plants often suffer from temperature stress, which seriously inhibits tree growth and even results in tree death. The present experiment was conducted to evaluate the effects of Glomus mosseae on growth, root morphology, superoxide dismutase (SOD) and catalase (CAT) activities, and soluble protein content of trifoliate orange (Poncirus trifoliata) seedlings at low (15°C), optimum (25°C) and high (35°C) temperatures. Sixty-eight days after temperature stresses, mycorrhizal colonization and number of both entry points and vesicles were significantly inhibited by low or high temperature. Mycorrhizal seedlings recorded significantly higher growth characteristics than non-mycorrhizal seedlings at both optimum and high temperatures, but the beneficial effects were almost lost at low temperature. Generally, mycorrhizal seedlings presented notably higher root traits (projected area, surface area, number of forks and volume) than non-mycorrhizal seedlings regardless of temperature levels. Mycorrhizal colonization significantly increased SOD and CAT activities and soluble protein content at high temperature, increased only SOD activity at optimum temperature, and decreased only soluble protein content at low temperature. It suggests that mycorrhizal alleviation of temperature stress in trifoliate orange seedlings was at high temperature, but the alleviation was obviously weakened at low temperature.
Cranfield Astrobiological Stratospheric Sampling Experiment (Cass.E): Overview of Flight Hardware Configuration, Implemented Planetary Protection and Contamination Control Procedures and Preliminary Post-Flight Results

CASS•E is an experiment that is the initial part of a program aimed at collecting direct evidence of microorganisms in the Earth‟s stratosphere. The experiment was launched on-board a stratospheric balloon in collaboration with Eurolaunch through the BEXUS (Balloon-borne EXperiments for University Students) program from Esrange, Sweden, in October and November 2010. It consisted of a pump which drew air from the stratosphere through inlet tubes and collected samples on a particle collection filter located inside an Ultra Clean Zone. Due to the low density of microbes in the stratosphere compared to the known levels of contamination present during ground handling, the experiment incorporated Planetary Protection and Contamination Control (PP&CC) protocols in its design and construction in order to minimise the chance that any microbes detected were ground derived contamination. Space community derived cleaning and sterilisation techniques were employed throughout Assembly Integration and Testing (AIT) as well as biobarrier mechanisms which were designed to open only in the stratosphere to prevent re-contamination of the instrument after sterilisation. The material presented in the paper covers the design, AIT and preliminary postflight results of CASS•E. The objective of this initial implementation and the first flights was to better understand the issues of incorporating PP&CC and additional features to control and understand contamination into a balloon platform and thereby inform future experiments rather than perform a definitive collection of stratospheric samples for life detection.
Automated Cell Nuclei Segmentation in Overlapping Cervical Images Using Deep Learning Model

Recently, an automated cell nuclei segmentation is an attractive research area in the cervical cancer image analysis with the overlapping cells. Due to the poor contrast, diverse cells and the occlusion, the segmentation suffers from several issues such as inaccurate region detection and the lack of boundary refinement. To alleviate such issues, this paper proposes the deep learning framework with the boundary refinement techniques. Prior to boundary refinement, this paper utilizes the noise removal technique called Neighborhood Concentric Filtering (NCF) based on connected component analysis. Then, the proposed work segments the cell nuclei and cytoplasm with clear boundary refinement. The proposed work utilizes the N-ary ternarybased texture pattern extraction to collect the features that describe the regions clearly. Then, this paper utilizes the fisher model to select the relevant features in order to reduce the dimensionality that directly reduces the time complexity. Finally, the Deep Learning (DL) models through the neural network approaches classifies the abnormal cells effectively. Besides, this paper investigates the effectiveness in terms of the various performance parameters over the existing methods in cervical cancer image analysis for earlier diagnosis applications.
Deep Syntactic Annotations for Broad-Coverage Psycholinguistic Modeling

This paper presents new hand-corrected deep syntactic annotations for the sentences in two broad-coverage psycholinguistic datasets: the Dundee eye-tracking corpus (Kennedy et al., 2003) and the Natural Stories self-paced reading corpus (Futrell et al., 2017). These texts are more ecologically valid than experiment-speciﬁc constructed stimuli, allowing researchers to probe the sentence comprehension process in a naturalistic setting. Deep syntactic annotations such as categorial grammars allow direct access to phenomena like non-local or conjoined semantic argument dependencies which are relevant to many questions about sentence processing but are difﬁcult to compute from common markup frameworks such as Penn Treebank or Universal Dependencies. Previously no gold-standard deep syntactic markups have been available for either Dundee or Natural Stories. The deep syntactic representation used for the proposed annotations (Nguyen et al., 2012) has been shown to (1) facilitate direct extraction of long-distance dependencies as well as many other syntactic constructions of interest, (2) support accurate automatic parsing, and (3) generate surprisal estimates that correlate with measures of processing difﬁculty (van Schijndel and Schuler, 2015). These annotations can be used for any psycholinguistic inquiry in which predictors must be computed from latent syntax trees.
On the spatial propagation of transient magnetic fields in AC machines

AC motors have proliferated as the most important machine type used in speed variable drive systems. The dynamic analysis and description of revolving field machines is supported by well-established theories: Park's transformation (1929), and the space vector theory by Kovacs and Racz (1959). Yet some inconsistencies with the theory of dynamic systems exist; the machine eigenvalues suggest the existence of two damped oscillators. A transient condition is described by time-dependent oscillations of terminal quantities. It appears unsatisfactory that the respective eigenfrequencies change with the velocity of the reference frame. This contradicts the common understanding according to which the eigenfrequency is an inherent system property. A clarification is reached using a novel approach for the dynamic analysis. The approach is based on complex state variables. These characterize a transient condition by the spatial propagation of distributed magnetic fields. The formal analysis is an extension to the space vector theory and the theory of dynamic systems.
Dense medium separation: An effective and robust preconcentration technology

With energy costs increasing and ore grades diminishing, the role of pre-concentration in hard rock mining operations has been gaining greater interest. To maximize energy conservation, the preconcentration process should be conducted at as coarse a crush size as possible while minimizing losses of pay metals. Dense medium separation (DMS) is a robust process that can be conducted at particle sizes as coarse as 300 mm and as fine as 500 μm with high separation efficiency, depending on liberation characteristics of the value minerals. The DMS process involves three steps: feed preparation, dense medium separation, and ferrous-based media recovery. This paper discusses each of these processing steps, but focus will be given to the dense medium separation stage. Various types of DMS equipment are reviewed. Pilot plant campaign case studies conducted at the SGS Lakefield site are presented, which have included a variety of mineral systems such as spodumene, sulphide-bearing gold ores, and complex sulphide ores. These case studies demonstrate that for amenable ores, mass rejection of 20-60% is possible while maintaining recoveries of greater than 90% in most cases.
Height Dependence of Gas Flows in an Ellerman Bomb

We performed spectroscopic observations of Ellerman bombs (EBs) in an active region of NOAA 10705 at Hida Observatory on 2004 November 24. The photospheric velocity ﬁelds of EBs have for the ﬁrst time been investigated spectroscopically. From the Doppler shifts of a Ti II absorption line (6559.576 ˚A) and a broad H ˛ emission line, we derived the photospheric velocity and the lower chromospheric velocity, respectively. The photospheric velocity of EBs was (cid:2) 0:2 km s (cid:3) 1 , indicating downward ﬂow, on average. We found that the photospheric velocity variation of EBs has a good temporal correlation with the H ˛ wing emission variation. On the other hand, the chromospheric velocity showed an upward ﬂow of (cid:2) 1–3 km s (cid:3) 1 on the average. From the characteristics of the ﬂow ﬁeld, we conclude that the observed EB occurred at the upper photospheric level. We suggest that it is important to know the motions of EBs in the photosphere because a plausible triggering mechanism of EBs is magnetic reconnection in the low-lying atmosphere.
Hydrological modelling of a catchment using the SWAT model in the upper Blue Nile basin of Ethiopia

Sustaining upland agriculture and food security has been very much constrained by continuing land degradation brought by soil erosion due to lack of effective rainwater management strategies. Hydrological models are essential to understand the hydrological response of a catchment. The current paper focuses on hydrological modeling of catchment with SWAT model using its two versions, SWAT-CN and SWAT-WB. In addition, the impact of existing land and water management practices on runoff yield and the applicability of the model for the region were also addressed. The model was applied on two watersheds, Mizewa (27 km
Ultracold few fermionic atoms in needle-shaped double wells: spin chains and resonating spin clusters from microscopic Hamiltonians emulated via antiferromagnetic Heisenberg and t–J models

Advances with trapped ultracold atoms intensified interest in simulating complex physical phenomena, including quantum magnetism and transitions from itinerant to non-itinerant behavior. Here we show formation of antiferromagnetic ground states of few ultracold fermionic atoms in single and double well (DW) traps, through microscopic Hamiltonian exact diagonalization for two DW arrangements: (i) two linearly oriented one-dimensional, 1D, wells, and (ii) two coupled parallel wells, forming a trap of two-dimensional, 2D, nature. The spectra and spin-resolved conditional probabilities reveal for both cases, under strong repulsion, atomic spatial localization at extemporaneously created sites, forming quantum molecular magnetic structures with non-itinerant character. These findings usher future theoretical and experimental explorations into the highly correlated behavior of ultracold strongly repelling fermionic atoms in higher dimensions, beyond the fermionization physics that is strictly applicable only in the 1D case. The results for four atoms are well described with finite Heisenberg spin-chain and cluster models. The numerical simulations of three fermionic atoms in symmetric DWs reveal the emergent appearance of coupled resonating 2D Heisenberg clusters, whose emulation requires the use of a t–J-like model, akin to that used in investigations of high Tc superconductivity. The highly entangled states discovered in the microscopic and model calculations of controllably detuned, asymmetric, DWs suggest three-cold-atom DW quantum computing qubits.
GLOBULAR CLUSTERS AND DARK SATELLITE GALAXIES THROUGH THE STREAM VELOCITY

The formation of purely baryonic globular clusters with no gravitationally bound dark matter is still a theoretical challenge. We show that these objects might form naturally whenever there is a relative stream velocity between baryons and dark matter. The stream velocity causes a phase shift between linear modes of baryonic and dark matter perturbations, which translates to a spatial offset between the two components when they collapse. For a 2σ (3σ) density fluctuation, baryonic clumps with masses in the range 105–2.5 × 106 M☉ (105–4 × 106 M☉) collapse outside the virial radii of their counterpart dark matter halos. These objects could survive as long-lived, dark-matter-free objects and might conceivably become globular clusters. In addition, their dark matter counterparts, which were deprived of gas, might become dark satellite galaxies.
Effects of vibrationally excited molecules on the production of O and N radicals in a pulsed corona discharge

The vibrationally excited molecules play an important role in the plasma process. We numerically calculate the two-dimensional point-to-plane streamer propagation and the vibrational kinetics in atmospheric pressure dry-air (80% N2, 20% O2) environment. The calculated streamer discharge characteristics, such as the mean streamer velocity and the streamer channel radius, show good agreement with an experimentally obtained results. The nitrogen radical production by streamer discharge can be successfully reproduced in our simulation with vibrationally excited molecules effects. On the other hand, the vibrationally excited molecules are not so important in oxygen radical production.
ATOM SEARCH OPTIMIZATION – NEURAL NETWORK FOR DRIVING DC MOTOR

DC motor applications are very widely used because DC motors are very suitable for applications, especially control. Thus, a proper DC motor controller design is required. DC motor speed control is very important to maintain the stability of motor operation. A recent type of metaheuristic algorithm that mimics the motion of atoms is introduced. Atom search optimization (ASO) is a mathematical model and duplicates the behavior of atoms in nature. Atoms intercommunicate with each other via the delivering contact force in the form of the Lennard-Jones potential and the constraint force produced from the potential bond length. The algorithm is simple and easy to be applied. In this study, the atomic search optimization (ASO) algorithm is proposed as a speed controller for the control dc motor. First, the ASO proposed by the algorithm is applied for the optimization of the neural network. Second, the ASO-NN proposal was the result compared to other algorithms. This paper compares the performance of two different control techniques applied to DC motors, namely the ASO-NN and proportional integral derivative (PID) methods. The results show that the proposed method has effectiveness. The calculation of the proposed ASO-NN control shows the best performance in the settling time. The ASO-NN method has the capability of settling time 0.04 seconds faster than the PID method.