Miracle-dz/UniFAR-FTUs huggingface dataset:
Nuclear magnetic resonance studies of quadrupolar nuclei and dipolar field effects

Experimental and theoretical research conducted in two areas in the field of nuclear magnetic resonance (NMR) spectroscopy is presented: (1) studies of the coherent quantum-mechanical control of the angular momentum dynamics of quadrupolar (spin I > 1/2) nuclei and its application to the determination of molecular structure; and (2) applications of the long-range nuclear dipolar field to novel NMR detection methodologies.The dissertation is organized into six chapters. The first two chapters and associated appendices are intended to be pedagogical and include an introduction to the quantum mechanical theory of pulsed NMR spectroscopy and the time dependent theory of quantum mechanics. The third chapter describes investigations of the solid-state multiple-quantum magic angle spinning (MQMAS) NMR experiment applied to I = 5/2 quadrupolar nuclei. This work reports the use of rotary resonance-matched radiofrequency irradiation for sensitivity enhancement of the I = 5/2 MQMAS experiment. These experiments exhibited certain selective line narrowing effects which were investigated theoretically.The fourth chapter extends the discussion of multiple quantum spectroscopy of quadrupolar nuclei to a mostly theoretical study of the feasibility of enhancing the resolution of nitrogen-14 NMR of large biomolecules in solution via double-quantum spectroscopy. The fifth chapter continues to extend the principles of multiple quantummore » NMR spectroscopy of quadrupolar nuclei to make analogies between experiments in NMR/nuclear quadrupolar resonance (NQR) and experiments in atomic/molecular optics (AMO). These analogies are made through the Hamiltonian and density operator formalism of angular momentum dynamics in the presence of electric and magnetic fields.The sixth chapter investigates the use of the macroscopic nuclear dipolar field to encode the NMR spectrum of an analyte nucleus indirectly in the magnetization of a sensor nucleus. This technique could potentially serve as an encoding module for the recently developed NMR remote detection experiment. The feasibility of using hyperpolarized xenon-129 gas as a sensor is discussed. This work also reports the use of an optical atomic magnetometer to detect the nuclear magnetization of Xe-129 gas, which has potential applicability as a detection module for NMR remote detection experiments. « less
The impact of tropospheric mapping function on PPP determination for one-month period

When using the PPP method, it is recommended to take into account the tropospheric influences for obtaining reliable estimates. Global Navigation Satellite System (GNSS) observations taken at low elevation suffer more strongly from atmospheric, antenna phase center variation and multipath effects, hence the observations are noisier than those at higher elevation angle, but they are essential to decorrelate the estimated station height and tropospheric zenith delay (ZTD). To relate the ZTD in the direction of an observation, the so-called mapping function (MF) are used. In this article the influence of different mapping function was studieds such as: Niell mapping function (NMF), Global Mapping Function (GMF) in conjunction with the Global Pressure and Temperature 2 -GPT2, Vienna Mapping Function 1 and no mapping function. The MF were used at different elevation cutoff angles – 50, 70, 100 and 150. The impact was analyzed: a) on the postfit residuals of the ionospheric free combination for phase (LC) and for pseudorange (PC), b) daily variability for North, East and Up component; c) evaluation of coordinates repeatability and how they are affected by the changes of the cutoff elevation angle and mapping function. The analyzed data was taken from 4 EUREF stations for a period of one month October 2015. By using the VMF1 mapping function, the lowest value was obtained for the postfit residuals of the LC combination for all the stations. The difference in daily variation between each individual solution for the horizontal component is at the level of ~0.3 ÷ 0.5 mm, with smaller effect on the East component compared to North, whereas the Up component is at the level of ~1.0 ÷ 1.5 mm. The standard deviation (SD) is used as a measure of station position repeatability and the results suggested that for high precision determination a cutoff elevation angle of 100 should be used. Although at low elevation 50 and 70 – the VMF1 performs better than the GMF/GPT2 and NMF, after 100 the GMF/GPT2 is strongly in agreement with VMF1 and after 150 the NMF shows similar results as VMF1 and GMF/GPT2. ARTICLE INFO
Three-Dimensional High-Resolution MIMO Radar Imaging via OFDM Modulation and Unitary ESPRIT

Imaging and recognition of targets with complex maneuvers bring a new challenge to conventional radar applications. In this paper, the three-dimensional (3D) high-resolution image is attained in real-time by a Multiple-Input-Multiple-Output (MIMO) radar system with single Orthogonal-Frequency-Division-Multiplexing (OFDM) pulse. First, to build the orthogonal transmit waveform set for MIMO transmission, we utilize complex orthogonal designs (CODs) for OFDM subcarrier modulation. Based on the OFDM modulation, a preprocessing method is developed for transmit waveform separation without conventional matched filtering. The result array manifold is the Kronecker product of the steering vectors of subcarrier/transmit antenna/receive antenna uniform linear arrays (ULAs). Then, the high-resolution image of target is attained by the Multidimensional Unitary Estimation of Signal Parameters via Rotational Invariant Techniques (MD-UESPRIT) algorithm. The proposed imaging procedures include the multidimensional spatial smoothing, the unitary transform via backward-forward averaging, and the joint eigenvalue decomposition (JEVD) algorithm for automatically paired coordinates estimation. Simulation tests compare the reconstruction results with the conventional methods and analyze the estimation precision relative to signal-to-noise ratio (SNR), system parameters, and errors.
Development of Polyp Detection Technology by Analyzing Deep-learning

This paper proposes a technique of guiding a clinician by recognizing colon polyp in patients in real time at the time of endoscopic examination. Automatic recognition of colon polyp by endoscopic image has applied Res-net’s advanced technique for calculating weight, existing deep learning method to enhance accuracy of recognizing colon polyp. Drawn result values provides information on shape of colon polyp guiding whether there is colon polyp and location of colon polyp in real time. Dataset used in developing analysis technique was collected from 5,000 cases from normal group and 5,000 cases from patients with colon polyp. For verification, accuracy was drawn by calculating confusion matrix with 10-fold cross validation. Accuracy of proposed technique was verified by drawing AUC of 0.96. Dataset was tested with data from control group and patient group in the ratio of 50%. We plan to develop deep learning technique that calculates the ratio of risk of occurrence of colorectal cancer. It is important to secure various test data such as images of colon polyp by type and whether there is adenocarcinoma. We plan to develop service that can predict ratio of risk of occurrence of colorectal cancer by applying relevant techniques automatically.
Linear and Non-Linear Octagonal Neutrosophic Numbers: Its Representation, α-Cut and Applications

In this paper, the primarily focus is to extend the concept of Octagonal Neutrosophic Numbers (ONN) since these numbers provide a wide range of applications while dealing with more fluctuations in the linguistic environment. Firstly, mathematical notions and definitions of Linear, Symmetric and Asymmetric types are proposed. Secondly, α-Cut is defined. Finally, a case study is done by using the TOPSIS technique of MCDM.
World and National Stock Market Reactions to COVID-19

COVID-19—the world’s most recent pandemic, has caused economic and financial crises globally. The situation is continually evolving overtime in a series of events, and stock markets must respond immediately to these evolving events with updates of expected cash flows and the real and perceived risks. This study asks how and how early the world and national markets react, and to which event or events in the series. Using the event-study method, based on returns on the world, French, German, Italian, Spanish, U.K., U.S., Chinese, Philippine, and Thai stocks, the study found significant, negative reactions to the disease. The reactions were to COVID-19’s extensive media coverage and pandemic declaration, not to the evolving events and situations when they actually occur.
Reconstruction of Organ Boundaries With Deep Learning in the D-Bar Method for Electrical Impedance Tomography

Objective: Medical electrical impedance tomography is a non-ionizing imaging modality in which low-amplitude, low-frequency currents are applied on electrodes on the body, the resulting voltages are measured, and an inverse problem is solved to determine the conductivity distribution in the region of interest. Due the ill-posedness of the inverse problem, the boundaries of internal organs are typically blurred in the reconstructed image. Methods: A deep learning approach is introduced in the D-bar method for reconstructing a 2-D slice of the thorax to recover the boundaries of organs. This is accomplished by training a deep neural network on labeled pairs of scattering transforms and the boundaries of the organs in the data from which the transforms were computed. This allows the network to “learn” the nonlinear mapping between them by minimizing the error between the output of the network and known actual boundaries. Further, a “sparse” reconstruction is computed by fusing the results of the standard D-bar reconstruction with reconstructed organ boundaries from the neural network. Results: Results are shown on simulated and experimental data collected on a saline-filled tank with agar targets simulating the conductivity of the heart and lungs. Conclusions and Significance: The results demonstrate that deep neural networks can successfully learn the mapping between scattering transforms and the internal boundaries of structures.
Towards a hybrid computational strategy based on Deep Learning for incompressible ﬂows

The Poisson equation is present in very diﬀerent domains of physics and engineering. In most cases, this equation can not be solved directly and iterative solvers are used. For many solvers, this step is computationally intensive. In this study, an alternative resolution method based on neural networks is evaluated for incompressible ﬂows. A ﬂuid solver coupled with a Convolutional Neural Network is developed and trained on random cases with constant density to predict the pressure ﬁeld. Its performance is tested in a plume conﬁguration, with diﬀerent buoyancy forces, parametrized by the Richardson number. The neural network is compared to a traditional Jacobi solver. The performance improvement is considerable, although the accuracy of the network is found to depend on the ﬂow operating point: low errors are obtained at low Richardson numbers, whereas the ﬂuid solver becomes unstable with large errors for large Richardson number. Finally, a hybrid strategy is proposed in order to beneﬁt from the calculation acceleration while ensuring a user-deﬁned accuracy level. In particular, this hybrid CFD-NN strategy, by maintaining the desired accuracy whatever the ﬂow condition, makes the code stable and reliable even at large Richardson numbers for which the network was not trained for. This study demonstrates the capability of the hybrid approach to tackle new ﬂow physics, unseen during the network training. of 4 64 and 32 ﬁlters respectively). The half-scale has 6 respectively 32, 64, full has ﬁlter). activation function last two layers of each
Developing Optimal Distinctive Open Innovation in Private Universities: Antecedents and Consequences on Innovative Work Behavior and Employee Performance

The purpose of this study is to analyze the effect of the concept of optimal distinctive open innovation as mediating variable in relationship between Person-Job Fit and Person-Organization Fit and work innovation behavior and lecturer performance. The method used in this study are through Structural Equation Modeling (SEM) analysis with the object of the study conducted on 193 lecturers determined by purposive random sampling technique at private universities in Central Java. The findings showed significant effects of person-organization fit on the optimal distinctive open innovation and on innovative work behavior. Moreover, person-job fit is of significant on optimal distinctive open innovation, and on innovative work behavior. In testing the effect of mediating variables, optimal distinctive open innovation is of significant on innovative work behavior which in turn affecting the significant influence of innovative work behavior on lecturer performance. The findings emphasize that the success-oriented way of thinking requires the expertise of employees to always create creative, superior and unique ideas. Private universities must always pay attention to the principles of industrial management and professionalism in human resource management, in order to survive and develop. Superior skills will produce superior performance, and superior skills are distinctive competence that supports the company to achieve positional advantage.
Enhanced Global and Local Curvature Properties for Corner Detection

Corner detection is basically a methods used to extract certain kind of features in images which could produce some information including the location or position of the corner points. Thus, in this paper an enhancement shape corner detection method is proposed to detect true corners of shape images. The overall performance of the proposed enhanced shape corner detector and six other existing shape detectors and descriptors including Harris, SUSAN, Harris-Laplace, CSS, SIFT and global and local curvature properties is presented. The experimental results of corner detection methods are tested using the benchmark binary image MPEG-7 Core Experiment Shape-1 Part B dataset. To measure the performance of corner detection evaluation, an appropriate number of true corners were determined.
Of Disputes and Petitions: Exploring Media Framing of the 2012 Presidential Election Petition In Ghana

ABSTRACT This exploratory study investigates how the media in Ghana framed the 2012 presidential Election Petition case. Using quantitative content analysis of stories from the digital platforms of an elite and a popular commercial radio station in Ghana, I found that both digital platforms relied mainly on game and conflict frames and the use of official sources in presenting stories. The use of the game frame was associated with the presence of official sources, and the use of conflict and responsibility frames. The study extends the theory of media coverage of Supreme Courts by showing that the political antecedents of a case holds a heavy sway in how the media covers the Supreme Court. It further shows that frames employed are influenced by a confluence of media routines, source needs, and judicial ramifications for journalists.
Digitalisation and precarious work practices in alternative economies: Work organisation and work relations in e-cab services

The emergence of the gig economy is mostly an alternative to the core service economy, which is gradually being transformed in spatial and temporal dimensions. It is not work in the gig economy, per se, that is problematic, but the way in which it is organised or structured is creating ruptures across several dimensions. This article seeks to explore the work organisation and work relations practised in these gig economies. In particular, it provides empirical findings on work practices in e-cab services so as to highlight the growing precariousness, in both economic and social contexts, among the service providers.
Does the Presence of a Preexisting Open Wound Increase Complication Risk for Implant Procedures?

ABSTRACT Despite limited evidence, a common belief in surgical practice is that patients should not undergo elective surgery until any open wounds heal because of the risk of seeding infection to the new surgical site. To our knowledge, this is the first study to analyze postoperative complication rates for implants placed in the context of open wounds using the American College of Surgeons National Surgical Quality Improvement Program database. We examined 30-day postoperative infections for breast, hip, knee, and spine implants using the 2012-2016 American College of Surgeons National Surgical Quality Improvement Program database. Patients were excluded for missing data and propensity matched 1:10 for demographics and comorbidities to minimize confounders. Complication rates were assessed using Pearson χ2 tests. Of 475,101 patients who received breast, hip, knee, or spine implants, 1491 did so with an open wound. After propensity score matching, the overall complication rate remained higher with the open wound cohort (6.7% vs 3.8%, P < 0.001). Although the presence of an open wound was associated with an increased risk of superficial surgical site infection, it was not associated with an increased risk of organ space surgical site infection, the site best correlated with infection at the implant. Nor was the presence of an open wound associated with a statistically significantly increased risk of deep incisional surgical site infection or wound dehiscence. After controlling for demographics, preoperative comorbidities, and surgical factors, the presence of an open wound before implant surgery is associated with an increased risk of complications, readmission, and reoperation, but not necessarily infection at the surgical site.
Design of a novel hybrid control strategy for multi‐inverter parallel system for resonance suppression

Comparing with the LCL‐type single‐inverter parallel system, the multi‐inverter parallel system has more complex resonance characteristics which are generated by three type resonance sources (reference current changing in the inverter control system, reciprocal effect among multi‐inverters and grid background harmonic) and cause great negative effects on power system. In order to suppress the resonance problem of LCL‐type multi‐inverters parallel system, a novel control strategy is proposed in this paper based on model current predictive control (MCPC) and two‐degree‐of‐freedom control. Through MCPC, this strategy can reduce the number of current inner‐loop PI of control layer and remove PWM module. Then, a potential robustness can be obtained. From the outer loop of control layer perspective, because MCPC's value function consists of inverter's output current and reference current of control layer inner loop, they can be approximately regarded as equal. And then through constructing the inverse model of control object, the two‐degree‐of‐freedom control can be introduced to realized transfer function unitarization of control layer's outer loop, so that the order difference between numerator's highest order term and denominator's highest order term of inverter resonance transfer function can be reduced. Accordingly, it can be found that with the three type resonance sources, there is no peak value in their resonance transfer functions and their gains all present attenuation effects. Therefore, the resonance problems of multi‐inverter parallel system can be eliminated. Through the theoretical derivation and simulation results, it can be proved that the proposed method can not only enhance the transient performance of multi‐inverter parallel system, but also its resonance problems.
DIGITAL LEARNING USING BLENDED POE2WE MODEL IN ENGLISH LESSON FOR FACING 21ST CENTURY CHALLENGES

This study aims to (1) describe the steps of Blended POE2WE model in English lesson; (2) test the effectiveness of the Blended POE2WE model in English lesson, and (3)describe the attitudes and activities of teachers and students when implementing the Blended POE2WE model in English lesson at one of the senior high schools in Ciamis, Indonesia. The method used in this research is mixed method. Data collection techniques used in this study are questionnaire, observation and documentation. Likert scale is used to measure attitudes, opinions, and perceptions of teachers and students. To measure the effectiveness of the POE2WE blended model in English lesson, a t-test was used using SPSS software with the provision that t count > t table . The results show that the steps of learning using Blended POE2WE models were in accordance with 21 st century competence (4C). The t-test on the final test between the model and control groups in each class is as follows: (1) t-test on the final test of class X Social 2 with the X Social 5 is t = 13.189; (2) t-test on the final test of class X Social 3 with the X Social 5 is t = 14.946; (3) t-test on the final test of class X Social 4 with the X Social 5 is t = 6.290. Based on the data analysis results, it can be concluded that the final ability test for students in the control class has a significant difference (t tabel >1.66) so that this model is effective to use for improving student achievement in English lesson. Meanwhile, the students’ attitudes towards the implementation of the POE2WE blended model showed improvement, by which students' attitudes towards the learning process is 60.18%, towards the teaching materials is 54.63% and towards the English teachers is 71.29%.
Growth Optimization of Chlorella Vulgaris in Mixotrophic Culture Enriched with Nutrients using Experimental Design

In this research, statistical optimization designs were applied to evaluate the growth of the Chlorella vulgaris in mixotrophic cultures enriched with nitrogen and phosphorus. The influence of each factor was evaluated at three levels, so a multifactorial experimental design was created 32. The results obtained showed that microalgae growth improves 32% by adding 50 mg of nitrogen and 25 mg of Nitrogen to the culture. This project demonstrates the relevance of the design of experiments for the optimization of algal biomass production in order to obtain bioproducts.
CRITICAL THINKING SKILLS PROFILE OF SENIOR HIGH SCHOOL STUDENTS IN LEARNING CHEMISTRY

Purpose of the study: This current study aims to analyze students’ critical thinking skills in learning chemistry specifically colloidal concept. Methodology: The research method used in this research is mixed-method by using explanatory sequential design. Data collection techniques used interviews, observation, and essay tests that have been developed based on critical thinking indicators by Ennis. This research is based on tests and interviews of students of class XI IPA in Pekanbaru. Quantitative data analysis techniques used a pre-experimental research design with one group pretest-posttest design and qualitative data analysis techniques used interviews and observation. Main Findings: Through essay tests, the average pre-test score was 30.15 and the post-test score was 50.67. The results provide information about high school students 'critical thinking skills about the concept of colloids which are still low so that alternative learning strategies are needed to improve students' critical thinking skills in chemistry learning. Applications of this study: The results of the research, expected to provide an overview for teachers and researchers about the condition of critical thinking skills of high school students in chemistry learning, specifically the concept of colloids. Novelty/Originality of this study: In this research, the novelty is the development of five indicators of critical thinking skills, the results of qualitative and quantitative analysis of students' critical thinking skills on colloidal concepts.