Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical and electrical diagnostics in an iodine plasma

Iodine is being studied as the next generation propellant for electric propulsion: dense, stored as a solid, it is also much cheaper than xenon for similar mass and ionization potential. Iodine plasmas are more complex and less studied than noble gases plasmas, leaving electric propulsion engineers with few models and diagnostics in their toolkits. Here we present three experimental diagnostics deployed on a low pressure Radio-Frequency inducted Inductively Coupled iodine plasma discharge. Two optical diagnostics are used to probe the I atom and the I2 molecule: classical absorption at 1.3 μm gives the line-of-sight integrated temperature and density of I; low-resolution broadband absorption spectroscopy between 480 and 500 nm gives the line-of-sight integrated density of I2. A Langmuir probe is used to determine the electron temperature and the negative species density at the centre of the plasma, provided that the data are treated by taking in consideration the electronegativity of the plasma. Comparisons with a global model derived from Grondein et al., 2016 tends to suggest that the recombination coefficient at the walls is underestimated for our setup.
Does inundation risk affect leaf-cutting ant distribution? A study along a topographic gradient of a Costa Rican tropical wet forest

Abstract: Successional state of forest and availability of pioneer plants are recognized factors affecting densities of leaf-cutting ants. However little is known about how abiotic factors can shape nest distributions. We investigated the effect of topography, soil, forest successional state and inundation risk on nest density and size of Atta cephalotes colonies along streams in a tropical wet forest in Costa Rica. In each forest type, we surveyed 12 sites, each site comprising five transects (10 × 100 m) varying in topography and proximity to streambeds. We found no difference regarding nest size or density between forest types or soil consociation. Nest density varied with topographic environment, with significantly higher nest density on slope tops (farther from streambeds) and without colonies in valley bottoms (closer to streambeds). Nests found in areas affected by the last great inundation before our study were scarcer and smaller than nests in non-flooded areas. We showed that inundation events favour an accumulation of Atta colonies towards higher sites, where they are also allowed to become larger and may survive longer. Inundation risk may be a strong force shaping the distribution of leaf-cutting ant nests in tropical floodplain forests, even concealing the relevance of successional state of forest.
Stakeholder Capacity Building in M&E on Performance of Literacy and Numeracy Educational Programme in Public Primary Schools in Nairobi County, Kenya

To create a radical change within the educational system in public primary schools in Kenya, there is need to invest more stakeholder capacity building and more so in monitoring and evaluation activities attached to educational programmes. In so doing, it is hoped that there would be transfer of knowledge and best practices within the education sector. The purpose of this study was to examine the influence of stakeholder capacity building for Monitoring and Evaluation on performance of literacy and numeracy educational programme. A descriptive survey research design and correlation design were used. To determine a sample size of 335 drawn from a target population of 2053, stratified sampling was conducted. A total 281 questionnaires were returned representing a response rate of 84%. Analysis of quantitative data was presented in frequencies and percentage whereas the mean and standard deviation were used to measure central tendency and dispersion respectively. Simple linear regression was conducted to test and measure both the relationship and strength of independent and dependent variable. The results revealed that r=0.675, R2=0.456, critical value was 3.875 less than the F value of 233.446 hence the model was fit, and the unstandardized beta coefficient was 0.490. Thus, it was concluded that stakeholder capacity building as part of Monitoring and Evaluation influences performance of literacy and numeracy educational programme. The study recommends that stakeholder capacity building is part of the Participatory Monitoring and Evaluation process, so it must be observed at all stages to ensure educational programme are implemented to the latter by bringing on board all the key stakeholders in education and particularly in literacy and numeracy skills aspects.
Morphological variation in Thermophis Malnate (Serpentes: Colubridae), with an expanded description of T-zhaoermii

7 Corresponding author 8 Equal contribution to this work Abstract The colubrid snake genus Thermophis is endemic to higher altitudes on the Qinghai-Xizang Plateau of China. It has been little studied and remains poorly understood. Until Guo et al.'s (2008) description of T. zhaoermii, Thermophis had been monotypic, containing only T. baileyi (Wall 1907). Here, a detailed comparison of external characters, skull and hemipe- nial morphology in T. baileyi and T. zhaoermii is presented. Both species are sexually dimorphic, and clearly distinct from one another in external, skull and hemipenial characters. An expanded characterization of the recently is provided.
Nonlinear Experimental Estimation and Verification of a MIMO System through EKF

Twin Rotor Multi Input Multi Output System (TRMS) is a highly nonlinear laboratory experimental model of helicopter. Real time experimental results are presented to prove the Extended Kalman Filter (EKF) observer performance in terms of estimation error convergence along with the Lyapunov stability analysis. The RMS error of the proposed EKF observer is compared with the previously published local state observer and neural observer estimation results [2]. Further an algorithm previously published in [19] is applied in real time TRMS and its RMS error are compared with earlier results. It is found that the proposed observer estimation errors for pitch angle and yaw angle are less than the earlier results. The efficient results are outcome of Matlab Simulink with Real time Windows target hardware–software interface. Robustness of the estimator is verified with the externally generated manual disturbance and against an unknown indirect surge in mains supply of the TRMS hardware. It was observed that the estimation errors are within 2 sigma limits. Further it is observed that wherever the slope of the pitch and yaw angle is fast, the estimation error section is denser and the time taken for estimation is less and vice versa.
Combined Effect of Explant, Inoculation Time and Co-cultivation Period on Agrobacterium - Mediated Genetic Transformation in Cucumber (Var. Shital)

Combined effect of explant, inoculation time and co-cultivation periodon Agrobacterium -mediated genetic transformation in cucumber was observed. Transformation ability was examined by histochemical assay of GUS reporter gene in survived calli. Conspicuous GUS positive (blue colour) region were detected in callus tissue. There were 3 factors in this investigation. Factor A consisted of three types of explants viz. leaf, nodal and internodal callus, factor B consisted of two durations of inoculation time viz. 3 and 5 min and factor C consisted of two co-cultivation periods viz. 24 and 48 hours. The highest number (4.04) and percentage (67.38%) of GUS +vecalliwas obtained from leaf explants when they were immersed in Agrobacterium suspension for 5 min and kept into co-cultivation media for 48 hrs.The lowest number(0.95) and percentage (15.89%) of GUS +ve calluswere observed in internodal explants when they were inoculated in Agrobacterium for 3 min and then co-cultivated for 24 hrs.
Characterization of Saccharomyces cerevisiae dna2 Mutants Suggests a Role for the Helicase Late in S Phase

The TOR proteins, originally identified as targets of the immunosuppressant rapamycin, contain an ATM-like “lipid kinase” domain and are required for early G1 progression in eukaryotes. Using a screen to identify Saccharomyces cerevisiae mutants requiring overexpression of Tor1p for viability, we have isolated mutations in a gene we call ROT1 (requires overexpression of Tor1p). This gene is identical to DNA2, encoding a helicase required for DNA replication. As with its role in cell cycle progression, both the N-terminal and C-terminal regions, as well as the kinase domain of Tor1p, are required for rescue of dna2 mutants. Dna2 mutants are also rescued by Tor2p and show synthetic lethality with tor1 deletion mutants under specific conditions. Temperature-sensitive (Ts) dna2 mutants arrest irreversibly at G2/M in a RAD9- and MEC1-dependent manner, suggesting that Dna2p has a role in S phase. Frequencies of mitotic recombination and chromosome loss are elevated in dna2 mutants, also supporting a role for the protein in DNA synthesis. Temperature-shift experiments indicate that Dna2p functions during late S phase, although dna2 mutants are not deficient in bulk DNA synthesis. These data suggest that Dna2p is not required for replication fork progression but may be needed for a later event such as Okazaki fragment maturation.
Economic Evaluation of Transformer Loss of Life Mitigation by Energy Storage and PV Generation

High penetration of plug-in electric vehicles (PEVs) can potentially put the utility assets such as transformer under overload stress causing decrease in their lifetime. The decrease in PV and battery energy storage system (BESS) prices has made them viable solutions to mitigate this situation. In this paper, the economic aspect of their optimal coordination is studied to assess transformer hottest spot temperature (HST) and loss of life. Monte Carlo simulation is employed to provide synthetic data of PEVs load in a residential complex and model their stochastic behavior. For load, temperature, energy price and PV generation, real data for City of College Station, Texas, USA in 2018 is acquired and a case study is developed for one year. The results illustrate using BESS and PV is economically effective and mitigates distribution transformer loss of life.
Coupling of 3D Analytical Calculation and PSO for the Identification of Magnet Parameters used in Magnetic Separation

─ In this work, the identification of the residual magnetic flux density of NdFeB permanent magnet, used in a drum magnetic separator, is performed. This identification problem has been formulated as an inverse optimization problem and solved using the Particle Swarm Optimization (PSO) method. For fast calculations, flux densities are computed using a 3D analytical method and the obtained results are compared with measurements. Index Terms ─ 3D analytical solution, inverse problem, magnetic separation, parameters identification, Particle Swarm Optimization, permanent magnet.
An Efficient Numerical Model for the Radiation Analysis of Microstrip Patch Antennas

─ An accurate integral equation method based on quasi-static relationship (QSR), thin dielectric sheet (TDS) method and high-order hierarchical Legendre (HOHL) basis function is proposed in this paper for fast analysis of microstrip antennas radiation problems. This technique employs the QSR of the current on the parallel plate capacitor to describe the field continuity boundary condition and to embody the tight coupling between the radiation patch, the ground plane and the substrate of microstrip antenna. The frequency offset problem of conventional VSIE can be effectively eliminated. Moreover, combined with the TDS and the HOHL basis function, the proposed model can provide high accuracy in input impedance and far-field performance with faster convergence speed and lower computational cost. Numerical results are presented to show the accuracy and efficiency of the proposed method. Index Terms ─ Microstrip antenna and array, numerical modelling, radiation analysis, quasi-static relationship, thin dielectric sheet method.
Implications for coral reef conservation of diver specialization

One activity with potential to provide a direct incentive for reef conservation is scuba diving. In the absence of effective management, diving can also have negative impacts. This study shows how an understanding of diver specialization can be used to help manage diving and increase its effectiveness as an incentive-driven conservation activity. Surveys were used to assess motivations, satisfactions and dive history of divers in Phuket (Thailand) and categorize divers by degree of specialization. Highly specialized divers were more likely to be on a live-aboard trip than less specialized divers and placed greater importance on the characteristics of the dive itself rather than the trip. Less specialized divers put more emphasis on non-dive characteristics. Satisfaction levels differed significantly among specialization groups in terms of overall satisfaction with the dive experience, satisfactions compared with motivations and satisfactions with specific trip characteristics. Overall there was a decline in satisfaction levels with increasing specialization. These finding are compared to a wildlife tourism model that links impacts with client characteristics and suggests a displacement of specialists by generalists and changes in the limits of acceptable change (LAC) over time. The dive data supported this progression leading to increased industry competition and reduced opportunities to sustain a broad-based dive industry that will act as an incentive-driven mechanism for reef conservation. Specific actions related to reef access and zoning according to a diver opportunity spectrum (DOS), establishing LAC and monitoring programmes, enforcing safety and environmental regulations are suggested to promote a more sustainable approach to dive management. Social science insights can be used to aid reef management strategies and increase the potential for diving to contribute towards reef conservation.
Improvement performances of Doubly Fed Induction Generator via MPPT Strategy using Model Reference Adaptive Control based Direct Power Control with Space Vector Modulation

In this paper Model Reference Adaptive Control (MRAC) for Wind energy conversion system (WECS) doubly fed induction generator (DFIG) is proposed using direct power control (DPC) based on space-vector modulation (SVM) afor fixed switching frequency in the d-q reference frame is proposed for wind generation application using MPPT strategy. First, a mathematical model of the doubly-fed induction generator written in an appropriate d-q reference frame is established to investigate simulations. In order to control the DFIG direct power control and space-vector modulation (DPC-SVM) are combined to replace the hysteresis controllers used in the original DPC drive, the active and reactive powers are controllers by a PID regulators. The performances of MRAC which is based on the DPC-SVM combination are investigated and compared to those obtained from the PID regulators Results obtained in Matlab/Simulink® environment show that the MRAC is more robust, superior dynamic performance and hence found to be a suitable replacement of the conventional controller for the high performance drive applications. Keywords— Model reference adaptive control (MRAC), Wind energy conversion system (WECS), Doubly fed induction generator (DFIG), Direct power control (DPC), Space vector modulation (SVM), Maximum power point tracking (MPPT).
Board Composition and Financial Reporting Quality of Deposit Money Banks in Nigeria

The management of public liability companies in Nigeria is responsible for the preparation of annual financial statement of which the auditor is to attest. Thus, financial report is one of the instruments used by stakeholders in assessing the board of directors’ performance. This study therefore, explored the extent at which the board composition (measured with three variables: Board size, board meeting and board independence) influence the financial reporting quality of Banks in Nigeria. The study used a sample of 15 Deposit Money Banks listed on the Nigeria Stock Exchange between 2005 and 2016. The data were analyzed and results estimated using Ordinary Least Square (OLS) Regression which was complimented with the panel data estimation technique. It was found that discretionary accrual as a proxy for financial reporting quality is positively related to Board size and board independence but relate negatively to board meeting. This study has established the fact that board composition is a key component of the quality of financial reporting quality of the listed Deposit Money Banks in Nigeria. It is recommended that the board size of banks in Nigeria should not be too large and must meet regularly to carry out their oversight function.
MLI-DSTATCOM Integrated With Distribution Transformer for Power Quality Enhancement in PDS

This article offers a novel cascaded structure convertor (CMC)-primarily based mostly distribution-kind STATCOM integrated with distribution electrical device for medium voltage utility. The star-related, n-level CMC is connected to the winding faucets on the first windings of the electrical device to reap voltage strain discount and maintain a bendy affiliation voltage degree. Taking complete use of the spare potential of distribution electrical device, a compromise between cascaded cells depends and also the voltage pressure is completed. The CMC-based altogether DSTATCOM is controlled as a controlled current supply to inject compensation current to the first windings through the taps. A modified decoupled fashionable manipulate is planned to control the device. Simulation version and scale-down model area unit developed to verify the performance of the planned integrated structure. Simulation and experimental effects demonstrate the great performance of the planned structure for reactive power compensation. Keywords—cascaded multilevel converter; DSTATCOM; reactive power compensation; winding taps; distribution transformer, Power Distribution Systems (PDS).
Graph Neural Networks with Continual Learning for Fake News Detection from Social Media

Although significant effort has been applied to fact-checking, the prevalence of fake news over social media, which has profound impact on justice, public trust and our society, remains a serious problem. In this work, we focus on propagation-based fake news detection, as recent studies have demonstrated that fake news and real news spread differently online. Specifically, considering the capability of graph neural networks (GNNs) in dealing with non-Euclidean data, we use GNNs to differentiate between the propagation patterns of fake and real news on social media. In particular, we concentrate on two questions: (1) Without relying on any text information, e.g., tweet content, replies and user descriptions, how accurately can GNNs identify fake news? Machine learning models are known to be vulnerable to adversarial attacks, and avoiding the dependence on text-based features can make the model less susceptible to the manipulation of advanced fake news fabricators. (2) How to deal with new, unseen data? In other words, how does a GNN trained on a given dataset perform on a new and potentially vastly different dataset? If it achieves unsatisfactory performance, how do we solve the problem without re-training the model on the entire data from scratch? We study the above questions on two datasets with thousands of labelled news items, and our results show that: (1) GNNs can achieve comparable or superior performance without any text information to state-of-the-art methods. (2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem---this issue has not been addressed in the previous work that applies GNNs for fake news detection. In order to solve the problem, we propose a method that achieves balanced performance on both existing and new datasets, by using techniques from continual learning to train GNNs incrementally.
An Accelerated DFO Algorithm for Finite-sum Convex Functions

Derivative-free optimization (DFO) has recently gained a lot of momentum in machine learning, spawning interest in the community to design faster methods for problems where gradients are not accessible. While some attention has been given to the concept of acceleration in the DFO literature, existing stochastic algorithms for objective functions with a finite-sum structure have not been shown theoretically to achieve an accelerated rate of convergence. Algorithms that use acceleration in such a setting are prone to instabilities, making it difficult to reach convergence. In this work, we exploit the finite-sum structure of the objective in order to design a variance-reduced DFO algorithm that provably yields acceleration. We prove rates of convergence for both smooth convex and strongly-convex finite-sum objective functions. Finally, we validate our theoretical results empirically on several tasks and datasets.
Multi-task Ensembles with Crowdsourced Features Improve Skin Lesion Diagnosis

Machine learning has a recognised need for large amounts of annotated data. Due to the high cost of expert annotations, crowdsourcing, where non-experts are asked to label or outline images, has been proposed as an alternative. Although many promising results are reported, the quality of diagnostic crowdsourced labels is still unclear. We propose to address this by instead asking the crowd about visual features of the images, which can be provided more intuitively, and by using these features in a multi-task learning framework through ensemble strategies. We compare our proposed approach to a baseline model with a set of 2000 skin lesions from the ISIC 2017 challenge dataset. The baseline model only predicts a binary label from the skin lesion image, while our multi-task model also predicts one of the following features: asymmetry of the lesion, border irregularity and color. We show that multi-task models with individual crowdsourced features have limited effect on the model, but when combined in an ensembles, leads to improved generalisation. The area under the receiver operating characteristic curve is 0.794 for the baseline model and 0.811 and 0.808 for multi-task ensembles respectively. Finally, we discuss the findings, identify some limitations and recommend directions for further research. The code of the models is available at this https URL.
Manifold Learning via Manifold Deflation

Nonlinear dimensionality reduction methods provide a valuable means to visualize and interpret high-dimensional data. However, many popular methods can fail dramatically, even on simple two-dimensional manifolds, due to problems such as vulnerability to noise, repeated eigendirections, holes in convex bodies, and boundary bias. We derive an embedding method for Riemannian manifolds that iteratively uses single-coordinate estimates to eliminate dimensions from an underlying differential operator, thus "deflating" it. These differential operators have been shown to characterize any local, spectral dimensionality reduction method. The key to our method is a novel, incremental tangent space estimator that incorporates global structure as coordinates are added. We prove its consistency when the coordinates converge to true coordinates. Empirically, we show our algorithm recovers novel and interesting embeddings on real-world and synthetic datasets.
Discrete ion stochastic continuum overdamped solvent algorithm for modeling electrolytes

When modeling electrolytes, molecular scale features, for example the electric double layer, often play a role in determining meso- and macro-scale dynamics. These features cannot be captured by continuum fluid dynamics based approaches, and direct simulation methods such as molecular dynamics can be computationally expensive. Here we present a mesoscale approach for the simulation of electrolytes which alleviates these issues: the discrete ion stochastic continuum overdamped solvent (DISCOS) method.