Miracle-dz/UniFAR-FTUs huggingface dataset:
Localization of the Swainsonine-Producing Chaetothyriales Symbiont in the Seed and Shoot Apical Meristem in Its Host Ipomoea carnea

Several species of fungi from the orders Chaetothyriales and Pleosporales have been reported to produce swainsonine and be associated as symbionts with plants of the Convolvulaceae and Fabaceae, respectively. An endosymbiont belonging to the Chaetothyriales produces swainsonine and grows as an epibiont on the adaxial leaf surfaces of Ipomoea carnea, but how the symbiont passes through plant growth and development is unknown. Herein, different types of microscopy were used to localize the symbiont in seeds and in cross sections of plant parts. The symbiont was found in several tissues including the hilum, the sclereids, and the hypocotyl of seeds. In five-day old seedlings and mature plants, the symbiont was found in the shoot apical meristem (SAM) and the adaxial surface of immature folded leaves. The mycelia generally formed a close association with peltate glandular trichomes. This report provides further data explaining the relationship between the seed transmitted Chaetothyriales symbiont and Ipomoea carnea. These results provide a possible explanation for how this symbiont, and others like Periglandula may persist and are transmitted over time.
Evaluation of Ground-Based Models for Estimating Surface Albedo with In-Situ Radiometric Measurements across China

Surface albedo is an essential parameter in many solar radiation applications. Although several models are available, it remains debatable whether they are applicable to other locations. Using long-term daily measurements of radiation acquired by observational networks in China, this study examined the applicability of six existing albedo models: Ineichen model (IeM), Gueymard model (GM), Dong model (DeM), Iziomon-Mayer model (IMM), Morton model (MM), and Zhou model (ZeM). The evaluation results of model performance through statistical analysis showed that among the available ground albedo models, ZeM had the best overall performance at 12 selected stations, IeM was shown to provide acceptable estimations for locations where albedo records are readily available. The statistical results of individual station have shownthat the number of input parameters is not the only key factor for determining the predictive performance of ground albedo models. In other words, a simple model has potential for accurate estimation of ground albedo with appropriate model parameters. Therefore, the simple two-parameter DeM was selected to re-calibrate with in-situ radiometric measurements, which can be adopted as a surrogate for ZeM to predict surface albedo in China.
Integrative NPK soil and foliar application improves growth, yield, antioxidant, and nutritional status of Capsicum annuum L. in sandy soils under semi-arid condition

Abstract Soils of arid and semi-arid environments are characterized by scant moisture conditions, low clay and organic matter contents, low water holding capacity and low inherent fertility, and hence various management practices including foliar fertilization is essential. The present study aims at assessing the effect of integrated soil and foliar application of nitrogen, phosphorus, and potassium (NPK) fertilizers on growth, yield, and contents of some osmoprotectants, antioxidant, and nutritional status of Capsicum annuum (L.) in sandy soil under semi-arid condition. A pot experiment was conducted with five NPK application treatments: 100% added to the soil + 0% foliar spray, 75% soil + 25% foliar spray, 50% soil + 50% foliar spray, 25% soil + 75% foliar spray, and 0% soil + 100% foliar spray. The results of the study indicate that the integrated treatment of 50% soil + 50% foliar spray proved to be the best treatment compared with other treatments. This treatment significantly increased growth components (leaf area plant‒1, shoot fresh and dry weights) and yield (number of fruits plant‒1, average fruit weight, and fruits weight plant‒1), and the contents of soluble sugars, free proline, and nutrients (NPK); while significantly reduced the contents of ascorbate and glutathione compared to other treatments, including the control. The combined treatment of 75% soil + 25% foliar spray was the second-best treatment with respect to all tested parameters. The study concludes that the recommend integrated treatment of 50% soil + 50% foliar spray minimizes nutrient loss and achieves higher growth and yield of hot pepper plants under semi-arid condition.
bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R

Non-regression-based inferences, such as discriminant analysis, can account for the eﬀect of predictor distributions that may be signiﬁcant in big data modeling. We describe bbl , an R package for Boltzmann Bayes learning, which enables a comprehensive supervised learning of the association between a large number of categorical predictors and multi-level response variables. Its basic underlying statistical model is a collection of (fully visible) Boltzmann machines inferred for each distinct response level. The algorithm reduces to the naive Bayes learner when interaction is ignored. We illustrate example use cases for various scenarios, ranging from modeling of a relatively small set of factors with heterogeneous levels to those with hundreds or more predictors with uniform levels such as image or genomic data. We show how bbl explicitly quantiﬁes the extra power provided by interactions via higher predictive performance of the model. In comparison to deep learning-based methods such as restricted Boltzmann machines, bbl -trained models can be interpreted directly via their bias and interaction parameters.
PartGAN: Weakly-supervised Part Decomposition for Image Generation and Segmentation

We propose PartGAN, a novel generative model that disentangles and generates background, object shape, object texture, and decomposes objects into parts without any mask or part annotations. To achieve object-level disentanglement, we build upon prior work and maximize the mutual information between the generated factors and sampled latent prior codes. To achieve part-level decomposition, we learn a part generator, which de-composes an object into parts that are spatially localized, disjoint, and consistent across instances. Extensive experiments on multiple datasets demonstrate that PartGAN discovers consistent object parts, which enable part-based controllable image generation.
MSc Electrical Power Systems-Dissertation Report

Increased penetration of renewables to the transmission network with energy storage systems has increased the need for extended reliability assessment of load points of transformers and transmission lines. Solar power intermittency limits its significance in maximum integration and dependence by utilities. Therefore, its necessary to determine PV and energy storage levels to improve essential infrastructure reliability. The essential component’s reliability is greatly influenced by the location PV and BESS. In this paper, the Markov modelling technique is adopted in designing both photovoltaic systems and BESS stochastic models. The model is used for reliability improvements studies of critical points. The paper is focused with PV and BESS locations and penetration levels influence on reliability improvements. This is achieved by using the failure and repair rates of photovoltaic and battery storage adopted in determining state probabilities and frequencies. The IEEE 24 bus system is used to evaluate load point reliability for different test case scenarios of photovoltaic integration and energy storage in Digsilent reliability tool Software. The simulation results indicate that integration of PV and BESS improves system reliability and load point reliability of critical infrastructures. However, integration of PV and BESS at different locations results in reduced reliability due to network overloading and solar intermittency effects. The network maximum PV and BESS penetration level is determined to be 32.5%, above which system failure occurs.
Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition

Brain-computer interfaces (BCls) decode recorded neural signals from the brain and/or stimulate the brain with encoded neural sig-nals. BCls span both hardware and software and have a wide range of applications in restorative medicine, from restoring movement through prostheses and robotic limbs to restoring sensation and communication through spellers. BCls also have applications in di-agnostic medicine, e.g., providing clinicians with data for detecting seizures, sleep patterns, or emotions. Despite their promise, BCls have not yet been adopted for long-term, day-to-day use because of challenges related to reliability and robustness, which are needed for safe operation in all scenarios. Ensuring safe operation currently requires hours of manual data collection and recalibration, involving both patients and clinicians. However, data collection is not targeted at eliminating specific faults in a BCI. This paper presents a new methodology for char-acterizing, detecting, and localizing faults in BCls. Specifically, it proposes partial test oracles as a method for detecting faults and slice functions as a method for localizing faults to characteristic patterns in the input data or relevant tasks performed by the user. Through targeted data acquisition and retraining, the proposed methodology improves the correctness of BCls. We evaluated the proposed methodology on five BCl applications. The results show that the proposed methodology (1) precisely localizes faults and (2) can significantly reduce the frequency of faults through retraining based on targeted, fault-based data acquisition. These results sug-gest that the proposed methodology is a promising step towards repairing faulty BCls.
Ideological extremism, perceived party system polarization, and support for democracy

Abstract Does ideological polarization undermine or strengthen people’s principled support for democracy? In this study, we suggest that different manifestations of ideological polarization have different implications in this respect. Using data from 11 surveys conducted with representative samples of the adult populations of a group of liberal democratic countries, part of the Comparative National Elections Project, we look at how people’s level of ideological extremism and their perceptions of ideological polarization in their countries’ party systems are related with their support for democracy. We show that citizens who hold more extreme ideological positions are indeed less supportive of democracy and that such a negative relationship is strengthened as citizens’ extremism increases. However, we also show that the citizens who display higher levels of principled support for democracy are those who perceive parties to be neither too distant nor too close to each other in ideological terms. In other words, while a very polarized partisan supply seems to undermine popular commitment with democracy, very low polarization may have similar consequences.
Efficient tool segmentation for endoscopic videos in the wild

In recent years, deep learning methods have become the most effective approach for tool segmentation in endoscopic images, achieving the state of the art on the available public benchmarks. However, these methods present some challenges that hinder their direct deployment in real world scenarios. This work explores how to solve two of the most common challenges: real-time and memory restrictions and false positives in frames with no tools. To cope with the first case, we show how to adapt an efficient general purpose semantic segmentation model. Then, we study how to cope with the common issue of only training on images with at least one tool. Then, when images of endoscopic procedures without tools are processed, there are a lot of false positives. To solve this, we propose to add an extra classification head that performs binary frame classification, to identify frames with no tools present. Finally, we present a thorough comparison of this approach with current state of the art on different benchmarks, including real medical practice recordings, demonstrating similar accuracy with much lower computational requirements.
SalientVR: saliency-driven mobile 360-degree video streaming with gaze information

Mobile 360° video streaming has grown significantly in popularity but the quality of experience (QoE) suffers from insufficient wireless network bandwidth. The state-of-the-art solutions are limited by the temporal correlation assumption. Recent studies are aware of the potential of saliency to further QoE improvement, but several fundamental challenges about saliency judgment, saliency acquirement, and quality adaptation are still not fully addressed. To solve these challenges, we present SalientVR, a saliency-driven mobile 360° video streaming system integrated with gaze information. We design (i) a precise gaze-driven saliency judging criterion for mobile VR viewers, (ii) two pragmatic gaze-driven, tile-level saliency acquiring methods based on cross-user similarity and a specific content-aware deep neural network respectively, and (iii) a lightweight saliency-aware quality adaptation algorithm with a motion-assisted online correction, which is robust to wireless bandwidth vagaries and saliency bias. Moreover, we contribute a gaze-annotated dataset and a gaze-driven quality assessment metric for 360° videos. By extensive prototype evaluations (based on dataset tests and user studies), compared to alternatives, SalientVR significantly enhances the video quality and reduces the rebuffering ratio over 4G/LTE network emulations and in the wild, which achieves a 43.68% QoE improvement.
Deep Learning Calabi-Yau Metrics

No analytic expressions for the Ricci-flat metrics on compact Calabi-Yau manifolds are known, which has led to the development of multiple numerical approximation schemes. This thesis shows that a deep learning approach using the energy functionals introduced by Headrick and Nassar can replace existing methods to approximate Calabi-Yau metrics on projective varieties. Building on top of the algebraic metrics introduced for Donaldson’s algorithm, the deep learning models introduced here can predict approximations to the Ricci-flat metric as a function of complex moduli parameters. A comparison with the benchmark of balanced metrics produced by Donaldson’s algorithm indicates these approximations are of relatively higher accuracy, justifying it as an alternative, standalone approximation scheme. This approach is facilitated by modern machine learning frameworks, which provide efficient automatic differentiation that can be used to derive geometrical objects, and work with complicated, geometrically motivated loss functions.
Computational Analysis of Active and Passive Flow Control for Backward Facing Step

The internal steady and unsteady flows with a frequency and amplitude are examined through a backward facing step (expansion ratio 2), for low Reynolds numbers (Re=400, Re=800), using the immersed boundary method. A lower part of the backward facing step is oscillating with the same frequency as the unsteady flow. The effect of the frequency, the amplitude, and the length of this oscillation is investigated. By suitable active control regulation, the recirculation lengths are reduced, and, for a percentage of the time period, no upper wall, negative velocity, region occurs. Moreover, substituting the prescriptively moving surface by a pressure responsive homogeneous membrane, the fluid–structure interaction is examined. We show that, by selecting proper values for the membrane parameters, such as membrane tension and applied external pressure, the upper wall flow separation bubble vanishes, while the lower one diminishes significantly in both the steady and the unsteady cases. Furthermore, for the time varying case, the length fluctuation of the lower wall reversed flow region is fairly contracted. The findings of the study have applications at the control of confined and external flows where separation occurs.
The Impact of Indoor Lighting on Work Comfortability in Temporary Workspace at Home during Work from Home (WFH)

This study aims to determine the significance of the indoor lighting effect on work productivity. The outcome of this research is expected to become a reference in improving the indoor lighting quality for the temporary workspace at home. Space utilisation for work at home increases in line with the practice of Large-Scale Social Restrictions (PSBB) and Work from Home (WFH). It forces some of the space function changes in the worker’s house. Work activities need to be supported by Indoor Health and Comfort (IHC), one of which is the suitability of light intensity and light temperature to the room functions. By applying a mixed-method approach, the research began with quantitative research, correlating the result analysed by the Pearson method. The research continued by qualitative research, study case approach. The collected data is analysed by the Tau Kendall method. The result of this research indicates that there is a significant positive correlation but not significant as indicated by the Pearson rvalue of 0.372 between the respondent’s thoughts about indoor lighting to their work duration.
Influencing sustainable consumer behavior on electricity consumption - Insights from an environmental documentary

Consumption of products that are produced by a massive exploration of natural resources should be extensively communicated. A story delivered in a documentary movie can be adopted to deliver compelling messages to the audience. This study aims to explore the insights embedded in an environmental documentary to provide input and suggestions for promotional messages. In addition, it is also to provide a framework to foster people’s sustainable behavior on electricity consumption. The methodology has applied a qualitative approach by conducting a thematic analysis. The data was drawn from a story of an environmental documentary that showed mining activities to produce electricity. It revealed some insights that can be adopted as messages for promoting consumption reduction on electricity.
Assessment of the impact of the kinetic effect of ion parallel heat conduction on DEMO-relevant SOL plasma using integrated SOL-divertor code SONIC

In plasmas of lower collisionality, such as the scrape-off layer (SOL) of a fusion tokamak device, the parallel heat conductivity of ions becomes smaller than the classical Spitzer–Harm model due to a nonlocal kinetic effect. We have assessed, by simulation, the impact and role of the kinetic effect of ion heat conductivity (abbreviated as ion KE in this paper) on DEMO-relevant tokamak SOL plasma, using the Japanese demonstration tokamak reactor concept JA DEMO. A series of test simulations, where the ion KE is modeled by a widely used free-streaming energy (FSE)-limited model, has demonstrated: (a) the ion KE decreases the ion parallel heat flux density in JA DEMO SOL (at the baseline operation scenario) around the X-point and further upstream of the low-field side (LFS) area along the separatrix, where the parallel collisionality is smaller. Up to 40%–60% decrease is observed, compared to the case without (w/o) ion KE, among the test cases where the ion KE level is scanned over the possible range (i.e. parameter α i of the FSE-limited model is varied over 0.2⩽αi⩽2.0 ); (b) the ion KE leads to significant increase in the ion temperature T i (up to 600% increase) and significant decrease in the ion density n i (up to −80%) over wide area of the SOL upstream. Energy balance analysis has suggested that the ion KE affects the upstream n i and T i by the power of 0.4 and −0.4, respectively, of the flux limiting factor, as long as spatial changes in plasma parameters are moderate. The results of this study serve as a fundamental assessment of the ion KE for DEMO- relevant SOL plasma, clarifying the need for further sophistication of the model toward more quantitative prediction.
RISK MANAGEMENT MEASURES IN CMMI

Risk management is a continuous process that could endanger the objectives of a project or application. Risks are handled to reduce and avoid threats effects on the objectives of the project. The sources of risk are both internal and external to the project. This research will identify general measures for the specific goal and its specific practices of Risk Management Process Area (PA) in Capability Maturity Model Integration (CMMI). CMMI is developed by Software Engineering Institute (SEI) in Carnegie Mellon University in USA. CMMI is a framework for improvement and assessment of computer information systems. The method we used to define the measures is to apply the Goal Questions Metrics (GQM) paradigm to the specific goals and its specific practices of Risk Management Process Area in CMMI.
Improvement of design parameters of geotextile containers made of polypropylene for removal of silt layers and industrial waste

The article deals with the issues of improving the design parameters of geotextile containers made of polypropylene for the removal of silt layers and industrial waste as exemplified by the Krasnodar reservoir. The article presents statistics of waste accumulation in the Russian Federation in recent years. A new technology for transportation and disposal of liquid waste is proposed. The calculation of the parameters of geotextile containers made of high-strength fabric materials based on polypropylene has been developed. The development of chemical industry has allowed us to offer a new cost-effective innovative technology of dewatering and waste disposal based on the use of geotextile containers made of high-strength fabric material. In the Russian Federation, more than 3.5 billion tons of waste are accumulated annually, the amount of which increases with the growth of economic activity. Of the waste produced in 2020, 47% was placed at the enterprises’ own facilities, and 19% was buried. In order to improve the state of the environment, it is necessary to improve waste storage and disposal technologies. The use of geotextile containers made of high-strength fabric materials for dewatering and waste disposal is the most environmentally and cost-effective technology that has no restrictions and contraindications for technical equipment and has all the necessary indicators of strength, rigidity and reliability for effective use.
Determining the Driving System for Robots with Delta 3 DOF Parallel Structure, Using SolidWorks

The robots which are composed of mechanisms with Delta 3 DOF parallel structure are increasingly used due to the advantages they poses such as high rigidity and that they can be used at high speed and acceleration. When the mechanical structure of the parallel robots with Delta 3 DOF structure is designed, a proper selection of the drive servomotors is imposed. The drive servomotors are chosen according to the values of the torque needed for the manipulated object to move in the workspace along a trajectory with a specific speed and acceleration. In this paper, the authors approach the study regarding the variation of the drive torques of parallel robots with Delta 3 DOF structure, when the manipulated object moves along a trajectory with imposed speed and acceleration, in order to choose the drive servomotors. In order to determine the value of the drive torque, SolidWorks software was used. From the results obtained by experimental means, it has been revealed that the usage of the software packages in the dynamic simulation of the mechanical assemblies determines the shorten of time, costs of projection, in comparison with practical testing which supposes the realization of the prototypes physically, expensive and time consuming process.