Miracle-dz/UniFAR-FTUs huggingface dataset:
An Asymmetric Nine-Phase Induction Motor for LCI-Fed Medium Voltage Drive Applications

This article presents a new asymmetric nine-phase induction motor-based load commutated inverter (LCI)-fed drive configuration as an alternative to the popular LCI-based synchronous motor drive topology in medium voltage (MV), high power applications. The proposed machine has three sets of three phase windings, two of which are designed to carry the rated active power from the LCI at MV levels. The other three phase winding is designed to handle all the reactive power requirement of the machine at a standard low voltage level from an insulated gate bipolar transistor (IGBT)-based voltage source inverter (VSI). The VSI also assists the LCI operation by supplying some additional reactive power required for safe commutation of the thyristors of the LCI. A smooth torque profile is also ensured by using the VSI to cancel out the low frequency harmonics in the quasi-square-shaped LCI currents. A laboratory prototype of the machine rated 75 kW, 1650/1650/400 V is designed and built to verify the proposed drive and the experimental results obtained are presented in this article.
Bringing Fronts Back: A Research Agenda to Investigate the Health and Well-Being Impacts of Front Gardens

This perspective article proposes an agenda to investigate the impacts of front gardens (yards) on health and well-being. As front gardens are increasingly being paved over, significant ecological benefits will be lost. In addition, urban green infrastructure has a measurable role to play in addressing major public health issues related to mental health, chronic stress, inflammatory, and cardiovascular diseases. The social value of front gardens remains un-quantified. Future research can contribute to assessing the psycho-socio-cultural value of front gardens, and fostering healthy environments for people to live, work, and play in. A better understanding of the health impacts of front gardens can provide a relatable argument to protect permeable surfaces and spaces for nature, as well as to discourage the general public from paving over their front gardens. We propose eight central research questions to be addressed in future work, and elaborate on further variables, lines of inquiry, and suggested intervention trials and observational studies. Potential research findings will have implications for decision-making in fields of horticulture, landscape architecture, urban planning, and public health.
A Sentiment Analysis Method Based on BLSTM and CNN Fusion

Sentiment analysis is a research hotspot in natural language processing in recent years. This paper proposes a sentiment analysis method that integrates LSTM and CNN in view of the fact that most existing sentiment analysis methods mix semantics and emotions. This method divides the text into semantic space and emotional space, and uses the attention model to train in two dimensions, and fuses the feature representations of the two to construct the final representation of the text. The results show that the model has achieved good results in multiple emotional review data.
A Mixed-Integer Convex Programming Algorithm for Security-Constrained Unit Commitment of Power System with 110-kV Network and Pumped-Storage Hydro Units

The secure operation of 110-kV networks should be considered in the optimal generation dispatch of regional power grids in large central cities. However, since 110-kV lines do not satisfy the premise of R << X in the direct current power flow (DCPF) model, the DCPF, which is mostly applied in the security-constrained unit commitment (SCUC) problem of high-voltage power grids, is no longer suitable for describing the active power flow of regional power grids in large central cities. Hence, the quadratic active power flow (QAPF) model considering the resistance of lines is proposed to describe the network security constraints, and an SCUC model for power system with 110-kV network and pumped-storage hydro (PSH) units is established. The analytical expressions of the spinning reserve (SR) capacity of PSH units are given considering different operational modes, and the SR capacity of PSH units is included in the constraint of the SR capacity requirement of the system. The QAPF is a set of quadratic equality constraints, making the SCUC model a mixed-integer nonlinear non-convex programming (MINNP) model. To reduce the computational complexity of solving the model when applied in actual large-scale regional networks, the QAPF model is relaxed by its convex hull, and the SCUC model is transformed into a mixed-integer convex programming (MICP) model, which can be solved to obtain the global optimal solution efficiently and reliably by the mature commercial solver GUROBI (24.3.3, GAMS Development Corporation, Guangzhou, China). Test results on the IEEE-9 bus system, the PEGASE 89 bus system and the Shenzhen city power grid including the 110-kV network demonstrate that the relaxed QAPF model has good calculation accuracy and efficiency, and it is suitable for solving the SCUC problem in large-scale regional networks.
Analysis and prediction of super-enhancers using sequence and chromatin signatures

Background Super-enhancers are clusters of active enhancers densely occupied by the Mediators, transcription factors and chromatin regulators, control expression of cell identity and disease associated genes. Current studies demonstrated the possibility of multiple factors with important roles in super-enhancer formation; however, a systematic analysis to asses the relative contribution of chromatin and sequence features of super-enhancers and their constituents remain unclear. In addition, a predictive model that integrates various types of data to predict super-enhancers has not been established. Results Here, we integrated diverse types of genomic and epigenomic datasets to identify key signatures of super-enhancers and their constituents and to investigate their relative contribution. Through computational modelling, we found that Cdk8, Cdk9 and Smad3 as new key features of super-enhancers along with many known. Comprehensive analysis of these features in embryonic stem cells and pro-B cells revealed their role in the super-enhancer formation and cellular identity. Further, we observed significant correlation and combinatorial predictive ability among many cofactors at the constituents of super-enhancers. By utilizing these features, we developed computational models which can accurately predict super-enhancers and their constituents. We validated these models using cross-validation and also independent datasets in four human cell-types. Conclusions Our analysis of these features and prediction models can serve as a resource to further characterize and understand the formation of super-enhancers. Taken together, our results also suggest a possible cooperative and synergistic interactions of numerous factors at super-enhancers and their constituents. We have made available our analysis pipeline as an open-source tool with a command line interface at https://github.com/asntech/improse.
Prediction in MOOCs: A Review and Future Research Directions

This paper surveys the state of the art on prediction in MOOCs through a systematic literature review (SLR). The main objectives are: first, to identify the characteristics of the MOOCs used for prediction, second, to describe the prediction outcomes, third, to classify the prediction features, fourth, to determine the techniques used to predict the variables, and, fifth, to identify the metrics used to evaluate the predictive models. Results show there is strong interest in predicting dropouts in MOOCs. A variety of predictive models are used, though regression and support vector machines stand out. There is also wide variety in the choice of prediction features, but clickstream data about platform use stands out. Future research should focus on developing and applying predictive models that can be used in more heterogeneous contexts (in terms of platforms, thematic areas, and course durations), on predicting new outcomes and making connections among them (e.g., predicting learners’ expectancies), on enhancing the predictive power of current models by improving algorithms or adding novel higher-order features (e.g., efficiency, constancy, etc.).
Multiple Kernel-Based Multimedia Fusion for Automated Event Detection from Tweets

A method for detecting hot events such as wildfires is proposed. It uses visual and textual information to improve detection. Starting with picking up tweets having texts and images, it preprocesses the data to eliminate unwanted data, transforms unstructured data into structured data, then extracts features. Text features include term frequency-inverse document frequency. Image features include histogram of oriented gradients, gray-level co-occurrence matrix, color histogram, and scale-invariant feature transform. Next, it inputs the features to the multiple kernel learning (MKL) for fusion to automatically combine both feature types to achieve the best performance. Finally, it does event detection. The method was tested on Brisbane hailstorm 2014 and California wildfires 2017. It was compared with methods that used text only or images only. With the Brisbane hailstorm data, the proposed method achieved the best performance, with a fusion accuracy of 0.93, comparing to 0.89 with text only, and 0.85 with images only. With the California wildfires data, a similar performance was recorded. It has demonstrated that event detection in Twitter is enhanced and improved by combination of multiple features. It has delivered an accurate and effective event detection method for spreading awareness and organizing responses, leading to better disaster management.
Simplified Heat and Mass Transfer Modeling for Anna Apples Cold Storage

Uniform storage conditions in cold stores are difficult to attain in practice. All storage facilities have one thing in common, the need for proper humidity control. High product and air temperatures and moistures are often observed in certain positions of a cold room leading to deterioration of food quality and safety. To reduce food losses, it is necessary to understand heat and mass transfers. The aim of this study was control of relative humidity inside cold rooms at 90 and 95% and it compared with other cold room without control of relative humidity to improve the optimum cooling condition and 1°C of all cooling rooms. A simplified model was used for describing and predicted the trend of heat and mass transfer of Anna apples (product and air temperatures and weight losses) at different zones in the cold room. Temperature and velocity of air and apples measurements were carried out inside cold room filled with four apple pallets. The result showed that, Good agreement between the predicted and experimental results was found for both final average Anna apple temperature and weight loss inside cold room.
Extraction of genomic DNA from woody plants and it's identification

Genomic DNA was extracted from leaves of Vitis amurensis,Actinidia arguta,Quercus mongolica,Juglans mandshurica and needles of Pinus sibirica,Pinus pumila by the method of modified CTAB.The isolated genomic DNA length was similar to lambdaDNA(48kb) and is suitable for both PCR amplification and digestion with restriction endonucleases.The absorbance ration(A_(260)/A_(280)) ranged 1.66--1.89. The quality of DNA extracted from third and fourth times suspension was better than from first and second times suspension. The yields of DNA ranged from 15mu-g·g~(-1) fresh mass (from fourth time suspension J. mandshuric) to 272mu-g·g~(-1) fresh mass (from third time suspension of V. amurensis). Although from first and second times suspension of P. pumila and P. sibirica wasn't isolated genomic DNA, but from third and fourth times suspension was obtained high quality genomic DNA. The genomic DNA can be used for next analysis via identification of PCR and digestion with restriction endonucleases.
Design and Implementation of Half-Bridge Resonant Converter With Novel Primary-Side Control

The opto-coupler is usually used for isolated resonant converter to feedback the secondary-side output information. However, the characteristics of the opto-coupler are easily affected by the operating conditions, which will cause higher standby power loss. In this article, a novel primary-side controlled isolated half-bridge resonant converter with output voltage and current estimation for LLC and series resonant converter (SRC) operations is presented. The output voltage is estimated by sampling the auxiliary winding voltage when the output diode current flows to zero, the output current is estimated by integrating the resonant current until the transformer decoupled. The operating principles of the half-bridge resonant converter and the design criteria of the key component parameters are addressed and analyzed. Also, the methods for estimating output voltage and output current are discussed in detail. Finally, an experimental prototype with rated power 100 W half-bridge resonant converter for input voltage 280–342 V and output voltage 24 V is built with microcontroller to validate the proposed primary-side regulation technique. Experimental results show the output voltage and current can be controlled with the error less than 1% and 8%, respectively. The highest efficiency of the system is 95.2%.
CODE-RADE a user centric system for delivery of science software.

Scientific computing can be distilled to the execution of sets of applications that are combined into complex workflows. Due to both the complexity and number of scientific packages and computing platforms, delivering these applications to end users has always been a significant challenge through the grid era, and remains so in the cloud era. In this contribution we describe a platform for user-driven, continuous integration and delivery of research applications in a distributed environment - project CODE-RADE. Starting with 6 hypotheses describing the problem at hand, we put forward technical and social solutions to these. Combining widely-used and thoroughly-tested tools, we show how it is possible to manage the dependencies and configurations of a wide range of scientific applications, in an almost fully-automated way. Finally, we will give some insight into how this platform could be extended to address issues of reproducibility and collaboration in scientific research in Africa.
Matlab-Realized Visual A* Path Planning Teaching Platform

Path planning in the global known environment is one of the main prob-lems in the field of mobile robots. Based on the characteristics of A* search method, this paper designs a simulation platform which is visible during the op-eration process to achieve graphicalization of the A* algorithm search process. The simulation platform is implemented by Matlab GUI method, which provides multi-parameter setting function, and outputs the specific traversal process of the planning method in the search space into the RGB space. The results of applying simulation platform to the teaching environment shows that this platform can provide an intuitive description of the path planning method. By extracting sam-ple data from the actual audience, the simulation platform proposed in this paper is compared with the platformless dissemination method. The experimental re-sults show that the simulation platform given in this paper can effectively im-prove the dissemination effect of the conventional multimedia teaching method.
Backstepping Control for Large Signal Stability of High Boost Ratio Interleaved Converter Interfaced DC Microgrids With Constant Power Loads

With the penetration of renewable generation and tightly regulated power electronic loads, the power quality and stability of modern dc microgrids are greatly challenged. To solve this problem, energy storage systems (ESSs) are widely proposed. Unfortunately, most of the existing control methods for ESSs can only ensure the stability of dc microgrids with small signal disturbances, which may not be satisfying for real-time applications where large signal disturbances exist. Moreover, they are usually designed for low boost ratio and low power converters, negating their suitability for grid-scale applications. To ensure the large signal stability of dc microgrids using high boost ratio interleaved converter interfaced ESSs, this article proposes a new backstepping control strategy with finite-time disturbance observers. Its effectiveness is verified by simulation and experiments carried out on an interleaved double dual boost converter.
Multisensor calibration and data fusion using LIDAR and vision

The work presented in this thesis expands an existing extrinsic calibration package to vision-based sensors. A ball is used as a calibration target to estimate the pose of multiple sensors relative to a reference frame. First, an algorithm is developed to detect the ball in an image then, two methods are implemented and tested to estimate the camera pose relative to a reference frame. Additionally, a graphical user interface is developed for the extrinsic calibration package. The interface allows multiple sensor configurations to be calibrated, greatly simplifies the calibration procedure and is easily expandable to new sensors. To illustrate the expansion process, Microsoft Kinect 3D-depth sensor is integrated in the calibration package and interface. Finally, from the estimated poses, sensor data fusion is achieved with ATLASCAR 1, an autonomous vehicle, and in indoors tests.
Adaptive features of a MIMO full-authority controller

Using a nonlinear 15-state helicopter model in 6 DOF a MIMO neural full-authority controller, which acts as a rate-damping SCAS, has been designed. The control activity is handled by two adaptive multi-MISO neural entities, which identify the forward and the inverse nonlinear helicopter model and are connected according to the predictor-corrector scheme. Either the inverse and the forward model are trained on-line: this procedure makes the controller adaptive as well as robust as regards the uncertainties, the modelling errors and sudden modifications of the dynamics (due to demages of the command system). Robustness and reconfigurability are valued through the analysis of the time histories which represent the closed loop response. Different scenari are proposed, in which the controller is requested to operate far from nominal conditions, maintaining unchanged its performance. Results reveal poor stability features for the off-line controller even in nominal conditions, while show that the neural rate-damping is robust and fault tolerant, when is fully trained on-line.
SPI2F - A Prototype Code Generator for Security Protocols

This thesis describes a new prototype tool that automatically generates a secure F# implementation of any protocol described in the Spi calculus. Type systems were previously proposed for analysing the security of both Spi calculus processes and F# implementations. The thesis investigates a formal translation from the Spi calculus to F# that is proved to preserve typability, and therefore the security properties of the original protocol are preserved.
Toward a Generative Human-in-the-Loop Approach for Conceptual Design Exploration Using Flow Failure Frequency in Functional Models

A challenge systems engineers and designers face when applying system failure risk assessment methods such as Probabilistic Risk Assessment (PRA) during conceptual design is their reliance on historical data and behavioral models. This paper presents a framework for exploring a space of functional models using graph rewriting rules and a qualitative failure simulation framework that presents information in an intuitive manner for human-in-the-loop decision-making and human-guided design. An example is presented wherein a functional model of an electrical power system is iteratively perturbed to generate alternatives. The alternative functional models suggest different approaches to mitigating an emergent system failure vulnerability in the electrical power system’s the heat extraction capability. A preferred functional model configuration that has a desirable failure flow distribution can then be identified. The method presented here helps systems designers to better understand where failures propagate through systems and guides modification of systems functional models to adjust the way in which systems fail to have more desirable characteristics.
Enhanced Convergent Encryption Key Generation for Secured Data Deduplication in Cloud Storage

Data deduplication is broadlyfor maintaining asole copy of user’s data in cloud storage servers. Data Deduplication is a process of verify that whether the data are already stored in the cloud storage or not and maintain a single copy of data in the cloud. Deduplication is possible in cloud only with the use of convergent encryption (CE) technique. This technique is used to generate convergent encryption key from the user data which is to be uploaded to the cloud and the user’s data are encrypted with this convergent encryption key. This CE is exposed to different cryptography attacks. To maintain secure deduplication with convergent encryption in cloud, this paper proposes enhanced convergent encryption key generation technique. The paper introduces scheme with Key Generation and Management as a Service (KGMaaS) mechanism for generating and maintaining key for convergent encryption and deduplication process. Generation of convergent encryption key takes place in user’s side. In this process, a key is used for generating the convergent encryption key unlike existing method. Key used to generate convergent encryption key is received from KGMaaS. KGMaaS is assumed to be a trusted party for key generation and sharing. The proposed technique is analyst for security vulnerability, and it is ensure the security and efficient deduplication by procedure used in the overall data deduplication process.
Convergence analysis of a characteristics-based hybrid method for multicomponent transport in porous media

In this paper, convergence of a characteristics-based hybrid method recently introduced in Daripa & Dutta (J. Comput. Phys., 335:249-282, 2017) has been proved. This method which combines a discontinuous finite element method and a modified method of characteristics (MMOC) has been successfully applied to solve a coupled, nonlinear system of elliptic and transport equations that arise in multicomponent two-phase porous media flows. The novelty in this paper is the convergence analysis of the MMOC procedure for a nonlinear system of transport equations. For this purpose, an analogous single-component system of transport equations has been considered and possible extension to multicomponent systems has been discussed. Error estimates have been obtained and these estimates have also been validated by realistic numerical simulations of flows arising in enhanced oil recovery processes.