Miracle-dz/UniFAR-FTUs huggingface dataset:
Off-Grid Fuel Cell System for High Power Quality Applications

There are a large number of critical loads, such as information technology, telecommunication, medical equipment and industrial process controls, which require high power quality. Conventional approaches to provide high power quality levels include battery based uninterruptible power supplies (UPSs) and emergency generators, using in most cases fossil fuels. These approaches suffer from reliability/maintenance problems associated with battery operating constraints, and from environmental impacts associated with the emissions of particles by the emergency generators. Systems that use fuel cells as electric energy sources are starting to be seen as a possibility for onsite energy production since they can work with high efficiency and without harmful emissions. In this work it is proposed a structure of a fuel cell system composed by a GenCore TM proton exchange membrane (PEM) 5kW Fuel Cell system from Plug Power and a single-phase inverter, which can operate either as an off-grid system or as a backup system in a high power quality system.
CALIPSO Level 3 Stratospheric Aerosol Product: Version 1.00 Algorithm Description and Initial Assessment

Abstract. In August 2018, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) project released a new level 3 stratospheric aerosol profile data product derived from nearly 12 years of measurements acquired by the space-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). This monthly averaged, gridded level 3 product is based on version 4.2 of the CALIOP level 1 and level 2 data products, which feature significantly improved calibration that now makes it possible to reliably retrieve profiles of stratospheric aerosol extinction and backscatter coefficients. This paper describes the science algorithm and data handling techniques that were developed to generate the CALIPSO version 1.00 level 3 stratospheric aerosol profile product. Further, we show that the retrieved extinction profiles capture the major stratospheric perturbations over the last decade resulting from volcanic eruptions, extreme smoke events, and signatures of stratospheric dynamics. Initial assessment of the product by inter-comparison with the stratospheric aerosol retrievals from the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) indicates good agreement in the tropical stratospheric aerosol layer (30° N–30° S), where the average difference between zonal mean extinction profiles is typically less than 25 % between 20 km and 30 km. However, differences can exceed 100 % in the very low aerosol loading regimes found above 25 km at higher latitudes.
Large-scale Analysis of Software Reuse for Code and License Changes

on the idea from Stack Overflow. To further understand the barriers of reusing code and to obtain suggestions for improving the code reuse process on Q&A platforms, we conducted a survey with 453 open source developers who are also on Stack Overflow. We found that the top 3 barriers that make it difficult for developers to reuse code from Stack Overflow are: (1) too much code modification required to fit in their projects, (2) incomprehensive code, and (3) low code quality. We summarized and analyzed all survey responses and we identified that developers suggest improvements for future Q&A platforms along the following dimensions: code quality, information enhancement & management, data organization, license, and the human factor. Our findings can be used as a roadmap for researchers and developers to improve code reuse.
On the development of inductive learning algorithms: generating flexible and adaptable concept representations

Vast amount of research in machine learning has focused on creating new algorithms stemming from reenements to existing learning models (e.g., neural nets, decision trees, instance-based learners, rule-based systems, bayesian estimators). This is convenient when a model M already exists exhibiting satisfactory performance over the class of domains of study S. It may occur, however, that no model over S performs adequately; the deenition of a new learning model is then necessary. The construction of a new learning model cannot rely on assumptions common to a process based on reenements alone: that a global evaluation metric is enough to guide the development process (e.g., a comparison based on predictive accuracy alone), and that the strategy of the model is always correct (inherent limitations in the strategy may exist). When a model is deened from scratch, it is recommended to adopt a functional view. This means we must rst deene the set of functionalities the new mechanism will perform (e.g., to cope with feature and class noise, to generate hypotheses amenable to interpretation and analysis, to elucidate feature interaction eeectively, etc.). After implementation, a functional decomposition analysis can assess how well the diierent components in the mechanism are able to carry out such functionalities. In the rst part of this thesis I outline basic steps for the development of new learning models. The outline conveys relevant information to the developer: 1) that the set of goal functionalities must be made clear from the beginning, and that the design and implementation must be oriented to accomplish such functionalities; 2) much can be learned from a functional decomposition analysis; by analyzing each component individually we can detect possible deeciencies; 3) it is vital to propose a design in which all components are made explicit, and thus amenable to modiication or replacement. iv To provide evidence supporting the importance of a functional view in the design of new learning models, I describe two experimental case studies. These studies serve to support the following ideas: that separating an algorithm into its constituent components is helpful to identify the individual contribution of each component; and that components of diierent nature can be integrated to achieve a desired functionality. The second part of this thesis deals with the design and implementation of a new learning algorithm: HCL (Hierarchical Concept Learner). This part has two main goals: to put into practice the basic steps for the development of …
High-Resolution ISAR Imaging With Sparse Aperture VFM Waveforms Under Low SNR Condition

In recent years, the compressed sensing theory has been widely used in sparse aperture radar imaging. The inverse synthetic aperture radar (ISAR) imaging based on the sparse aperture echo of V-style frequency modulation (V-FM) waveform, which can mitigate the ambiguity appeared in range and velocity, has been proposed in this paper. After analyzing and interpreting the reason why the VFM signal pulse compression needs to use dual channels, we built the VFM waveform sparse echo model and analyze the causes of echo sparseness. A modified weighted compressive sensing (MWCS) algorithm is proposed to obtain high-resolution images under strong noise environment. The innovation of this paper lies in the new weighting method and the iterative reconstruction algorithm. The experimental results are shown to demonstrate the validity of the proposed method.
Yeast Two‐Hybrid Technology

Please find below the published articles that we are aware of reporting the biochemical and functional validation of protein interactions discovered with Hybrigenics Yeast Two-Hybrid technology, ULTImate Y2H™. The mean impact factor for these 321 publications is 8.1 and the top 18 papers have an impact factor above 19.
Exploring Machine Learning Techniques for Irony Detection

Irony detection is considered a complex task in Natural Language Processing. This paper ﬁrst introduce and cover the recently state of irony detection. Then we review and summarize previous related research on text-based irony detection. Finally we compare various classiﬁers including the proposed CNN model on three dataset of tweets, and analysis and discuss the results. We conclude that CNN is eﬀective for irony detection under various situation with our model outperforming all the other classiﬁers
A Second-Order Diffusion Model for Influence Maximization in Social Networks

In social networks, several influential individuals can promote an idea or a product to numerous individuals. Thus, it is valuable to solve the influence maximization (IM) problem, which asks for finding the most influential set of individuals in a social network. To estimate the influence of individuals, the existing independent cascade (IC) model simulates the influence diffusion only considering the influences from direct in-neighbors to nodes. This consideration does not hold in real life. In many cases, people are likely influenced by information depending on where it comes from, instead of who gives it. To simulate the influence diffusion more accurate, this paper proposes the second-order IC model, which takes the previous influence into consideration. In addition, we design an approximate algorithm and its distributed extension for IM under the second-order IC model. Experimental results show that our second-order IC model outperforms the IC model in terms of simulating influence diffusions. The proposed algorithms are efficient, and the obtained node sets are influential.
A Digital LDO Regulator With a Self-Clocking Burst Logic for Ultralow Power Applications

Conventional capless digital low-dropout (DLDO) regulators adopt either a high-speed clock or the burst mode at the expense of a larger quiescent current in order to overcome the degradation of the load transient response caused by the absence of an external capacitor, which causes high power consumption. In this paper, a capless DLDO regulator with a self-clocking burst logic for ultralow power applications is proposed. The self-generated clock in the burst mode of the proposed burst logic is activated temporally in order to achieve both faster load transient response and lower quiescent current. The proposed DLDO regulator is implemented in 14-nm FinFET CMOS technology. The quiescent current and figure-of-merit (FoM) of the proposed DLDO regulator are $0.69~\mu \text{A}$ and 0.097 ps, respectively, with an active area of 0.0035 mm2, excluding a 0.1-nF integrated output capacitor.
Multi-objective Grasshopper Optimization Algorithm based Reconfiguration of Distribution Networks

Network reconfiguration is a nonlinear optimization procedure which calculates a radial structure to optimize the power losses and improve the network reliability index while meeting practical constraints. In this paper, a multi-objective framework is proposed for optimal network reconfiguration with the objective functions of minimization of power losses and improvement of reliability index. The optimization problem is solved by multi-objective grasshopper optimization algorithm (MOGOA) which is one of the most modern heuristic optimization tools. To solve an optimization problem, the suggested algorithm mathematically mimics and formulates the behavior of grasshopper swarms. The modifying comfort zone coefficient needs grasshoppers to balance exploration and exploitation, which helps the MOGOA to find an exact approximation of global optimization and not trapped in local optima. The efficiency of the suggested technique is approved regarding the 33-bus and 69-bus test systems. Optimization results expressed that the suggested technique not only presents the intensified exploration ability but also has a better solution compared with previous algorithms. Keyword: Reconfiguration, Powe loss, Reliability, Multi-objective grasshopper optimization algorithm, Multi-objective optimization.
Exploring MOOC Acceptance and Readiness among Postgraduate Students at a Higher Learning Institution

The pattern of learning in recent years have been impacted by the rise of Massive Open Online Courses (MOOCs). The impact of MOOCs on the field of education have been investigated by many researches. MOOCs, since their inception have attracted a considerable amount of attention within higher education. This study therefore examines the extent of IIUM postgraduate students’ awareness knowledge of MOOCs, how-to knowledge of MOOCs, actual usage of MOOCs, perceived usefulness of MOOCs, attitude towards MOOCs and intention to use MOOCs for academic purposes. The research question concentrated on the extent of the students’ awareness knowledge, how-to knowledge, perceived usefulness, attitude, actual usage, and intention to use MOOCs for academic purposes. One hundred and ninety respondents were involved in this study. The analysis procedure made use of descriptive statistics to find the percentages, frequencies, mean and standard deviation of data. The findings reveal that majority of the respondents do not have awareness knowledge and how-to knowledge of MOOCs. It also shows very few respondents have actual usage of MOOCs. However, majority of the respondents have positive attitude towards MOOCs and majority of them show their intentions to use MOOCs for academic purposes. The study reveals a vital implication on how IIUM ought to scheme policies to increase adequate awareness of MOOCs and more importantly its usefulness to students.
Research of relationship between weight coefficient of product HMM and instantaneous SNR in bimodal speech recognition

In order to better realize speech recognition in complicated noise environment, a new Hidden Markov Model (HMM) was proposed. The relationship between weight coefficient of product HMM and instantaneous Signal Noise Ratio (SNR) was researched and confirmed. In this proposed model, a two-dimension training model was built based on independently trained audio-HMMand visual-HMM, and re-estimation strategy was used to obtain higher recognition accuracy. Generalized Probabilistic Descent (GPD) algorithm was introduced to adjust weight coefficient. The experimental results show that, the proposed bimodal recognition approach with adjusting weight coefficient exhibits good performance on speech recognition in noisy environment.
Introduction To Education Teaching In A Diverse Society

introduction to education teaching in a diverse society is available in our digital library an online access to it is set as public so you can get it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the introduction to education teaching in a diverse society is universally compatible with any devices to read.
Recommendation System for Engineering Students' Specialization Selection Using Predictive Modeling

Educational data mining (EDM) can be used in extracting useful patterns in students’ academic records which may aid in management decision making on determination of engineering students’ specialization track once the general engineering academic requirements were completed. The objective of the research is to provide a specialization selection recommendation for engineering students through application of data mining algorithm and adoption of the rule sets generated by a predictive model. The attributes that may be significant in creating prediction were determined using correlation-based feature selection. Comparative analysis among known algorithms shows that the highest accuracy was considered. A decision tree classification model using WEKA and J48 produced an accuracy value of 80.06. The study revealed that Gender, Algebra, Calculus and physics courses found to have significant effect in predicting the engineering specialization, thus strengthening the general notion that for engineering students to be more successful, the academic performance with the above courses should be highly considered.
Towards engineering the microalga Chlorella sorokiniana for the production of tailored high-value oils

This project explores the production of LC-PUFAs, such as the nutritionally relevant omega-3 fatty acids EPA and DHA, in the freshwater microalga Chlorella sorokiniana (UTEX 1230). C. sorokiniana is of interest due to its rapid growth rate, tolerance to high light and temperature, and ability to accumulate a large proportion of cell weight as lipids. Since wild-type C. sorokiniana terminates fatty acid synthesis at ALA (C18:3n3), a genetic engineering approach is required to produce LC-PUFAs. Changes in FAME and lipid content in different growth conditions including carbon source, nitrogen source, and trophic state were assessed in this microalga. These investigations confirmed the common finding that nitrogen stress is a robust way to induce neutral lipid accumulation, but also highlighted the potential of pH change as an alternate stressor for TAG production. To facilitate the heterologous gene expression needed to increase the range of fatty-acids produced by C. sorokiniana, this work develops and begins to characterise a toolbox of genetic parts for nuclear transformation. This combinatorial parts library, compatible with a standard cloning syntax to enable sharing between groups, comprises five coding sequences, four promoter/5’UTRs and four 3’UTR/terminators. The coding sequences include two antibiotic reporters, two fluorescent reporters and one lipid gene. Among the regulatory sequences, two were novel putative promoters from chlorovirus, as identified from detailed bioinformatics analysis of published transcriptomic and genomic data. An attempt to improve an existing Agrobacterium-mediated transformation strategy was utilised to validate some of the parts within the library. Preliminary evidence suggests the putative chlorovirus promoters may be active within both C. sorokiniana and Chlamydomonas reinhardtii. Challenges with appropriate selectable markers and transformation efficiency highlight the difficulty in genetic engineering of microalgal strains, especially where these tools are not well established, but also showcase the pressing need for such foundational research to be conducted.
PHYTOCHEMICAL AND ANTIBACTERIAL ACTIVITY OF DIVERSE SOLVENT EXTRACT OF LEAF (Plumbagozeylanica).

The present investigation is focused to assess the Phytochemical study and antibacterial activities of the leaf extract of Plumbagozeylanica. The leaf extracts were prepared by using various solvents with an increasing polarity such as hexane, chloroform, ethyl acetate and Methanol. The filtered extract was assessed for its activity against multifarious bacteria such as Staphylococcus aureus, Klebsiellapneumoniae, Bacillus cereus, Salmonella typhi and Pseudomonas aeruginosa by performing well diffusion method. Out of the four solvents trialled, ethyl acetate extract alone showed Zone of inhibition and confirmed significant activity against all the tested pathogens.
Role of the Structure of Graphene Oxide Sheets on the CO2 Adsorption Properties of Nanocomposites Based on Graphene Oxide and Polyaniline or Fe3O4-Nanoparticles

New nanocomposites based on graphene oxide with polyaniline (PANI) or Fe3O4 nanoparticles (MG) were synthesized for CO2 capture. To study the effect of graphene oxide structure on the CO2 retention capacity, CO2/N2 selectivity, adsorbent regeneration, and adsorption kinetics, two different graphene oxides were synthesized by oxidation of graphite flakes (GO) and commercial carbon nanofibers (NSGO). CO2 adsorption isotherms at 300 K were evaluated by gravimetric experiments. The results described the CO2 adsorption as reversible physisorption mechanism, where the CO2 adsorption capacity increases linearly with the micropore volume. Results also demonstrate the good CO2/N2 selectivity, recyclability, and fast adsorption kinetics of the materials tested. Differences concerning the adsorption properties of nanocomposites are related to the chemical composition and the size of graphene oxide sheets.
RF Filter Synthesis Based on Passively Coupled N-Path Resonators

<inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>-path circuits, consisting of an array of switched capacitors driven with non-overlapping clocks, have been used to create radio frequency bandpass and band-stop filters. This paper covers a synthesizing methodology of RF bandpass filters (BPFs) that uses multiple passively coupled <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>-path resonators to realize a more selective transfer function with transmission zeros. Design details and measurement results of a proof-of-concept filter prototype with a 0.8–1.1-GHz tunable bandpass frequency realized in a 65-nm CMOS technology are presented.
Comparison of Unsupervised Learning Methods for Natural Image Processing

For computer vision based appraoches such as image classification (Krizhevsky et al. 2012), object detection (Ren et al. 2015) or pixel-wise weed classification (Milioto et al. 2017) machine learning is used for both feature extraction and processing (e.g. classification or regression). Historically, feature extraction (e.g. PCA; Ch. 12.1. in Bishop 2006) and processing were sequential and independent tasks (Wöber et al. 2013). Since the rise of convolutional neuronal networks (LeCun et al. 1989), a deep machine learning approach optimized for images, in 2012 (Krizhevsky et al. 2012), feature extraction for image analysis became an automated procedure. A convolutional neuronal net uses a deep architecture of artificial neurons (Goodfellow 2016) for both feature extraction and processing. Based on prior information such as image classes and supervised learning procedures, parameters of the neuronal nets are adjusted. This is known as the learning process. Simultaneously, geometric morphometrics (Tibihika et al. 2018, Cadrin and Friedland 1999) are used in biodiversity research for association analysis. Those approaches use deterministic two-dimensional locations on digital images (landmarks; Mitteroecker et al. 2013), where each position corresponds to biologically relevant regions of interest. Since this methodology is based on scientific results and compresses image content into deterministic landmarks, no uncertainty regarding those landmark positions is taken into account, which leads to information loss (Pearl 1988). Both, the reduction of this loss and novel knowledge detection, can be done using machine learning. Supervised learning methods (e.g., neuronal nets or support vector machines (Ch. 5 and 6. in Bishop 2006)) map data on prior information (e.g. labels). This increases the performance of classification or regression but affects the latent representation of the data itself. Unsupervised learning (e.g. latent variable models) uses assumptions concerning data structures to extract latent representations without prior information. Those representations does not have to be useful for data processing such as classification and due to that, the use of supervised and unsupervised machine learning and combinations of both, needs to be chosen carefully, according to the application and data. In this work, we discuss unsupervised learning algorithms in terms of explainability, performance and theoretical restrictions in context of known deep learning restrictions (Marcus 2018, Szegedy et al. 2014, Su et al. 2017). We analyse extracted features based on multiple image datasets and discuss shortcomings and performance for processing (e.g. reconstruction error or complexity measurement (Pincus 1997)) using the principal component analysis (Wöber et al. 2013), independent component analysis (Stone 2004), deep neuronal nets (auto encoders; Ch. 14 in Goodfellow 2016) and Gaussian process latent variable models (Titsias and Lawrence 2010, Lawrence 2005).