Miracle-dz/UniFAR-FTUs huggingface dataset:
DYNAMIC BEHAVIOR COMPARISON OF AN ACTIVE MAGNETIC BEARING USING SLIDING MODE WITH VOLTAGE AND CURRENT CONTROL

Magnetic bearing is a product based on magnetic levitation and has been researched for more than forty years. At first, their function is the same of mechanical bearings, but it acts with no contact, using field forces provided by electromagnets to keep rotor’s position. Its advantages over other types of bearings are several, like the possibility of reaching incredibly high rotational speed, vibration control, high efficiency, low costs of maintenance, no contamination of fluids in turbo-machinery, and others. This high performance is only possible thanks to the improvement of control instrumentation and theory, which is the base behind the active magnetic bearing (AMB). Since then, several control laws are being implemented to improve the stability of the rotor dynamics. The objective of this paper is to use the Sliding Mode Control (SMC) technique within an experimental model of a rotor supported by two AMBs. The SMC is based on making the space-state function "slide" over a straight line, changing the behavior of the system dynamics. This is a robust technique that allows good results even if there is some uncertainty or variation of physical parameters. This uncertainty can be in rotor characteristics or in the model itself, due to the hypotheses made. Considering this system being highly non-linear and with low fault tolerance, a robust control law brings many advantages and makes easier to achieve good results when dealing with a real prototype. However, the model relate the magnetic force linearly with the input current, which should be the variable of control, but current control involves a more complex electronic implementation that is harder to project in a system with inductive load. A voltage control simplify the control signal amplification, but raise the order of the system, making it more unstable, since the inductance of the AMB coils tends to oppose the current change. Thus, a voltage control requires an accurate control and a comparison with current control using SMC in both cases is tested to determine the advantages and disadvantages of each. Finally, some results will be shown for a experimental prototype with simplistic electronic project, using PWM signal for current control.
MULTI-MODAL STUDY OF THE EFFECT OF INFORMATION COMPLEXITY IN A CRISIS MANAGEMENT GAME

In this paper, we study the eﬀect of information complexity on player in-game behaviour and physiological responses during a dilemma-based crisis management game. We run a user study, where players attempt to solve a crisis scenario while their in-game and physiological activity is being monitored through game logs and wearable physiological sensors. Results show that information complexity has noticeable eﬀects on players’ decision making and physiological responses, while moderate correlation was found between speciﬁc in-game-and physiology-based behavioural features. This study is focused on exploring behavioural patterns correlated to various levels of information complexity. Our ﬁndings can be applied in future studies aiming at designing per-sonalised crisis management training scenarios.
Presence of Sugarcane yellow leaf virus (SCYLV) in sugarcane (Saccharum spp.) isolated from Colima

Sugarcane is one of the important industrial crops worldwide and is affected by various viral diseases, including the Sugarcane yellow leaf virus (SCYLV). In Mexico, this virus has been detected based on symptomatology and °Brix; however, the diagnosis based on these parameters is not conclusive. The objective of the study was to detect the presence and distribution of SCYLV in sugarcane areas of western Mexico (Colima, Jalisco and Nayarit) and to determine the phylogenetic origin of a Colima isolate. The work was developed in the Tecoman Experimental Field during 2013-2014. Total RNA was isolated from collected leaves and RT-PCR was performed with oligonucleotides specific for SCYLV. A total of 233 samples were analyzed and the incidence of SCYLV carrier plants was 14.6%, affecting the hybrids CP 72-2086, Mex 69-290 and Atemex 96-40. The BLAST analysis showed that the partial CP sequence of Colmex-317 (512 bp) is homologous with sequences from Brazil, China, India, Kenya and USA and also shares identity percentages higher than 99% with other sequences from several countries. The phylogenetic analysis of Colmex-317 with partial sequences and complete genomes of SCYLV isolated from different parts of the world revealed that the Mexican isolate belongs to the Brazilian genotype (BRA) and was grouped with sequences from Brazil, China, Kenya and South Africa. However, it is necessary to evaluate a larger number of isolates and longer sequences to determine if the BRA genotype is the only one present in Mexico.
Economic complexity and growth*

An increasing literature highlights the importance of economic complexity in the development process. Recent contributions propose ways to measure economic complexity, by considering what economies export. We improve the proposed measures by constructing the indicators on data featuring greater product disaggregation and a larger number of countries. We apply this measure to evaluate the correlation between complexity and economies’ current income and future growth. Complexity is robustly correlated to countries’ income and can predict growth for a large list of countries. Since complexity indicators are fed with information of each country relative to the others, the inclusion of economies for which export figures hardly reflect true complexity creates a noise that reduces the indicators’ correlation with growth for the entire sample. Finally, we show that this correlation remains significant when many of the usual determinants are included in growth regressions.
Relative Informational Efficiency and Predictability in the Corporate Bond Market

I examine the relative informational efficiency of corporate bonds and the underlying stocks through the lead-lag relation between their daily returns. I find that the daily stock returns lead the daily bond returns for all credit rating categories but the safest (Aaa) and least safe (Ca-D) bond portfolios. These findings indicate that the stock market is more efficient than the bond market when incorporating new information. The bond market’s relative informational inefficiency implies that there are trading opportunities for the bonds that are highly sensitive to the release of common information. I also find that the stock market detects impending defaults earlier than the bond market, which implies that bond holders have enough time to protect their capital.
FORMATION AND EVAPORATION OF REGULAR BLACK HOLES

Black holes, predicted by Einstein gravity, appear to exist in the universe. Singularities were also predicted to form inside them by singularity theorems [Penrose 1965, Hawking 1967, Penrose & Hawking 1970. ]. However, they are generally regarded as indicating the breakdown of the classical theory. Quantum field theory on a stationary black-hole background predicted Hawking radiation [Hawking 1975]. The ingoing radiation has negative energy flux which contradicts the assumptions of the theorems and, in a semi-classical approximation, causes the black hole to shrink. In the usual picture, the black hole shrinks until the singularity is reached [Hawking 1976], leading to the information puzzle. However, if the singularity does not exist, such a picture cannot be correct.
Graduate Prediction Using Ontology Approach

—The paper seeks to predict the completion of graduation of students studying in a University. The filtration of the redundant features will be done by Ranker method. The use of SVM method will also be taken into consideration for filtration of attributes. The decision tree algorithms C4.5/J48 and ID3 are used for generating the decision tree which will serve as a basis for the generation of an ontology. Moreover, the classification rules are extended by applying a semantic based approach for creating classification tree ontology. The ontology represents the classification rules that are used to enable machines to interpret and identify learner factors in process of prediction. The explanation of how ontological representation plays a role in classifying students to predictive target class is done. Semantic Web Rule Language is used for the specification of classification rules.
Sorption of Cu (II) Ions by Adsorption using Orange Peel

The purpose of the present work is to find out feasibility of the process to remove copper ions from the aqueous solution using powdered orange peel as an adsorbent. Powered activated carbon was also used along with pretreated orange peel to compare the efficiencies. Adsorption is taken into consideration to be one of the most excellent technologies broadly used in international environmental safety regions. Batch adsorption study was conducted to find out the impact related to the numerous strictures like pH, contact period, adsorbent dosage, concentration of preliminary metal Cu(II) ions besides the size of the adsorbent particle on the removal of copper ions from synthetic solutions. The statistics related to the adsorption isotherm which was obtained by carrying out the experiments, performing modeling of such data is very important so that forecasting of adsorption besides knowhow of adsorption. Experimental statistics were investigated with the use of numerous models related to the adsorption and that includes BET model, Freundlich model, Langmuir model as well as Modified Freundlich isotherms model. Equilibrium facts are also evaluated with the use of above-mentioned models related to the process of adsorption.
Auto-Off ID: Automatic Detection of Offensive Language in Social Media

As the popularity of social media grows, computer-mediated anonymity allows users to engage in activities that they would not do in real life. This makes users vulnerable to abuse through Internet platforms. Due to the enormous number of social media data, it is not possible to manually filter out the overflow of abusive content in online communities and social networking sites. The research work proposes a multi-level classification model that deploys various machine and deep learning models to effectively identify offensive content in a tweet. The proposed Auto-Off ID system is designed to build a system that classifies tweets as offensive or non-offensive; filters out and classifies offensive tweets as either targeted or non-targeted; filters out targeted tweets and identify mentions of individuals and organizations who have been bullied. The study is supported by the text analysis features with lexicon features using LIWC, POS tags for primary and secondary users, Twitter Tag Scores (TTS). This system is evaluated using a diverse choice of machine learning and deep learning models from which it is proved that C-LSTM outperform with an accuracy of 91.72% for offensive language identification; LDA + Logistic Regression training with SVM accuracy of 90.87% for offensive tweet classification.
Shifts in hydroclimatology of US megaregions in response to climate change

Most of the population and economic growth in the United States occurs in megaregions as the clustered metropolitan areas, whereas climate change may amplify negative impacts on water and natural resources. This study assesses shifts in regional hydroclimatology of fourteen US megaregions in response to climate change over the 21st century. Hydroclimatic projections were simulated using the Variable Infiltration Capacity (VIC) model driven by three downscaled climate models from the Multivariate Adaptive Constructed Analogs (MACA) dataset to cover driest to wettest future conditions in the conterminous United States (CONUS). Shifts in the regional hydroclimatolgy and basin characteristics of US megaregions were represented as a combination of changes in the aridity and evaporative indices using the Budyko framework and Fu’s equation. Changes in the climate types of US megaregions were estimated using the Fine Gaussian Support Vector Machine (SVM) method. The results indicate that Los Angeles, San Diego, and San Francisco are more likely to experience less arid conditions with some shifts from Continental to Temperate climate type while the hydroclimatology of Houston may become drier with some shifts from Temperate to Continental climate type. Additionally, water yield is likely to decrease in Seattle. Change in the hydroclimatology of Denver and Phoenix highly depends on the selected climate model. However, the basin characteristics of Phoenix have the highest sensitivity to climate change. Overall, the hydroclimatic conditions of Los Angeles, San Diego, Phoenix, Denver, and Houston have the highest sensitivity to climate change. Understanding of future shifts in hydroclimatology of megaregions can help decision-makers to attenuate negative consequences by implementing appropriate adaptation strategies, particularly in the water-scare megaregions.
Prediction of CO2 solubility in aqueous solutions of N, N-Diethyl ethanolamine (DEEA): A modified Kent-Eisenberg model approach

A tremendous increase in the carbon dioxide emissions over the last few decades has caused serious environmental concern. The research fraternity is working towards the discovery of novel solvents for carbon dioxide with greater removal efficiency. N, N-Diethyl ethanolamine (DEEA), a tertiary amine which can be prepared from renewable resources, is a potential absorbent for carbon dioxide removal. Determination of the equilibrium characteristics of the system plays a key role in the designing of industrial plants. In this work, the equilibrium solubility of CO2 in aqueous solutions of DEEA at different temperatures and CO2 partial pressures has been modeled using the modified Kent-Eisenberg model. The model predicted values were found to be in good agreement with the reported experimental data.
Adsorption of Metal Ion Manganese (Mn) in Sodium Silicate Solution using Chitosan

High purity Silica precipitate is very potential materials which can be utilized for many applications for high grade silicon, filler, photovoltaic, catalytic chemical, etc. Indonesia has abundant natural resources including quartz sand as raw materials for making high purity silica precipitate. Silica precipitate is made by roasting quartz sand forming sodium silicate solution. In order to obtain high purity silica precipitate, purification of sodium silicate solution is needed. In this study, purification of sodium silicate solution is performed by adsorption process of metal ion manganese (Mn) using chitosan as adsorbent. The experiment was done using batch technique. The variation of adsorption process was performed to get optimum conditions such as sample concentration, adsorbent mass, contact time, pH and temperature. The adsorption of metal ions of Mn was analyzed by atomic adsorption spectroscopy. The result of experiment shows that the best mass of chitosan for Mn adsorption process is 0.2 grams which gives percentage adsorption of 56.63%. The optimum contact time is 30 minutes, it results percentage adsorption of 52.5%. The optimum pH 3 gives adsorption percentage of 58.67%. And the best temperature for adsorption is at 60 °C with the percentage adsorption of 65.72%. Adsorption mechanism of metal ion Mn follow the isotherm model of langmuir and freundlich with the linearity value of R2 = 0.99 and 0.98 respectively.
Auto Block IoT: A Forensics Framework for Connected Vehicles

The increase in the number of vehicles has led to the increase in number of accidents. The rise in connected vehicles and autonomous vehicles will increase the level of forensics and investigations required in case of an accident. A hindrance to the investigation is the reliability of data. This paper proposes a blockchain framework to ensure that the vehicular data at the time of accident is stored in a tamper-proof and reliable way for further analysis by various stakeholders. A test bed using IoT, IPFS and Etherum test network is proposed and evaluated to study the feasibility of the proposed approach.
An Efficient Approach to Microarray Data Classification using Elastic Net Feature Selection, SVM and RF

DNA Microarray technology forms an integral part of the bioinformatics world and provides researchers with ability to monitor a large amount of gene expressions simultaneously. The analysis of this data proves extremely beneficial in the detection of several diseases. Classification of the gene expression data obtained from microarrays is an imperative step in providing further information on the types of possible diseases present. Several classification models for microarray data have been proposed, yielding considerable results in terms of accuracy and execution time. This paper demonstrates the implementation of a microarray data classification system using Elastic Net for feature selection on two classification mechanisms. The classification of the data has been carried out using SVM and Random Forest Classifier methods. The implemented system using Elastic Net and SVM exhibits a much better performance in terms of accuracy and execution time than most of the existing systems.
Acceleration of Web Interface Generation Through Voice Commands

Advancement in technology is useless unless it is accompanied by a greater access to technology. Be it lack of technical knowledge or a dearth of ability to comprehend or reason, technology must allow low-code or no-code access to all. This work tries to bridge this accessibility barrier by bringing Natural Language Processing (NLP) to the website development process. using NLP and Azure Cognitive Services, we will be able to understand what elements the user wants to add to the website by extracting the required information. Dynamic generation of web pages using voice commands from the user will not only increase low-code accessibility to naive users, but will also speed up work for experienced users by allowing them to easily add elements to the website.
Comparative Analysis of Machine Learning Methods to Detect Chronic Kidney Disease

Chronic Kidney disease (CKD) is a lifelong health hazard that can cause the failure of kidneys. Symptoms of this develop slowly and are not obvious. Early detection of Chronic Kidney Disease can lead to significant progress in finding the cure for this disease. Through this study, we aim to employ ML techniques for the prediction and diagnosis of Chronic Kidney Disease. The findings obtained from our predictive analysis combined with the expertise of healthcare professionals can help in making an accurate prognosis. For this, we have used a dataset containing data from 400 individuals acquired from the University of California Irvine (UCI) repository. Various feature selection techniques have been used to optimize the number of features affecting Chronic Kidney Disease. Subsequently, these desirable features are chosen and used in different ML models and their accuracy, sensitivity is compared. Multiple Machine learning algorithms have been explored such as Logistic Regression, Naïve Bayes, KNN, SVM, Decision Trees, Random Forest Classifier, and Extra Trees Classifier. It was concluded that Decision Trees using information gain gave six optimal features and the Extra Trees Classifier model gives the best accuracy of 99.36 % with Extra Trees Classifier having one of the least execution times.
Extraction of High-level and Low-level feature for classification of Image using Ridgelet and CNN based Image Classification

Remote Sensing image classification is an important research area for the recent time, because of its various application areas. Among the many available feature extraction methods, this paper uses the ridgelet based feature extraction method and those obtained features are combined with deep features obtained from the Convolutional Neural Network (CNN). Here, the Ridgelet’s are used to obtain the low-level features and CNN is used to obtain high-level feature. The system tries to construct the ridgelet filter for obtaining the low-level feature. The multi-resolution CNN is introduced using the concept of fusing high-level and low-level features via ridgelet and CNNs. Then, fused features are then classified and the output classified image is obtained. Experimental verifications are conducted on NWPU-RESISC45 dataset and the output results are provided to prove the best classification accuracies compared with the other proposed systems.
Formation of functional selectin ligands on activated T cells and thymic progenitors : the role of core 2 β1,6-N-acetylglucosaminyltransferases in the control of lymphocyte trafficking and thymic progenitor homing

The core 2 p l^-A'-acetylglucosaminyltransferase (C2GlcNAcT) family of enzymes (C2GlcNAcT-I,-II,-III) synthesize branched O-glycans. A significant body of work highlights the importance of C2GlcNAcT-I in controlling selectin-ligand-mediated cell trafficking while little is known about the role of the two other C2GlcNAcT isoenzymes. 1) The first objective of this thesis is to determine in vitro and in vivo T cell stimulation conditions that guide P-selectin ligand expression in absence of C2GlcNAcT-I. Mitogen stimulation of splenocytes maintained under very-high-density culture conditions uncovers C2GlcNAcT-I-independent P-selectin ligand formation in CD8 + T cells, but not CD4 + T cells. CD8 + T cells of C2GlcNAcT-I u 1 1 mice also roll under shear flow on immobilized Pselectin in a PSGL-l(P-selectin-glycoprotein-ligandl)-specific manner. Using RT-PCR analysis, we identify C2GlcNAcT-III as the likely source of core 2 activity. Up regulation of P-selectin ligand in C2GlcNAcT-F 1 1 CD8 + T cells correlates with higher core 2 enzyme activity, as measured by a standard enzymatic assay and cell-surface binding of the core 2sensitive mAb 1B11. This reveals the well-established C2GlcNAcT-I substrates CD43 and CD45 as additional physiological targets of C2GlcNAcT-III. Adoptive transfer of C2GlcNAcT-I u 1 1 T cells from mice transgenic for the male antigen (HY) T cell receptor shows that C2GlcNAcT-I-independent P-selectin ligand formation occurs on CD8 + T cells under in vivo stimulation conditions. In consequence, C2GlcNAcT-III emerges as a contributor to P-selectin ligand formation that may co-operate with C2GlcNAcT-I to control CD8 + T cell trafficking. 2) A second objective of this thesis is to explore whether C2GlcNAcT-I plays a role in controlling early T cell progenitor migration to the thymus. PSGL-1 is expressed by these thymic progenitors. In order for PSGL-1 to be recognized by P-selectin which is constitutively expressed at low levels on the thymic endothelium, it must be modified by C2GlcNAcT-I. Using mice with deficiencies in PSGL-1, C2GlcNAcT-I and P-selectin, we employ parabiosis and competitive repopulation to show that C2GlcNAcT-I modification of PSGL-1 expressed on thymic progenitors is a functionally imperative component of the thymic homing process.
Short-term impacts of 2017 western North American wildfires on meteorology, the atmosphere’s energy budget, and premature mortality

Western North American fires have been increasing in magnitude and severity over the last few decades. The complex coupling of fires with the atmospheric energy budget and meteorology creates short-term feedbacks on regional weather altering the amount of pollution to which Americans are exposed. Using a combination of model simulations and observations, this study shows that the severe fires in the summer of 2017 increased atmospheric aerosol concentrations leading to a cooling of the air at the surface, reductions in sensible heat fluxes, and a lowering of the planetary boundary layer height over land. This combination of lower-boundary layer height and increased aerosol pollution from the fires reduces air quality. We estimate that from start of August to end of October 2017, ∼400 premature deaths occurred within the western US as a result of short-term exposure to elevated PM2.5 from fire smoke. As North America confronts a warming climate with more fires the short-term climate and pollution impacts of increased fire activity should be assessed within policy aimed to minimize impacts of climate change on society.