Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial cavity drag reduction at high reynolds numbers

Air lubrication to reduce hull skin friction is an idea that originated more than a century ago. There are few implementations of this concept, and there are even fewer systematic investigations at high Reynolds numbers. To address this, an experimental investigation was performed at the W. B. Morgan Large Cavitation Channel that examined the drag-reducing effects of a ventilated partial cavity at high Reynolds numbers. The design was accomplished using both linear gravity wave theory and a two-dimensional inviscid numerical model via Fluent. The physical model was a 12 m long flat plate with a plenum on the bottom. The plenum was formed by an abrupt step near the nose and a long sloping reattachment region toward the rear. Air was injected from the aft face of the step to create a cavity approximately 17.8 cm deep. Friction loads, air flow, and cavity pressure were measured over a range of air fluxes and speeds near the cavity design speed of 3.4 m/s. Cavities were shown to be stable with respect to large changes in air flux and slow perturbations in tunnel speed and pressure. Stable cavities were produced that reduced the skin drag by more than 95% over the extent of the cavity, including the cavity closure.
Image Representations for Ranking and Classification

This thesis concerns the tasks of image re-ranking and image classification. These tasks are solved by learning statistical models given a representation of visual content of the image and a similarity measure between images. Here we aim to improve performance of the tasks by extending the bag-of-words image representation, while using existing statistical models and similarity measures between images. We adapt the image representation according to a given task. First we explore the task of image re-ranking, whose goal is to re-order the images retrieved by a text query such that images relevant to a query are ranked above non-relavant ones. Inspired by text re-ranking methods we developed a query-relative image representation that depends on the visual content of the image, but also on the query used to retrieve it. Next, we adapt the representation for the task of image classification, which aims to assign one or more labels to an image that is related to the content of the image. We have adapted the representation by learning a visual vocabulary specifically for the classification task. We have also introduced a new representation that encodes the information about spatial layout of image parts in much more compact manner than currently used representations that encode the spatial layout. All developed image representations are compact, fast to construct and already perform very good with linear models. We show marked improvements on several stan- dard and challenging datasets with respect to state-of-art-methods. For image classification and image re-ranking tasks we have shown that adapting the representation to the task improves the performance.
First antineutrino energy spectrum from $^{235}$U fissions with the STEREO detector at ILL

This article reports the measurement of the $^{235}$U-induced antineutrino spectrum shape by the STEREO experiment. 43'000 antineutrinos have been detected at about 10 m from the highly enriched core of the ILL reactor during 118 full days equivalent at nominal power. The measured IBD yield spectrum is unfolded to provide a pure $^{235}$U spectrum in antineutrino energy. A careful study of the unfolding procedure, including a cross-validation by an independent framework, has shown that no major biases are introduced by the method. A significant local distortion is found with respect to predictions around $E_\nu \simeq 5.3$ MeV. A gaussian fit of this local excess leads to an amplitude of $A = 12.1 \pm 3.4\%$ (3.5$\sigma$).
Personalized treatment strategies in glioblastoma: MGMT promoter methylation status

: The identification of molecular genetic biomarkers considerably increased our current understanding of glioma genesis, prognostic evaluation, and treatment planning. In glioblastoma, the most malignant intrinsic brain tumor entity in adults, the promoter methylation status of the gene encoding for the repair enzyme O6-methylguanine-DNA methyltransferase ( MGMT) indicates increased efficacy of current standard of care, which is concomitant and adjuvant chemoradiotherapy with the alkylating agent temozolomide. In the elderly, MGMT promoter methylation status has recently been introduced to be a predictive biomarker that can be used for stratification of treatment regimes. This review gives a short summery of epide-miological, clinical, diagnostic, and treatment aspects of patients who are currently diagnosed with glioblastoma. The most important molecular genetic markers and epigenetic alterations in glioblastoma are summarized. Special focus is given to the physiological function of DNA methylation – in particular, of the MGMT gene promoter, its clinical relevance, technical aspects of status assessment, its correlation with MGMT mRNA and protein expressions, and its place within the management cascade of glioblastoma patients.
Persistence-based clustering in riemannian manifolds

We present a clustering scheme that combines a mode-seeking phase with a cluster merging phase in the corresponding density map. While mode detection is done by a standard graph-based hill-climbing scheme, the novelty of our approach resides in its use of topological persistence to guide the merging of clusters. Our algorithm provides additional feedback in the form of a set of points in the plane, called a persistence diagram (PD), which provably reflects the prominences of the modes of the density. In practice, this feedback enables the user to choose relevant parameter values, so that under mild sampling conditions the algorithm will output the correct number of clusters, a notion that can be made formally sound within persistence theory. The algorithm only requires rough estimates of the density at the data points, and knowledge of (approximate) pairwise distances between them. It is therefore applicable in any metric space. Meanwhile, its complexity remains practical: although the size of the input distance matrix may be up to quadratic in the number of data points, a careful implementation only uses a linear amount of memory and takes barely more time to run than to read through the input. In this conference version of the paper we emphasize the experimental aspects of our work, describing the approach, giving an intuitive overview of its theoretical guarantees, discussing the choice of its parameters in practice, and demonstrating its potential in terms of applications through a series of experimental results obtained on synthetic and real-life data sets. Precise statements and proofs of our theoretical claims can be found in the full version of the paper [7].
Optimization of the extraction process of polyphenols from cashew apple agro-industrial residues

The goal of this study was to determine the chemical composition of cashew apples agro-industrial residue and optimize the process of polyphenols extraction in this residue. The extraction process conditions were defined using a 24-1 fractional factorial experimental design using acetone and methanol as solvents. The independent variables were: time (30 to 90 min), temperature (30 to 50 °C), solvent concentrations (50% to 90%), agitation speed (100 to 300 rpm); the dependent variables were: total phenolic content and DPPH scavenging capacity. The optimized process was carried out by applying the Central Composite Rotational Design (CCRD) considering the results obtained with the 24-1 fractional factorial experimental design. The residue presented bioactive compounds in its composition, with emphasis on the content of total phenolic compounds (1975.64 mg/ 100 g). The extraction process was not affected by methanol; however, acetone affected the amounts of extracted phytochemicals. Extracts with high levels of polyphenols and strong DPPH scavenging capacity (> 80%) were obtained using 55% acetone, 30 minutes, 30 °C, and 150 rpm. The results showed that cashew apple residue is a potential natural source of bioactive compounds with strong antioxidant capacity. These compounds could be used partially or totally to replace synthetic antioxidants.
Behavioral Ethics: Toward a Deeper Understanding of Moral Judgment and Dishonesty

Early research and teaching on ethics focused on either a moral development perspective or philosophical approaches, and used a normative approach by focusing on the question of how people should act when resolving ethical dilemmas. In this paper, we briefly describe the traditional approach to ethics and then present a (biased) review on the behavioral approach to ethics. We define behavioral ethics as the study of systematic and predictable ways in which individuals make ethical decisions and judge the ethical decisions of others that are at odds with intuition and the benefits of the broader society. By focusing on a descriptive rather than a normative approach to ethics, behavioral ethics is better suited than traditional approaches to address the increasing demand from society for a deeper understanding of what causes even good people to cross ethical boundaries.
CHS-Net: A Deep learning approach for hierarchical segmentation of COVID-19 infected CT images

The pandemic of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as computed tomography (CT), X-ray, etc., plays a significant role in diagnosing the patients by presenting the visual representation of the functioning of the organs. However, for any radiologist analyzing such scans is a tedious and time-consuming task. The emerging deep learning technologies have displayed its strength in analyzing such scans to aid in the faster diagnosis of the diseases and viruses such as COVID-19. In the present article, an automated deep learning based model, COVID-19 hierarchical segmentation network (CHS-Net) is proposed that functions as a semantic hierarchical segmenter to identify the COVID-19 infected regions from lungs contour via CT medical imaging. The CHS-Net is developed with the two cascaded residual attention inception U-Net (RAIU-Net) models where first generates lungs contour maps and second generates COVID-19 infected regions. RAIU-Net comprises of a residual inception U-Net model with spectral spatial and depth attention network (SSD), consisting of contraction and expansion phases of depthwise separable convolutions and hybrid pooling (max and spectral pooling) to efficiently encode and decode the semantic and varying resolution information. The CHS-Net is trained with the segmentation loss function that is the weighted average of binary cross entropy loss and dice loss to penalize false negative and false positive predictions. The approach is compared with the recently proposed research works on the basis of standard metrics, it is observed that the proposed approach outperformed the recently proposed approaches and effectively segments the COVID-19 infected regions in the lungs.
Maintaining Smart Contracts on Ethereum: Issues, Techniques, and Future Challenges

Smart contracts are self-executed programs that run on a blockchain. They cannot be modified once deployed and hence they bring unique maintenance challenges compared to conventional software. This study focuses on the key novel maintenance issues related to smart contracts on Ethereum, and aims to answer (i) What kinds of issues will smart contract developers encounter for corrective, adaptive, perfective, and preventive maintenance? (ii) What are the current maintenance methods used for smart contracts? and (iii) What should we do in the future to increase the maintainability of smart contracts? To obtain the answers to these research questions, we first conducted a systematic literature review to analyze 131 smart contract related research papers published from 2014 to 2020. Since the Ethereum ecosystem is fast-growing some results from previous publications might be out-of-date and there may be a practice gap between academia and industry. To address this we performed an online survey of smart contract developers on Github to validate our findings and we received 165 useful responses. Based on the survey feedback and literature review, we present the first empirical study on smart contract maintenance. Our study can help smart contract developers better maintain their smart contract-based projects, and we highlight some key future research directions to improve the Ethereum ecosystem.
A survey of joint intent detection and slot-filling models in natural language understanding

Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks have been deemed to proceed independently. However, more recently, joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. This article is a compilation of past work in natural language understanding, especially joint intent classification and slot filling. We observe three milestones in this research so far: Intent detection to identify the speaker's intention, slot filling to label each word token in the speech/text, and finally, joint intent classification and slot filling tasks. In this article, we describe trends, approaches, issues, data sets, evaluation metrics in intent classification and slot filling. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. To interpret the state-of-the-art trends, we provide multiple tables that describe and summarise past research along different dimensions, including the types of features, base approaches, and dataset domain used.
Resistive MHD Simulations of Laminar Round Jets with Application to Magnetic Nozzle Flows

Resistive MHD Simulations of Laminar Round Jets with Application to Magnetic Nozzle Flows. (December 2011) Daniel Borsodi Araya, B.S. Texas A&M University Chair of Advisory Committee: Dr. Sharath Girimaji This thesis investigates fundamental flows of resistive magnetohydrodynamics (MHD) by a new numerical tool based on the gas-kinetic method. The motivation for this work stems from the need to analyze the mechanisms of plasma detachment in the exhaust plume of the magnetoplasma rocket known as VASIMR R ©. This rocket has great potential for reducing the travel time for deep space exploration missions. However, it is very difficult to investigate detachment in ground-based experiments because this large-scale device can fully function only in a vacuum. This difficulty makes computational analysis and modeling an important part of the design and testing process. A parallelized Boltzmann-BGK continuum flow solver is expanded to include resistive MHD physics. This new code is validated against known solutions to MHD channel flows and new results are presented for simulations of a laminar round jet subject to a constant applied magnetic field as well as the diverging magnetic field of a current loop. Additionally, a parametric map is presented that outlines appropriate conditions required when using a fluid model for magnetic nozzle flows. The work of this thesis serves as an introductory step to developing a robust numerical flow solver capable of simulating magnetic nozzle flows and other plasmas that cannot be easily replicated in ground facilities.
Emotions at work : theory, research and applications in management

About the Editors. List of Contributors. Preface. PART I: THE NATURE OF EMOTION. Varieties and Functions of Human Emotion, (Robb Stanley and Graham Burrows). Emotion, Mood and Temperament: Similarities, Differences and a Synthesis (Elizabeth Gray and David Watson). Discrete Emotions in Organizational Life (Richard Lazarus and Yochi Cohen-Charash). PART II: MEASURING AND ASSESSING EMOTION AT WORK. Emotions in the Workplace: Biological Correlates (Maurice King). Measuring Emotions at Work (Roy Payne). PART III: ORGANIZATIONAL INFLUENCES ON EMOTION. Affect at Work: An Historical Perspective (Howard Weiss and Art Brief). Culture as a Source, Expression and Reinforcer of Emotions in Organizations (Janice Beyer and David Nino). Origins and Consequences of Emotions in Organizational Teams (Carsten de Dreu, Michael West, Agneta Fischer, and Sarah McCurtin ). Emotions and Organizational Control (Stephen Fineman ). Helping Individuals Manage Emotional Responses (Rose Evison ). Organizational Management of Stress and Destructive Emotions at Work (Cary Cooper and Sue Cartwright ). Emotion and Offices at Work (Ian Donald ). PART V: EMOTIONS AND THE FUTURE. Future Work and its Emotional Implications (Peter Herriot ). Inner Technology: Emotions in the New Millenium (Ayman Sawaf, Harold Bloomfield, and Jared Rosen ). Conclusion: (Roy L. Payne and Cary L. Cooper). Index.
EFSA Panel on Biological Hazards (BIOHAZ); Scientific Opinion on the evaluation of the safety and efficacy of ListexTM P100 for the removal of Listeria monocytogenes surface contamination of raw fish

variants. Pilot and industrial scale studies should consider parameters affecting decontaminating efficacy, and should verify that application on raw fish has an impact on reduction of L. monocytogenes contamination on the final product. The persistence or activity of P100 as well as potential changes in L. monocytogenes counts should be evaluated during fish storage. Tests to investigate potential development of resistance or reduced susceptibility to biocides and key therapeutic antimicrobials, following use of Listex TM P100, are recommended. The continuous effectiveness of Listex TM P100 against L. monocytogenes and the potential for selection and dominance of strains naturally-resistant to P100 should be monitored.
EFSA Panel on Biological Hazards (BIOHAZ); Scientific Opinion on Scientific Opinion on risk based control of biogenic amine formation in fermented foods

A qualitative risk assessment of biogenic amines (BA) in fermented foods was conducted, using data from the scientific literature, as well as from European Union-related surveys, reports and consumption data. Histamine and tyramine are considered as the most toxic and food safety relevant, and fermented foods are of particular BA concern due to associated intensive microbial activity and potential for BA formation. Based on mean content in foods and consumer exposure data, fermented food categories were ranked in respect to histamine and tyramine, but presently available information was insufficient to conduct quantitative risk assessment of BA, individually and in combination(s). Regarding BA risk mitigation options, particularly relevant are hygienic measures to minimize the occurrence of BA-producing microorganisms in raw material, additional microbial controls and use of BAnonproducing starter cultures. Based on limited published information, no adverse health effects were observed after exposure to following BA levels in food (per person per meal): a) 50 mg histamine for healthy individuals, but below detectable limits for those with histamine intolerance; b) 600 mg tyramine for healthy individuals not taking monoamino oxidase inhibitor (MAOI) drugs, but 50 mg for those taking third generation MAOI drugs or 6 mg for those taking classical MAOI drugs; and c) for putrescine and cadaverine, the information was insufficient in that respect. Presently, only high-performance liquid chromatography (HPLC)-based methods enable simultaneous and high sensitivity quantification of all BA in foods, hence are best suited for monitoring and control purposes. Monitoring of BA concentrations in fermented foods during the production process and along the food chain would be beneficial for controls and further knowledge. Further research on BA in fermented foods is needed; particularly on toxicity and associated concentrations, production process-based control measures, further process hygiene and/or food safety criteria development, and validation of analysis methods. © European Food Safety Authority, 2011
A comprehensive model of Penning energy transfers in Ar − CO2 mixtures

Ionizing energy transfer mechanisms due to the excited argon atoms, called Penning transfers, have been investigated for various Ar − CO2 mixtures at 0.4, 0.8, 1.2 and 1.8 atm gas pressures. The Penning energy transfer probabilities are extracted from the systematic gas gain measurements carried out in cylindrical proportional counters. In this report, contributions of the several transfer processes are identified by studying the pressure and mixing proportion dependence of the transfer rates with a model.
Anuário do Instituto de Geociências

Urbanization brought a lot of pollution-related issues that are mitigable by the presence of urban vegetation. Therefore, it is necessary to map vegetation in urban areas, to assist the planning and implementation of public policies. As a technology presented in the last decades, the so-called Terrestrial Mobile Mapping Systems TMMS, are capable of providing cost and time effective data acquisition, they are composed primarily by a Navigation System and an Imaging System, both mounted on a rigid platform, attachable to the top of a ground vehicle. In this context, it is proposed the creation of a low-cost TMMS, which has the feature of imaging in the near-infrared (NIR) where the vegetation is highly discriminable. After the image acquisition step, it becomes necessary for the semantic segmentation of vegetation and non-vegetation. The current state of the art algorithms in semantic segmentation scope are the Convolutional Neural Networks CNNs. In this study, CNNs were trained and tested, reaching a mean value of 83% for the Intersection Over Union (IoU) indicator. From the results obtained, which demonstrated good performance for the trained neural network, it is possible to conclude that the developed TMMS is suitable to capture data regarding urban vegetation.
I–PD controller for integrating plus time-delay processes

An integral-proportional derivative (I-PD) control strategy for integrating plus time-delay processes is proposed. The proposed scheme consists of an inner PD loop and an outer I-loop for taking care of the servo as well as the regulatory action. Explicit formulas for tuning of the proposed controller are derived in terms of gain margin, phase margin and critical gain. Moreover, pole-placement and frequency loop-shaping design methods are also explored for designing the I-PD controller. Simulation results show the superiority of the proposed I-PD controller over conventional P/PI/PID ones for integrating processes. The design is also validated through experiments on a temperature control process.