Showing papers by "Instituto Superior Técnico published in 2021"

Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog

Abstract: We report on the population of 47 compact binary mergers detected with a false-alarm rate of 0.01 are dynamically assembled. Third, we estimate merger rates, finding RBBH = 23.9-+8.614.3 Gpc-3 yr-1 for BBHs and RBNS = 320-+240490 Gpc-3 yr-1 for binary neutron stars. We find that the BBH rate likely increases with redshift (85% credibility) but not faster than the star formation rate (86% credibility). Additionally, we examine recent exceptional events in the context of our population models, finding that the asymmetric masses of GW190412 and the high component masses of GW190521 are consistent with our models, but the low secondary mass of GW190814 makes it an outlier.

Review on ammonia as a potential fuel: from synthesis to economics

Abstract: Ammonia, a molecule that is gaining more interest as a fueling vector, has been considered as a candidate to power transport, produce energy, and support heating applications for decades. However, the particular characteristics of the molecule always made it a chemical with low, if any, benefit once compared to conventional fossil fuels. Still, the current need to decarbonize our economy makes the search of new methods crucial to use chemicals, such as ammonia, that can be produced and employed without incurring in the emission of carbon oxides. Therefore, current efforts in this field are leading scientists, industries, and governments to seriously invest efforts in the development of holistic solutions capable of making ammonia a viable fuel for the transition toward a clean future. On that basis, this review has approached the subject gathering inputs from scientists actively working on the topic. The review starts from the importance of ammonia as an energy vector, moving through all of the steps in the production, distribution, utilization, safety, legal considerations, and economic aspects of the use of such a molecule to support the future energy mix. Fundamentals of combustion and practical cases for the recovery of energy of ammonia are also addressed, thus providing a complete view of what potentially could become a vector of crucial importance to the mitigation of carbon emissions. Different from other works, this review seeks to provide a holistic perspective of ammonia as a chemical that presents benefits and constraints for storing energy from sustainable sources. State-of-the-art knowledge provided by academics actively engaged with the topic at various fronts also enables a clear vision of the progress in each of the branches of ammonia as an energy carrier. Further, the fundamental boundaries of the use of the molecule are expanded to real technical issues for all potential technologies capable of using it for energy purposes, legal barriers that will be faced to achieve its deployment, safety and environmental considerations that impose a critical aspect for acceptance and wellbeing, and economic implications for the use of ammonia across all aspects approached for the production and implementation of this chemical as a fueling source. Herein, this work sets the principles, research, practicalities, and future views of a transition toward a future where ammonia will be a major energy player.

Ecological impacts of run-of-river hydropower plants—Current status and future prospects on the brink of energy transition

Abstract: The general perception of small run-of-river hydropower plants as renewable energy sources with little or no environmental impacts has led to a global proliferation of this hydropower technology. However, such hydropower schemes may alter the natural flow regime and impair the fluvial ecosystem at different trophic levels. This paper presents a global-scale analysis of the major ecological impacts of three main small run-of-river hydropower types: dam-toe, diversion weir, and pondage schemes. This review's main objective is to provide an extensive overview of how changing the natural flow regime due to hydropower operation may affect various aspects of the fluvial ecosystem. Ultimately, it will inform decision-makers in water resources and ecosystem conservation for better planning and management. This review analyses data on ecological impacts from 33 countries in five regions, considering the last forty years' most relevant publications, a total of 146 peer-reviewed publications. The analysis was focused on impacts in biota, water quality, hydrologic alteration, and geomorphology. The results show, notably, the diversion weir and the pondage hydropower schemes are less eco-friendly; the opposite was concluded for the dam-toe hydropower scheme. Although there was conflicting information from different countries and sources, the most common impacts are: water depletion downstream of the diversion, water quality deterioration, loss of longitudinal connectivity, habitat degradation, and simplification of the biota community composition. A set of potential non-structural and structural mitigation measures was recommended to mitigate several ecological impacts such as connectivity loss, fish injuries, and aquatic habitat degradation. Among mitigation measures, environmental flows are fundamental for fluvial ecosystem conservation. The main research gaps and some of the pressing future research needs were highlighted, as well. Finally, interdisciplinary research progress involving different stakeholders is crucial to harmonize conflicting interests and enable the sustainable development of small run-of-river hydropower plants.

Flood susceptibility modelling using advanced ensemble machine learning models

Abstract: Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

Green roof and green wall benefits and costs: A review of the quantitative evidence

Abstract: Greening the urban environment can be an important strategy to tackle the problems of urban densification and meet the United Nations Sustainable Development Goals. Green infrastructures, like green roofs and green walls, have multiple associated environmental, social and economic benefits that improve buildings performance and the urban environment. Yet, the implementation of green roofs and green walls is still limited, as these systems often have additional costs when compared to conventional solutions. Recent studies have been comparing these greening systems to other solutions, balancing the long-term benefits and costs. Also, there is significant research on green roofs and green walls benefits. Although, green roofs and green walls economic analyses don't include all benefits due to measuring difficulties. The associated uncertainty regarding the quantification of the benefit makes it difficult to compare the research outcomes. This paper aims to provide a research review of existing benefits and costs of different types of green roofs and green walls. These were divided between building scale benefits, urban scale benefits and life cycle costs, focusing on the identification of results variability and assessment of their average quantification. The analysis shows that in general, there are few data regarding intangible benefits, as the promotion of quality of life and well-being. Also, there are still few studies quantifying green walls benefits and costs. High variability in data is mostly related to the different characteristics of systems, buildings envelope, surrounding environment and local weather conditions.

Jet energy scale and resolution measured in proton-proton collisions at s =13 TeV with the ATLAS detector

Abstract: Jet energy scale and resolution measurements with their associated uncertainties are reported for jets using 36-81 fb$^{-1}$ of proton-proton collision data with a centre-of-mass energy of $\sqrt{s}=13$ TeV collected by the ATLAS detector at the LHC. Jets are reconstructed using two different input types: topo-clusters formed from energy deposits in calorimeter cells, as well as an algorithmic combination of charged-particle tracks with those topo-clusters, referred to as the ATLAS particle-flow reconstruction method. The anti-$k_t$ jet algorithm with radius parameter $R=0.4$ is the primary jet definition used for both jet types. Jets are initially calibrated using a sequence of simulation-based corrections. Next, several $\textit{in situ}$ techniques are employed to correct for differences between data and simulation and to measure the resolution of jets. The systematic uncertainties in the jet energy scale for central jets ($|\eta| 2.5$ TeV). The relative jet energy resolution is measured and ranges from ($24 \pm 1.5$)% at 20 GeV to ($6 \pm 0.5$)% at 300 GeV.

Machine learning approach for pavement performance prediction

Abstract: In recent years, there has been an increasing interest in the application of machine learning for the prediction of pavement performance. Prediction models are used to predict the future pavement c...

A Survey on Blockchain Interoperability: Past, Present, and Future Trends

Abstract: Blockchain interoperability is emerging as one of the crucial features of blockchain technology, but the knowledge necessary for achieving it is fragmented. This fact makes it challenging for academics and the industry to achieve interoperability among blockchains seamlessly. Given this new domain’s novelty and potential, we conduct a literature review on blockchain interoperability by collecting 284 papers and 120 grey literature documents, constituting a corpus of 404 documents. From those 404 documents, we systematically analyzed and discussed 102 documents, including peer-reviewed papers and grey literature. Our review classifies studies in three categories: Public Connectors, Blockchain of Blockchains, and Hybrid Connectors. Each category is further divided into sub-categories based on defined criteria. We classify 67 existing solutions in one sub-category using the Blockchain Interoperability Framework, providing a holistic overview of blockchain interoperability. Our findings show that blockchain interoperability has a much broader spectrum than cryptocurrencies and cross-chain asset transfers. Finally, this article discusses supporting technologies, standards, use cases, open challenges, and future research directions, paving the way for research in the area.

GW190521 as a Merger of Proca Stars: A Potential New Vector Boson of 8.7×10^{-13} eV.

Abstract: Advanced LIGO-Virgo have reported a short gravitational-wave signal (GW190521) interpreted as a quasicircular merger of black holes, one at least populating the pair-instability supernova gap, that formed a remnant black hole of ${M}_{f}\ensuremath{\sim}142\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ at a luminosity distance of ${d}_{L}\ensuremath{\sim}5.3\text{ }\text{ }\mathrm{Gpc}$. With barely visible pre-merger emission, however, GW190521 merits further investigation of the pre-merger dynamics and even of the very nature of the colliding objects. We show that GW190521 is consistent with numerically simulated signals from head-on collisions of two (equal mass and spin) horizonless vector boson stars (aka Proca stars), forming a final black hole with ${M}_{f}=23{1}_{\ensuremath{-}17}^{+13}\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$, located at a distance of ${d}_{L}=57{1}_{\ensuremath{-}181}^{+348}\text{ }\text{ }\mathrm{Mpc}$. This provides the first demonstration of close degeneracy between these two theoretical models, for a real gravitational-wave event. The favored mass for the ultralight vector boson constituent of the Proca stars is ${\ensuremath{\mu}}_{\mathrm{V}}=8.7{2}_{\ensuremath{-}0.82}^{+0.73}\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}13}\text{ }\text{ }\mathrm{eV}$. Confirmation of the Proca star interpretation, which we find statistically slightly preferred, would provide the first evidence for a long sought dark matter particle.

Muon reconstruction and identification efficiency in ATLAS using the full Run 2 pp collision data set at √ s = 13 TeV

Abstract: This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 $$\hbox {fb}^{-1}$$ fb - 1 of pp collision data at $$\sqrt{s}=13$$ s = 13 TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of $$Z\rightarrow \mu \mu $$ Z → μ μ and $$J/\psi \rightarrow \mu \mu $$ J / ψ → μ μ decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of $$|\eta |<2.7$$ | η | < 2.7 .

Simultaneous Presence of Open Metal Sites and Amine Groups on a 3D Dy(III)-Metal-Organic Framework Catalyst for Mild and Solvent-Free Conversion of CO2 to Cyclic Carbonates.

Abstract: Carbon dioxide (CO2) fixation to generate chemicals and fuels is of high current importance, especially toward finding mild and efficient strategies for catalytic CO2 transformation to value added products. Herein, we report a novel Lewis acid-base bifunctional amine-functionalized dysprosium(III) metal-organic framework [Dy3(data)3·2DMF]·DMF (2,5-data: 2,5-diamino-terephthalate), NH2-TMU-73. This compound was fully characterized and its crystal structure reveals a 3D metal-organic framework (MOF) with micropores and free NH2 groups capable of promoting the chemical fixation of CO2 to cyclic carbonates. NH2-TMU-73 is built from the Dy(III) centers and data2- blocks, which are arranged into an intricate underlying net with a rare type of xah topology. After activation, NH2-TMU-73 and its terephthalate-based analogue (TMU-73) were applied for CO2-to-epoxide coupling reactions to produce cyclic carbonates. Important features of this catalytic process concern high efficiency and activity in the absence of cocatalyst, use of solvent-free medium, atmospheric CO2 pressure, and ambient temperature conditions. Also, NH2-TMU-73 features high structural stability and can be recycled and reused in subsequent catalytic tests. An important role of free amino groups and open metal sites in the MOF catalyst was highlighted when suggesting a possible reaction mechanism.

Third-Order Nonlinear Optical Behavior of an Amide-Tricarboxylate Zinc(II) Metal-Organic Framework with Two-Fold 3D+3D Interpenetration.

Abstract: A new metal-organic framework (MOF), [Zn4(μ4-O)(μ6-L)2(H2O)2]n·nDMF (ZSTU-10), was assembled from zinc(II) nitrate and N,N',N″-bis(4-carboxylate)trimesicamide linkers and fully characterized. Its crystal structure discloses an intricate two-fold 3D+3D interpenetrated MOF driven by the [Zn4(μ4-O)]-based tetragonal secondary building units and the C3-symmetric tris-amide-tricarboxylate linkers (μ6-L3-). Topological analysis of ZSTU-10 reveals two interpenetrated 3,6-connected nets with an rtl (rutile) topology. Z-Scan analysis at 532 nm was conducted to study a nonlinear optical (NLO) behavior of ZSTU-10. The nonlinear responses of ZSTU-10 were explored under various laser intensities, revealing notable third-order NLO properties in the visible region. A large two-photon absorption at lower incident intensities highlights the fact that ZSTU-10 can be applied in optical limiting devices as well as optical modulators. Moreover, a nonlinear refractive index (n2) is indicative of a self-defocusing behavior. This work thus expands a family of novel MOF materials with remarkable optical properties.

Hybrid metal additive manufacturing: A state–of–the-art review

Abstract: This paper starts from the early developments and working principles of the additive manufacturing of polymers, continues with a glimpse on the extension to metals with identification and characterization of the two most widespread technologies, and ends with an overview of the recent developments in hybrid metal additive manufacturing. Earlier classifications of hybrid manufacturing with roots on the utilization of primarily processed raw materials in the form of ingots, sheets, rods, tubes, profiles, powders and pellets are revisited in the light of the emergence of a new type of hybridization resulting from the combination of additive manufacturing with traditional manufacturing processes. Special emphasis is given to the combination of additive manufacturing with forming processes with the two-fold objective of (i) increasing the applicability domain of metal additive manufacturing and overcoming its limitations related to low productivity, metallurgical defects, rough surface quality and lack of dimensional precision, and (ii) adding flexibility and fostering new applications of traditional forming processes.

Cysteine metabolic circuitries: druggable targets in cancer

Abstract: To enable survival in adverse conditions, cancer cells undergo global metabolic adaptations. The amino acid cysteine actively contributes to cancer metabolic remodelling on three different levels: first, in its free form, in redox control, as a component of the antioxidant glutathione or its involvement in protein s-cysteinylation, a reversible post-translational modification; second, as a substrate for the production of hydrogen sulphide (H2S), which feeds the mitochondrial electron transfer chain and mediates per-sulphidation of ATPase and glycolytic enzymes, thereby stimulating cellular bioenergetics; and, finally, as a carbon source for epigenetic regulation, biomass production and energy production. This review will provide a systematic portrayal of the role of cysteine in cancer biology as a source of carbon and sulphur atoms, the pivotal role of cysteine in different metabolic pathways and the importance of H2S as an energetic substrate and signalling molecule. The different pools of cysteine in the cell and within the body, and their putative use as prognostic cancer markers will be also addressed. Finally, we will discuss the pharmacological means and potential of targeting cysteine metabolism for the treatment of cancer.

Hyperspectral Image Denoising Based on Global and Nonlocal Low-Rank Factorizations

Abstract: The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio of the measurements, thus calling for effective denoising techniques. HSIs from the real world lie in low-dimensional subspaces and are self-similar. The low dimensionality stems from the high correlation existing among the reflectance vectors, and self-similarity is common in real-world images. In this article, we exploit the above two properties. The low dimensionality is a global property that enables the denoising to be formulated just with respect to the subspace representation coefficients, thus greatly improving the denoising performance and reducing the computational complexity during processing. The self-similarity is exploited via a low-rank tensor factorization of nonlocal similar 3-D patches. The proposed factorization hinges on the optimal shrinkage/thresholding of the singular value decomposition (SVD) singular values of low-rank tensor unfoldings. As a result, the proposed method is user friendly and insensitive to its parameters. Its effectiveness is illustrated in a comparison with state-of-the-art competitors. A MATLAB demo of this work is available at https://github.com/LinaZhuang for the sake of reproducibility.

Soft computing techniques: Systematic multiscale models to predict the compressive strength of HVFA concrete based on mix proportions and curing times

Abstract: Advances in technology and environmental issues allow the building industry to use ever more high-performance engineered materials. In this study, the hardness of concrete mixtures with high-volume fly ash (HVFA) has been evaluated and modeled for the LEED (Leadership for Energy and Environmental Design). High-performance building materials may have greater strength, ductility, external factor resistance, more environmentally sustainable construction, and lower cost than conventional building materials. To overcome the mentioned matter, this study aims to establish systematic multiscale models to predict the compressive strength of concrete mixes containing a high volume of fly ash (HVFA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested HVFA concrete mixes) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear, Nonlinear Regressions, Multi-logistic Regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of concrete, i.e. fly ash (class C and F) incorporation ratio (0–80% of cement's mass), water-to-binder ratio (0.27–0.58), and gravel, sand, cement contents and curing ages (3–365 days). According to the correlation coefficient (R) and the root mean square error, the compressive strength of HVFA concrete can be well predicted in terms of w/b, fly ash, cement, sand, and gravel densities, and curing time using various simulation techniques. Among the used approaches and based on the training data set, the model made based on the ANN, M5P-tree, and Non-linear regression models seem to be the most reliable models. The results of this study suggest that the M5Ptree-based model is performing better than other applied models using training and testing datasets. The maximum and minimum percentage of error between the actual test results and the outcome of the prediction using MLR, LR, M5P-tree, and ANN were 0.03–43%, 0.03–54%, 0.04–33%, and 0.03–41% respectively. Based on the outcomes from the models and statistical assessments such as coefficient of determination (R2), mean absolute error (MAE) and the root mean square error (RMSE), the models M5P-tree, ANN, and MLR respectively were predicted the compressive strength of the HVFA concrete very well with a high value of R2 and low values of MAE and RMSE based on the comparison with experimental data. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of HVFA concrete with this data set.

High-Fidelity Controlled- Z Gate with Maximal Intermediate Leakage Operating at the Speed Limit in a Superconducting Quantum Processor

Abstract: Simple tuneup of fast two-qubit gates is essential for the scaling of quantum processors. We introduce the sudden variant (SNZ) of the net zero scheme realizing controlled-Z (CZ) gates by flux control of transmon frequency. SNZ CZ gates realized in a multitransmon processor operate at the speed limit of transverse coupling between computational and noncomputational states by maximizing intermediate leakage. Beyond speed, the key advantage of SNZ is tuneup simplicity, owing to the regular structure of conditional phase and leakage as a function of two control parameters. SNZ is compatible with scalable schemes for quantum error correction and adaptable to generalized conditional-phase gates useful in intermediate-scale applications.