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Understanding excited-state intramolecular proton transfer (ESIPT) is essential for designing organic molecules to enhance photophysical and luminophore properties in the development of optoelectronic devices. In this context, an attempt has been made to understand the impact of substituents on the ESIPT process of 2-(oxazolinyl)-phenol. Electron donating (EDG: -NH2, -OCH3, and -CH3) and electron withdrawing (EWG: -Cl, -Br, -COOH, -CF3, -CN, and -NO2) substitutions have been computationally designed and screened through density functional theory (DFT) and time-dependent density-functional theory (TDDFT) calculations. Furthermore, the ground state intramolecular proton transfer and ESIPT mechanisms of these designed luminophores are explored using the transition state theory. The results reveal that molecules with EDG show higher absorption and emission peaks than molecules with EWG and also indicate that the mobility of charge carriers in 2-(oxazolinyl)-phenol derivatives is significantly influenced by substituents. We found that the EWGs decrease the reorganization energy and increase the vertical ionization potential and electron affinity values, as well as the highest occupied molecular orbital-lowest unoccupied molecular orbital gap, compared to the EDG substituted molecules. Significantly, the excited state (S1) of the keto emission (K) form shows notably larger values for the EDG substitutions. The intersystem crossing pathway efficiency weakens with reduced spin-orbit coupling matrix element in the enol form with electron-donating substituents and vice versa in the keto form during S1-T3 transitions. Our research links intramolecular proton transfers and triplet generation, making these substituted molecules appealing for optoelectronic devices. Introducing EDGs, such as -NH2, boosts the ESIPT reaction in 2-(oxazolinyl)-phenol. This study guides designing ESIPT emitters with unique photophysical properties.

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Artistic pieces can be studied from several perspectives, one example being their reception among readers over time. In the present work, we approach this interesting topic from the standpoint of literary works, particularly assessing the task of predicting whether a book will become a best seller. Unlike previous approaches, we focused on the full content of books and considered visualization and classification tasks. We employed visualization for the preliminary exploration of the data structure and properties, involving SemAxis and linear discriminant analyses. To obtain quantitative and more objective results, we employed various classifiers. Such approaches were used along with a dataset containing (i) books published from 1895 to 1923 and consecrated as best sellers by the Publishers Weekly Bestseller Lists and (ii) literary works published in the same period but not being mentioned in that list. Our comparison of methods revealed that the best-achieved result-combining a bag-of-words representation with a logistic regression classifier-led to an average accuracy of 0.75 both for the leave-one-out and 10-fold cross-validations. Such an outcome enhances the difficulty in predicting the success of books with high accuracy, even using the full content of the texts. Nevertheless, our findings provide insights into the factors leading to the relative success of a literary work.

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Introduction: The covid-19 pandemic has left a high number of infected and dead in the world, where several social measures have been imposed on the population, especially social isolation. Objective: Given this scenario, based on Vygotsky's socio-historical theory, this original article aimed to identify the meanings of social isolation in the covid-19 pandemic for Brazilian youth. Method: A survey was carried out, with the participation of 571 young Brazilians, women and men aged between 18 and 25, who answered a sociodemographic questionnaire and the Free Word Association Method (FWAM), analyzed with the help of IRaMuTeQ. Results: The results showed that virtualized socialization is insufficient for the social demand of young people, as evidenced by the most evoked words in category 1, "Homesickness" (χ² = 55,41); "Boredom" (χ² = 50,06); "Anxiety" (χ² = 42,67); "Loneliness" (χ² = 37,15); "Sadness" (χ² = 32,78). It was also identified that their routine was transformed with the insertion of obligations and leisure primarily in the home environment, demonstrated by the prevalence of words like "House" (χ² = 116,18); "Home Office" (χ² = 26,06); "Movie" (χ² = 26,06); "Mask" (χ² = 24,96); "Alcohol" (χ² = 21,67); "Lockdown" (χ² = 21,67), in category 2. Still, young people brought their resignifications in this period, in the face of collective responsibility, indicated by the choice of words in category 3, such as "Care" (χ² = 84,05); "Responsibility" (χ² = 59,21); "Respect" (χ² = 55,03); "Prevention" (χ² = 52,68); "Empathy" (χ² = 34,06); "Protection" (χ² = 21,99). Conclusions: It is concluded that isolation brought negative consequences on relationships and mental health of young people, but also a greater social awareness.

Introducción: La pandemia del covid-19 ha dejado un alto número de contagiados y muertos en el mundo, donde se han impuesto varias medidas sociales a la población, en especial el aislamiento social. Objetivo: Ante ese escenario, con base en la teoría sociohistórica de Vygotsky, este artículo original tuvo como objetivo identificar los significados del aislamiento social en la pandemia de covid-19 para la juventud brasileña. Método: Se realizó una encuesta, con la participación de 571 jóvenes brasileños, mujeres y hombres de 18 a 25 años, que respondieron un cuestionario sociodemográfico y la Técnica de Asociación Libre de Palabras (TALP), analizados con la ayuda de IRaMuTeQ. Resultados: Los resultados mostraron que la socialización virtualizada es insuficiente para la demanda social de los jóvenes, como lo demuestran las palabras más evocadas en la clase 1, "Nostalgia" (χ² = 55,41) "Aburrimiento" (χ² = 50,06); "Ansiedad" (χ² = 42,67); "Soledad" (χ² = 37,15); "Tristeza" (χ² = 32,78). También identificaron que su rutina se transformó con la inserción de obligaciones y ocio principalmente en el ámbito del hogar demostrado por la prevalencia de palabras como "Casa" (χ² = 116,18); "Teletrabajo" (χ² = 26,06); "Película" (χ² = 26,06); "Mascarilla" (χ² = 24,96); "Alcohol" (χ² = 21,67); "Confinamiento" (χ² = 21,67), en la clase 2. Aún así, los jóvenes trajeron sus resignificaciones en este período, frente a la responsabilidad colectiva, indicado por la elección de palabras en la clase 3, como "Cuidado" (χ² = 84,05); "Responsabilidad" (χ² = 59,21); "Respeto" (χ² = 55,03); "Prevención" (χ² = 52,68); "Empatía" (χ² = 34,06); "Protección" (χ² = 21,99). Conclusiones: Se concluye que el aislamiento trajo consecuencias negativas en las relaciones y la salud mental de los jóvenes, pero también una mayor conciencia social.

Introdução: A pandemia de covid-19 tem deixado um elevado número de infectados e mortos no mundo, onde diversas medidas sociais foram impostas à população, especialmente de isolamento social. Objetivo: Diante desse cenário, com base na teoria sócio-histórica de Vygotsky, este artigo original objetivou identificar quais os significados do isolamento social na pandemia de covid-19 para a juventude brasileira. Método: Realizou-se uma pesquisa de levantamento, com a participação de 571 jovens brasileiros, mulheres e homens de 18 a 25 anos, que responderam um questionário sociodemográfico e à Técnica de Associação Livre de Palavras (TALP), analisados com auxílio do IRaMuTeQ. Resultados: Os resultados mostraram que a socialização virtualizada é insuficiente para a demanda social dos jovens, como evidenciado através das palavras mais evocadas na classe 1, "Saudade" (χ² = 55,41) "Tédio" (χ² = 50,06); "Ansiedade" (χ² = 42,67); "Solidão" (χ² = 37,15); "Tristeza" (χ² = 32,78). Também identificaram que a rotina destes foi transformada com a inserção das obrigações e lazer prioritariamente no ambiente de casa, demonstrado pela prevalência de palavras como "Casa" (χ² = 116,18); "Home Office" (χ² = 26,06); "Filme" (χ² = 26,06); "Máscara" (χ² = 24,96); "Álcool" (χ² = 21,67); "Lockdown" (χ² = 21,67), na classe 2. Ainda, os jovens trouxeram suas ressignificações nesse período, diante da responsabilidade coletiva, apontado pela escolha de palavras na classe 3, como "Cuidado" (χ² = 84,05); "Responsabilidade" (χ² = 59,21); "Respeito" (χ² = 55,03); "Prevenção" (χ² = 52,68); "Empatia" (χ² = 34,06); "Proteção" (χ² = 21,99). Conclusão: Conclui-se que o isolamento trouxe consequências negativas nas relações e saúde mental dos jovens, mas também uma maior conscientização social.

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Zirconia oxide (ZrO2) is a material that has aroused great interest in the scientific community for its general use in various technological applications, such as fuel cells, solar cells, electronic devices, catalysis, dental biomaterial and ceramics. When it is applied as a catalyst, the doping and vacancy effects of their crystalline phases are important properties to guide new developments. This work investigates tetragonal and monoclinic crystalline phases of the Zn-doped ZrO2 by periodic density functional calculations. Changes in the electronic and acid-basic properties were performed by Bader charge analysis, the density of states calculations (DOS) and the projected density of states (PDOS). The formation of oxygen vacancies was also evaluated. The calculated oxygen vacancy formation energies indicate that it is much easier to generate oxygen vacancy in the Zn-doped ZrO2 than in the pure material; in addition, oxygen vacancy formation is favored in the monoclinic phase. Bader charge analyses and projected density of states indicated that the doping of ZrO2 with Zn creates more basic and acid sites. The most stable material is the Zn-doped 3-fold coordinated Zr atom of the m-ZrO2, which can be used for future developments and applications.

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Plants from the Garcinia genus have been used worldwide due to their therapeutic properties. Among the various metabolites isolated from this genus, 7-epi-clusianone, a tetraprenylated benzophenone, stands out for its wide range of identified biological activities. This benzophenone can exist in five tautomeric forms, although the benzene-d6 and chloroform-d3 solution nuclear magnetic resonance (NMR) spectra revealed only two tautomeric forms (B and C) in equilibrium, with concentration ratio depending on the solvent in which the spectrum was obtained. Calculated energy values suggested that tautomeric forms B and E would be prevalent in benzene-d6 solution, in contrast to the experimental data. Considering this conflicting result, we employed the statistical DP4 + method based on 13C and 1H NMR chemical shift calculations, in the gas phase and in benzene-d6 solution, to confirm that the B and C tautomeric forms of 7-epi-clusianone are the most prevalent in the experimental conditions.

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Chagas disease is a life-threatening illness caused by the parasite Trypanosoma cruzi. The diagnosis of the acute form of the disease is performed by trained microscopists who detect parasites in blood smear samples. Since this method requires a dedicated high-resolution camera system attached to the microscope, the diagnostic method is more expensive and often prohibitive for low-income settings. Here, we present a machine learning approach based on a random forest (RF) algorithm for the detection and counting of T. cruzi trypomastigotes in mobile phone images. We analyzed micrographs of blood smear samples that were acquired using a mobile device camera capable of capturing images in a resolution of 12 megapixels. We extracted a set of features that describe morphometric parameters (geometry and curvature), as well as color, and texture measurements of 1,314 parasites. The features were divided into train and test sets (4:1) and classified using the RF algorithm. The values of precision, sensitivity, and area under the receiver operating characteristic (ROC) curve of the proposed method were 87.6%, 90.5%, and 0.942, respectively. Automating image analysis acquired with a mobile device is a viable alternative for reducing costs and gaining efficiency in the use of the optical microscope.

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Prostate cancer (PCa) is a pervasive condition that is manifested in a wide range of histologic patterns in biopsy samples. Given the importance of identifying abnormal prostate tissue to improve prognosis, many computerized methodologies aimed at assisting pathologists in diagnosis have been developed. It is often argued that improved diagnosis of a tissue region can be obtained by considering measurements that can take into account several properties of its surroundings, therefore providing a more robust context for the analysis. Here we propose a novel methodology that can be used for systematically defining contextual features regarding prostate glands. This is done by defining a Gland Context Network (GCN), a representation of the prostate sample containing information about the spatial relationship between glands as well as the similarity between their appearance. We show that such a network can be used for establishing contextual features at any spatial scale, therefore providing information that is not easily obtained from traditional shape and textural features. Furthermore, it is shown that even basic features derived from a GCN can lead to state-of-the-art classification performance regarding PCa. All in all, GCNs can assist in defining more effective approaches for PCa grading.

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Extraordinary progress has been made over the last decade in the treatment of multiple myeloma with the incorporation of new drugs, particularly proteasome inhibitors, immunomodulators, and monoclonal antibodies. The combined use of innovative drugs, already in the first lines of treatment, has led to an expressive increase in the survival of these patients. However, the approach to relapse remains a great challenge, and the disease continues to be incurable. In this scenario, modern immunotherapy has gained the limelight, especially with its recent use of CAR-T cells in clinical trials, as in the case of multiple myeloma, having the BCMA as the primary target. The results are impactful in the treatment of multiple myeloma patients who have had multiple relapses and are triple- and penta-refractory. In this Consensus, we have brought together a group of experts in multiple myeloma to discuss and forward their recommendations for the future, which we hope is very near, incorporating the CAR-T in our country.

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Chimeric antigen receptor T-cells (CAR-T cells) are a new modality of oncological treatment which has demonstrated impressive response in refractory or relapsed diseases, such as acute lymphoblastic leukemia (ALL), lymphomas, and myeloma but is also associated with unique and potentially life-threatening toxicities. The most common adverse events (AEs) include cytokine release syndrome (CRS), neurological toxicities, such as the immune effector cell-associated neurotoxicity syndrome (ICANS), cytopenias, infections, and hypogammaglobulinemia. These may be severe and require admission of the patient to an intensive care unit. However, these AEs are manageable when recognized early and treated by a duly trained team. The objective of this article is to report a consensus compiled by specialists in the fields of oncohematology, bone marrow transplantation, and cellular therapy describing recommendations on the Clinical Centers preparation, training of teams that will use CAR-T cells, and leading clinical questions as to their use and the management of potential complications.

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The world desperately needs new technologies and solutions for gas capture and separation. To make this possible, molecular modeling is applied here to investigate the structural, thermodynamic, and dynamical properties of a model for the poly(urethane urea) (PUU) oligomer model to selectively capture CO2 in the presence of CH4. In this work, we applied a well-known approach to derive atomic partial charges for atoms in a polymer chain based on self-consistent sampling using quantum chemistry and stochastic dynamics. The interactions of the gases with the PUU model were studied in a pure gas based system as well as in a gas mixture. A detailed structure characterization revealed high interaction of CO2 molecules with the hard segments of the PUU. Therefore, the structural and energy properties explain the reasons for the greater CO2 sorption than CH4. We find that the CO2 sorption is higher than the CH4 with a selectivity of 7.5 at 298 K for the gas mixture. We characterized the Gibbs dividing surface for each system, and the CO2 is confined for a long time at the gas-oligomer model interface. The simulated oligomer model showed performance above the 2008 Robeson's upper bound and may be a potential material for CO2/CH4 separation. Further computational and experimental studies are needed to evaluate the material.

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The development of polymer electrolytes (PEs) is crucial for advancing safe, high-energy density batteries, such as lithium-metal and other beyond lithium-ion chemistries. However, reaching the optimum balance between mechanical stiffness and ionic conductivity is not a straightforward task. Zwitterionic (ZI) gel electrolytes comprising lithium salt and ionic liquid (IL) solutions within a fully ZI polymer network can, in this context, provide useful properties. Although such materials have shown compatibility with lithium metal in batteries, several fundamental structure-dynamic relationships regarding ionic transport and the Li+ coordination environment remain unclear. To better resolve such issues, molecular dynamics simulations were carried out for two IL-based electrolyte systems, N-butyl-N-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide ([BMP][TFSI]) with 1 M LiTFSI salt and a ZI gel electrolyte containing the IL and a ZI copolymer: poly(2-methacryloyloxyethyl phosphorylcholine-co-sulfobetaine vinylimidazole), poly(MPC-co-SBVI). The addition of ZI polymer decreases the [TFSI]- -[Li]+ interactions and increases the IL ion diffusivities, and consequently, the overall ZI gel ionic conductivity. The structural analyses showed a large preference for lithium-ion interactions with the polymer phosphonate groups, while the [TFSI]- anions interact directly with the sulfonate group and the [BMP]+ cations only display secondary interactions with the polymer. In contrast to previous experimental data on the same system, the simulated transference numbers showed smaller [Li]+ contributions to the overall ionic conductivities, mainly due to negatively charged lithium aggregates and the strong lithium-ion interactions in the systems.

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Herein, molecular modeling techniques were used with the main goal to obtain candidates from a drug database as potential targets to be used against SARS-CoV-2. This novel coronavirus, responsible by the COVID-19 outbreak since the end of 2019, became a challenge since there is not vaccine for this disease. The first step in this investigation was to solvate the isolated S-protein in water for molecular dynamics (MD) simulation, being observed a transition from "up" to "down" conformation of receptor-binding domain (RBD) of the S-protein with angle of 54.3 and 43.0 degrees, respectively. The RBD region was more exposed to the solvent and to the possible drugs due to its enhanced surface area. From the equilibrated MD structure, virtual screening by docking calculations were performed using a library contained 9091 FDA approved drugs. Among them, 24 best-scored ligands (14 traditional herbal isolate and 10 approved drugs) with the binding energy below -8.1 kcal/mol were selected as potential candidates to inhibit the SARS-CoV-2 S-protein, preventing the human cell infection and their replication. For instance, the ivermectin drug (present in our list of promise candidates) was recently used successful to control viral replication in vitro. MD simulations were performed for the three best ligands@S-protein complexes and the binding energies were calculated using the MM/PBSA approach. Overall, it is highlighted an important strategy, some key residues, and chemical groups which may be considered on clinical trials for COVID-19 outbreak. [Formula: see text]Communicated by Ramaswamy H. Sarma.

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Abstract: Honey pollen samples of Melipona seminigra pernigraMoure & Kerr 1950 sampled between 2017 and 2019 from experimental apiaries installed in campo rupestre on canga (CRC) vegetation of the Serra dos Carajás aimed to evaluated seasonal floral availability of undisturbed and mining-influenced areas. Around one hundred pollen types were identified mainly belonging to Fabaceae, Myrtaceae and Euphorbiaceae (31, 6 and 5 species, respectively). Mining area presented the highest pollen richness, almost twice those identified in the undisturbed areas. 80% of the pollen types are rare with concentrations ≤ 2,000 pollen grains/10 g, while the remaining were the most abundant, frequent and the primary bee sources. These latter correspond mostly to native plants species such as Tapirira guianensis Aubl., Protium spp., Aparisthmium cordatum (A.Juss.) Baill., Mimosa acutistipula var. ferrea Barneby, Periandra mediterranea (Vell.) Taub., Miconia spp., Pleroma carajasense K.Rocha, Myrcia splendens (Sw.) DC., Serjania spp. and Solanum crinitum Lam. All pollen types were identified during both seasons, but higher concentration values are related to the dry period (June-September). The statistical analysis of the pollen data indicated that there was no significant difference between undisturbed and mining-influenced areas, since primary bee sources of this study are widespread used in revegetation of mined areas.

Resumo: O conteúdo polínico de amostras de mel coletadas nos anos de 2017 e 2019 de apiários experimentais de Melipona seminigra pernigraMoure & Kerr 1950, instalado dentro de uma vegetação de campo rupestre em um afloramento de canga na Serra dos Carajás, sudeste da Amazônia, foi analisado para entender a variabilidade local dos recursos florais em áreas naturais e perturbadas. Aproximadamente 100% dos tipos polínicos foram identificados e pertencem principalmente às famílias Fabaceae, Myrtaceae e Euphorbiaceae (31, 6 e 5 espécies, respectivamente). Áreas de mineração apresentaram a maior riqueza de pólen, quase o dobro daquelas identificadas em áreas perturbadas. 80% dos tipos de pólen são raros com concentrações ≤ 2.000 grãos de pólen/10g, enquanto que os restantes foram os mais abundantes, frequentes e fontes primárias para as abelhas. Este últimos correspondem principalmente a plantas nativas como Tapirira guianensis Aubl., Protium spp., Aparisthmium cordatum (A.Juss.) Baill., Mimosa acutistipula var. ferrea Barneby, Periandra mediterrânea (Vell.) Taub., Miconia spp., Pleroma carajasense K.Rocha, Myrcia splendens (Sw.) DC., Serjania spp. e Solanum crinitum Lam. Todos os tipos polínicos foram identificados durante ambas as estações, mas altas concentrações estão relacionadas ao período seco (junho-setembro). A análise estatística indicou que não houve diferença significativa nos dados de pólen de mel entre áreas naturais e áreas anteriormente degradadas, uma vez que as fontes primárias das abelhas deste estudo são amplamente utilizadas na revegetação de áreas mineradas.

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Lithium-metal batteries are promising candidates to fulfill the future performance requirements for energy storage applications. However, the tendency to form metallic dendrites and the undesirable side reactions between the electrolyte and the Li electrode lead to poor performance and safety issues in these batteries. Therefore, understanding the interfacial properties and the Li-metal surface/electrolyte interactions is crucial to resolve the remaining obstacles and make these devices feasible. Here, we report a computational study on the interface effects in ternary polymer electrolytes composed by poly(ethylene oxide) (PEO), lithium salts, and different ionic liquids (ILs) confined between two Li-metal slabs. Atomistic simulations are used to characterize the local environment of the Li+ ions and the transport properties in the bulk and at the interface regions. Aggregation of ions at the metal surface is seen in all investigated systems; the structure and composition are directly correlated to the IL components. The strong interactions between the electrolyte species and the Li-metal atoms result in the structuring of the electrolyte at the interface region, in which comparatively small and flat ions result in a well-defined region with extensive Li+ populations and high self-diffusion coefficients. In contrast, large ions such as [P222mom]+ increase the PEO density in the bulk due to large steric effects at the interface. Therefore, the choice of specific ILs in ternary polymer electrolytes can tune the structure-dynamic properties at the Li-metal surface/electrolyte interface, controlling the SEI formation at the electrode surface, and thereby improve battery performance.

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All cellular processes can be ultimately understood in terms of respective fundamental biochemical interactions between molecules, which can be modeled as networks. Very often, these molecules are shared by more than one process, therefore interconnecting them. Despite this effect, cellular processes are usually described by separate networks with heterogeneous levels of detail, such as metabolic, protein-protein interaction, and transcription regulation networks. Aiming at obtaining a unified representation of cellular processes, we describe in this work an integrative framework that draws concepts from rule-based modeling. In order to probe the capabilities of the framework, we used an organism-specific database and genomic information to model the whole-cell biochemical network of the Mycoplasma genitalium organism. This modeling accounted for 15 cellular processes and resulted in a single component network, indicating that all processes are somehow interconnected. The topological analysis of the network showed structural consistency with biological networks in the literature. In order to validate the network, we estimated gene essentiality by simulating gene deletions and compared the results with experimental data available in the literature. We could classify 212 genes as essential, being 95% of them consistent with experimental results. Although we adopted a relatively simple organism as a case study, we suggest that the presented framework has the potential for paving the way to more integrated studies of whole organisms leading to a systemic analysis of cells on a broader scale. The modeling of other organisms using this framework could provide useful large-scale models for different fields of research such as bioengineering, network biology, and synthetic biology, and also provide novel tools for medical and industrial applications.

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In this paper, we investigated the possibility of using the magnetic Laplacian to characterize directed networks. We address the problem of characterization of network models and perform the inference of the parameters used to generate these networks under analysis. Many interesting results are obtained, including the finding that the community structure is related to rotational symmetry in the spectral measurements for a type of stochastic block model. Due the hermiticity property of the magnetic Laplacian we show here how to scale our approach to larger networks containing hundreds of thousands of nodes using the Kernel Polynomial Method (KPM), a method commonly used in condensed matter physics. Using a combination of KPM with the Wasserstein metric, we show how we can measure distances between networks, even when these networks are directed, large, and have different sizes, a hard problem that cannot be tackled by previous methods presented in the literature.

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Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of the regular behavior and the adverse situations that may occur with the bees. It also may lead to better management and utilization of bees as pollinators. We address an investigation with Recurrent Neural Networks in the task of forecasting bees' level of activity taking into account previous values of level of activity and environmental data such as temperature, solar irradiance and barometric pressure. We also show how different input time windows, algorithms of attribute selection and correlation analysis can help improve the accuracy of our model.

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