Mateus Cavalcante Sá, Marco Aurélio Holanda de Castro
Building Information Modeling (BIM) é uma tecnologia promissora na indústria da construção civil. Percebe-se sua consolidação, e as aplicações dessa tecnologia tornam-se indispensáveis para o dia a dia de empresas e projetistas. No segmento de projetos de infraestrutura, especificamente no setor de saneamento, a metodologia BIM vem sendo implementada e disseminada. Uma implementação apropriada facilita os processos de projeto e construção, podendo resultar em obras de melhor qualidade e prazos reduzidos. Nesse sentido, este trabalho apresenta uma proposta de automatização da geração de redes de abastecimento de água em BIM, utilizando uma interface entre o sistema UFC e o Civil 3D. Essa interface foi desenvolvida por meio de rotinas em Dynamo e linguagem Python. Entre as funcionalidades, a interface permite realizar conversões de arquivos do formato .inp para .xlsx. Para validar sua funcionalidade, realizaram-se testes de geração automática em BIM de redes de abastecimento de diversos formatos, incluindo tubos e conexões dimensionados com o sistema UFC, o que permitiu uma avaliação mais ampla. Os resultados demonstraram a aplicabilidade da ferramenta, que constitui uma alternativa viável para integração em fluxos de trabalho.
The transformation to Industry 4.0 has significantly revolutionized manufacturing and production processes, raising important questions about their impact on sustainability. This study aims to explore the interplay between Industry 4.0 and the economic, social, and environmental dimensions of sustainability. The methodological approach includes advanced text-mining, sentiment analysis, and association rule-mining techniques to examine 6,759 abstracts from the Scopus database. The text mining highlighted frequent keywords related to Industry 4.0 and the three sustainability dimensions, characterized by “economic growth,” “circular economy,” “social responsibility,” “education 4.0,” “energy efficiency,” and “waste management.” Sentiment analysis revealed a predominantly positive perspective, with 2,608 positive sentiments out of 2,761 in the economic dimension, 1,604 out of 1,728 in the social dimension, and 1,352 out of 1,527 in the environmental dimension. The association rule mining uncovered the associations between Industry 4.0 and each sustainability dimension. The highest support was observed between Industry 4.0 and economic sustainability, with a support value of 0.444, confidence of 0.855, and a lift of 1.060. These findings highlight the role of Industry 4.0 in promoting resource efficiency and reducing waste through circular economy principles and advanced manufacturing technologies. For the social dimension, the analysis revealed a strong association with Industry 4.0 (support: 0.430, confidence: 0.831, lift: 1.030), emphasizing its role in enhancing worker safety and job satisfaction by automating hazardous tasks and creating new high-tech job opportunities. In the environmental dimension, a significant association was found (support: 0.380, confidence: 0.827, lift: 1.024), showing Industry 4.0′s contribution to sustainability through optimized energy consumption and emissions reduction as the integration of big data and IoT enables real-time monitoring of environmental impacts. The rule combining economic and social aspects with Industry 4.0 (support: 0.219, confidence: 0.87, lift: 1.078) highlights the interconnected nature of these dimensions, suggesting many studies consider economic and social dimensions together in the Industry 4.0 context.
Niranjan Mahadevan, Rozi Fernanda, Yusuke Kouzai
et al.
<i>Rhizoctonia solani</i> is a basidiomycete phytopathogenic fungus that causes rapid necrosis in a wide range of crop species, leading to substantial agricultural losses worldwide. The species complex is divided into 13 anastomosis groups (AGs) based on hyphal fusion compatibility and further subdivided by culture morphology. While <i>R. solani</i> classifications were shown to be independent of host specificity, it remains unclear whether different <i>R. solani</i> isolates share similar virulence mechanisms. Here, we investigated the infectivity of Japanese <i>R. solani</i> isolates on <i>Brachypodium distachyon</i> and barley. Two isolates, AG-1 IA (from rice) and AG-4 HG-I+II (from cauliflower), infected leaves of both plants, but only AG-4 HG-I+II infected roots. <i>B. distachyon</i> accessions Bd3-1 and Gaz-4 and barley cultivar ‘Morex’ exhibited enhanced resistance to both isolates compared to <i>B. distachyon</i> Bd21 and barley cultivars ‘Haruna Nijo’ and ‘Golden Promise’. During AG-1 IA infection, but not AG-4 HG-I+II infection, resistant Bd3-1 and Morex induced genes for salicylic acid (SA) and <i>N</i>-hydroxypipecolic acid (NHP) biosynthesis. Pretreatment with SA or NHP conferred resistance to AG-1 IA, but not AG-4 HG-I+II, in susceptible <i>B. distachyon</i> Bd21 and barley Haruna Nijo. On the leaves of susceptible Bd21 and Haruna Nijo, AG-1 IA developed extensive mycelial networks with numerous infection cushions, which are specialized infection structures well-characterized in rice sheath blight. In contrast, AG-4 HG-I+II formed dispersed mycelial masses associated with underlying necrosis. We propose that the <i>R. solani</i> species complex encompasses at least two distinct infection strategies: AG-1 IA exhibits a hemibiotrophic lifestyle, while AG-4 HG-I+II follows a predominantly necrotrophic strategy.
The study focuses on the hydrogeological characterization of the aquifer system in the central sector of Cartago, Costa Rica. This area was selected due to its significant urbanization and agricultural activities, both of which heavily depend on groundwater resources. The conceptual hydrogeological model was developed using well records, field hydrogeological observations along rivers and material extraction pits, macroscopic sample collection for thin-section analysis, spring and well inventories, and piezometric level analysis. A series of hydrogeological profiles were modeled to visualize the subsurface configuration of hydrogeological units and their relationships with existing geological materials. In areas with sufficient well density and ad¬equate geographic distribution, the groundwater flow dynamics within the hydrogeological units were also analyzed. The results revealed that the aquifer system consists of a variety of materials, predominantly alluvial and laharic deposits, which function as aquifer hydrogeological units. These materials contain interspersed clay lenses, fine sands, and coarse sands, which collectively influence the formation of saturated zones, aquitards, and aquicludes. Additionally, these characteristics determine the degree of confinement of the aquifer units. In some sectors, this confinement results in water upwelling, creating artesian conditions. Flow directions were predominantly oriented from north to south, following the surface gradient, although variations in flow direction highlighted the complexity and interconnectivity of the units. For the first time, the hydrogeological model of the Cartago aquifer system was defined. It comprises the Taras, La Chinchilla, Cartago, El Bosque, Tejar, and Dulce Nombre hydrogeological units. Each of these units corresponds to a specific portion of the study area within the central sector of Cartago, which lies atop the Cartago aquifer system.
Kaitlyn M. Sarlo Davila, Rahul K. Nelli, Kruttika S. Phadke
et al.
ABSTRACTThe potential infectivity of severe acute respiratory syndrome associated coronavirus-2 (SARS-CoV-2) in animals raises a public health and economic concern, particularly the high susceptibility of white-tailed deer (WTD) to SARS-CoV-2. The disparity in the disease outcome between humans and WTD is very intriguing, as the latter are often asymptomatic, subclinical carriers of SARS-CoV-2. To date, no studies have evaluated the innate immune factors responsible for the contrasting SARS-CoV-2-associated disease outcomes in these mammalian species. A comparative transcriptomic analysis in primary respiratory epithelial cells of human (HRECs) and WTD (Deer-RECs) infected with the SARS-CoV-2 WA1/2020 strain was assessed throughout 48 h post inoculation (hpi). Both HRECs and Deer-RECs were susceptible to virus infection, with significantly (P < 0.001) lower virus replication in Deer-RECs. The number of differentially expressed genes (DEG) gradually increased in Deer-RECs but decreased in HRECs throughout the infection. The ingenuity pathway analysis of DEGs further identified that genes commonly altered during SARS-CoV-2 infection mainly belong to cytokine and chemokine response pathways mediated via interleukin-17 (IL-17) and nuclear factor-κB (NF-κB) signaling pathways. Inhibition of the NF-κB signaling in the Deer-RECs pathway was predicted as early as 6 hpi. The findings from this study could explain the lack of clinical signs reported in WTD in response to SARS-CoV-2 infection as opposed to the severe clinical outcomes reported in humans.IMPORTANCEThis study demonstrated that human and white-tailed deer primary respiratory epithelial cells are susceptible to the SARS-CoV-2 WA1/2020 strain infection. However, the comparative transcriptomic analysis revealed that deer cells could limit viral replication without causing hypercytokinemia by downregulating IL-17 and NF-κB signaling pathways. Identifying differentially expressed genes in human and deer cells that modulate key innate immunity pathways during the early infection will lead to developing targeted therapies toward preventing or mitigating the “cytokine storm” often associated with severe cases of coronavirus disease 19 (COVID-19). Moreover, results from this study will aid in identifying novel prognostic biomarkers in predicting SARS-CoV-2 adaption and transmission in deer and associated cervids.
In the present era, teachers must go beyond traditional lecture-based teaching methods to create engaging learning experiences and motivate students to actively participate in the educational process. Through classroom observations, it was identified that third-grade students at SDK Lemuel 1 school often experienced feelings of boredom and lacked enthusiasm during the learning sessions, consequently hindered their progress in mastering vocabulary. This study employed a qualitative descriptive approach to examine the effectiveness of incorporating learning media as a means of support for elementary students, as well as to comprehend the challenges faced by teachers when using video as a learning media in the classroom. The results of this study demonstrate a remarkable improvement in the average scores of students’ pre-test and post-test assessments, indicating an approximate increase of 20,63 points. Thus, it can be inferred that integrating the Little Fox Chinese video as a supplementary learning media for third-grade students at SDK Lemuel 1 school not only enhances their enthusiasm and active participation in the learning process, but also leads to improved learning outcomes and enhances their proficiency in mastering Mandarin vocabulary.
Herbson Ismael Honório Luz, Vitor Hugo Miro Couto Silva, Marcos Paulo Mesquita da Cruz
O Ceará é o maior produtor de caju, tanto de polpa (pedúnculo) quanto de castanha. Como toda atividade agrícola, a cajucultura tem riscos próprios (clima, preço, disponibilidade de insumos e produção sazonal) que demandam um olhar mais cuidadoso no planejamento e na condução da empresa agrícola. Uma das ferramentas para melhorar as tomadas de decisão por parte dos agentes econômicos da cadeia produtiva agrícola é o conhecimento das causas da flutuação dos preços dos produtos comercializados. Por meio de modelos de regressão linear simples, foram decompostas uma série de preços de castanha de caju em casca paga ao produtor, de janeiro de 2014 a setembro de 2021, e de preços de castanha de caju beneficiada pagos no atacado, de junho de 2014 a setembro de 2021. Os modelos de regressão para as duas séries de preços mostraram uma tendência de decrescimento. No entanto, o coeficiente linear dos preços ao produtor foi estatisticamente não significativo. Portanto, há tendência de estabilidade. A previsão de preços para setembro de 2022, com base nos modelos de regressão e levando em conta o fator sazonal, indica o valor de R$ 4,83/Kg para a castanha em casca (produtor). Para a castanha beneficiada, dado o fator de sazonalidade, o preço chegou a R$ 34,02/Kg.
Abstract Arctic amplification (AA), the larger warming of the Arctic compared to the rest of the planet, is widely attributed to the increasing concentrations of atmospheric CO2, and is caused by local and non-local mechanisms. In this study, we examine AA, and its seasonal cycle, in a sequence of abrupt CO2 forcing experiments, spanning from 1 to 8 times pre-industrial CO2 levels, using a state-of-the-art global climate model. We find that increasing CO2 concentrations give rise to stronger Arctic warming but weaker AA, owing to relatively weaker warming of the Arctic in comparison with the rest of the globe due to weaker sea-ice loss and atmosphere-ocean heat fluxes at higher CO2 levels. We further find that the seasonal peak in AA shifts gradually from November to January as CO2 increases. Finally, we show that this seasonal shift in AA emerges in the 21st century in high-CO2 emission scenario simulations. During the early-to-middle 21st century AA peaks in November–December but the peak shifts to December-January at the end of the century. Our findings highlight the role of CO2 forcing in affecting the seasonal evolution of amplified Arctic warming, which carries important ecological and socio-economic implications.
Francesco Saverio Santaga, Alberto Agnelli, Angelo Leccese
et al.
Soil-sample collection and strategy are costly and time-consuming endeavors, mainly when the goal is in-field variation mapping that usually requires dense sampling. This study developed and tested a streamlined soil mapping methodology, applicable at the field scale, based on an unsupervised classification of Sentinel-2 (S2) data supporting the definition of reduced soil-sampling schemes. The study occurred in two agricultural fields of 20 hectares each near Deruta, Umbria, Italy. S2 images were acquired for the two bare fields. After a band selection based on bibliography, PCA (Principal Component Analysis) and cluster analysis were used to identify points of two reduced-sample schemes. The data obtained by these samplings were used in linear regressions with principal components of the selected S2 bands to produce maps for clay and organic matter (OM). Resultant maps were assessed by analyzing residuals with a conventional soil sampling of 30 soil samples for each field to quantify their accuracy level. Although of limited extent and with a specific focus, the low average errors (Clay ± 2.71%, OM ± 0.16%) we obtained using only three soil samples suggest a wider potential for this methodology. The proposed approach, integrating S2 data and traditional soil-sampling methods could considerably reduce soil-sampling time and costs in ordinary and precision agriculture applications.
The problem of strengthening weak or overloaded bases is an important objective of underground space development. It is especially urgent if there are alternating weak layers in the base. The paper presents a practical geotechnical case of strengthening the overloaded base of a reinforced concrete foundation plate for a 25-storey residential building under construction. Combined soil piles that consist of Jet (type 1) soil concrete piles reinforced along the longitudinal axis with drilled injection piles made by electric discharge technology (EDT piles) are used as buried structures. This method of arrangement of a combined buried reinforced concrete structure is conditioned by the need to increase the load-bearing capacity of a pile in soil by two or more times.
The troposphere is one of the atmospheric layers where most weather phenomena occur. Temperature variations in the troposphere, especially at 500 hPa, a typical level of the middle troposphere, are significant indicators of future weather changes. Numerical weather prediction is effective for temperature prediction, but its computational complexity hinders a timely response. This paper proposes a novel temperature prediction approach in framework of physics-informed deep learning. The new model, called PGnet, builds upon a generative neural network with a mask matrix. The mask is designed to distinguish the low-quality predicted regions generated by the first physical stage. The generative neural network takes the mask as prior for the second-stage refined predictions. A mask-loss and a jump pattern strategy are developed to train the generative neural network without accumulating errors during making time-series predictions. Experiments on ERA5 demonstrate that PGnet can generate more refined temperature predictions than the state-of-the-art.