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    <title>DSpace Communidade:</title>
    <link>https://repositorio.uema.br/jspui/handle/123456789/1878</link>
    <description />
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        <rdf:li rdf:resource="https://repositorio.uema.br/jspui/handle/123456789/6281" />
        <rdf:li rdf:resource="https://repositorio.uema.br/jspui/handle/123456789/6280" />
        <rdf:li rdf:resource="https://repositorio.uema.br/jspui/handle/123456789/6279" />
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    <dc:date>2026-07-09T01:21:41Z</dc:date>
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  <item rdf:about="https://repositorio.uema.br/jspui/handle/123456789/6281">
    <title>Uso do hábitat e padrão de estruturação das assembléias de aves Charadriiformes (Charadriidae, Scolopacidae e Laridae) na Ilha de São Luís, zona costeira maranhense</title>
    <link>https://repositorio.uema.br/jspui/handle/123456789/6281</link>
    <description>Título: Uso do hábitat e padrão de estruturação das assembléias de aves Charadriiformes (Charadriidae, Scolopacidae e Laridae) na Ilha de São Luís, zona costeira maranhense
Abstact: The coastal ecosystem of São Luís Island harbors a diverse array of habitats utilized by Charadriiform birds during parts of their life cycle. These habitats are selected during the overwintering period based on food availability, refuge from predators, and suitability as stopover sites during migration. This study investigated the structure, diversity, and habitat preferences of Charadriiform bird assemblages in the coastal zone of São Luís Island across four distinct areas—Porto do Itaqui, Praia do Cajueiro, Ferry Boat, and Canal da Raposa—using 500-meter transects surveyed on foot and by boat between March 2023 and July 2025. Direct counting and bioacoustic methods were employed for species identification, and comprehensive statistical analysis was conducted using Generalized Linear Mixed Models (GLMMs). A total of 31 species were recorded, eight of which are classified as threatened at the national level. The Raposa area exhibited the highest species richness (31), followed by Porto do Itaqui (22), Cajueiro (19), and Ferry (18). Regarding maximum abundance across the areas, *C. pusilla* showed the highest count (8,000) at Cajueiro, while *Calidris minutilla* showed the lowest count (2) at Raposa. Of the 36 models generated, the GLMM accounting for the interaction between area and habitat—including time as a factor and a zero-inflated component—was identified as the most competitive model for explaining bird diversity. Porto do Itaqui stood out in pairwise comparisons of diversity indices among the areas, demonstrating greater evenness between diversity and abundance. Regarding habitat preference, *C. pusilla* was primarily associated with "Lavado" (LAV) and mangrove (MAN) habitats, while *C. semipalmatus* and *A. interpres* preferred dune habitats with temporary lagoons (DLT). *C. cirrocephalus* in Sea and River (MR) and Sandy Intertidal Zone (ZIA) habitats; *P. simplex* in Beach (PRA) habitat; *L. atricilla*, *G. nilotica*, *S. superciliaris*, and *S. antillarum* in Sea and River (MR) habitats; *C. canutus*, *T. acuflavidus*, *S. hirundo*, and *R. niger* in Beach (PRA) and Sandy Intertidal Zone (ZIA) habitats. The results underscore the importance of coastal environments in Maranhão in the face of anthropogenic threats to habitat quality at migratory bird wintering and stopover sites, contributing relevant information to conservation policies for the study areas—such as the National Coastal Management Plan (PNGC) and the Coastal Management Law (Law No. 7.661/1988)—given that these habitats are crucial for population continuity during migration, particularly for species experiencing population declines, such as *C. pusilla*, *S. antillarum*, and *C. wilsonia*; the study also supports monitoring efforts for the National Action Plan (PAN) for the Conservation of Migratory Birds.</description>
    <dc:date>2026-02-02T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://repositorio.uema.br/jspui/handle/123456789/6280">
    <title>A gente reza quando vai batizar o boi: a resistência do Boi de Pindaré</title>
    <link>https://repositorio.uema.br/jspui/handle/123456789/6280</link>
    <description>Título: A gente reza quando vai batizar o boi: a resistência do Boi de Pindaré
Abstact: This study analyses different aspects of the Boi de Pindaré, a bumba-meu-boi group,&#xD;
such as community relations, forms of resistance, and state actions aimed at&#xD;
incorporating this cultural manifestation into the market through staged&#xD;
performances. It examines the group’s ritual phases and their connections with&#xD;
debates on tradition, customs, and identity. The study seeks to reinterpret categories&#xD;
and concepts drawn both from theory and from the vocabulary used by social actors,&#xD;
and to investigate internal dimensions intrinsic to the Boi, such as the sense of&#xD;
belonging and the idea of community, in order to understand the group’s internal&#xD;
dynamics. The methodology is based on bibliographical research as well as&#xD;
fieldwork, including interviews, direct observation, and photographic documentation&#xD;
of elements of the Boi de Pindaré.</description>
    <dc:date>2025-12-15T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://repositorio.uema.br/jspui/handle/123456789/6279">
    <title>Estimativas de arrecadação do ICMS do Estado do Maranhão usando algoritmos de machine learning</title>
    <link>https://repositorio.uema.br/jspui/handle/123456789/6279</link>
    <description>Título: Estimativas de arrecadação do ICMS do Estado do Maranhão usando algoritmos de machine learning
Abstact: Tax collection forecasting is a cornerstone of fiscal planning and efficient public&#xD;
management. The Tax on Circulation of Goods and Services (ICMS) constitutes the&#xD;
main source of revenue for Brazilian states, and its accurate projection is crucial&#xD;
for allocating resources to strategic areas. However, the complexity of its dynamics,&#xD;
influenced by non-linear macroeconomic variables, and the lack of studies applied&#xD;
to the reality of the state of Maranhão pose a challenge for public administrators.&#xD;
This work aims to address this gap by investigating how machine learning&#xD;
techniques can improve the accuracy of forecasting monthly ICMS revenue in&#xD;
Maranhão. The overall objective is to develop and validate advanced computational&#xD;
models using a historical series of economic and social data from January 1997&#xD;
to April 2024. This quantitative and applied research adopted the CRISP-DM&#xD;
framework. Data were collected from public sources such as SEFAZ-MA, IBGE,&#xD;
and the Central Bank. Initially, nineteen independent variables were considered,&#xD;
and a Multiple Linear Regression model was used to select the most relevant ones,&#xD;
such as GDP, diesel consumption, and electricity consumption indicators. Four&#xD;
machine learning algorithms were implemented, compared, and validated:&#xD;
Random Forest, Decision Tree, Linear Regression, and XGBoost. Performance&#xD;
evaluation was performed using the RMSE, MAE, MAPE, SMAPE, and R² metrics,&#xD;
using the k-fold cross-validation technique (with k=10) and a data split of 80% for&#xD;
training and 20% for testing. This study contributes a practical and validated&#xD;
model that can be integrated into the state's budget planning process, promoting&#xD;
more transparent, efficient, and data-driven fiscal management</description>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://repositorio.uema.br/jspui/handle/123456789/6278">
    <title>Detecção de parasitas do gênero Trypanosoma em pequenos ruminantes no norte maranhense, Brasil</title>
    <link>https://repositorio.uema.br/jspui/handle/123456789/6278</link>
    <description>Título: Detecção de parasitas do gênero Trypanosoma em pequenos ruminantes no norte maranhense, Brasil
Abstact: Animal trypanosomiasis represents a significant sanitary and economic challenge for tropical livestock production, causing severe anemia and systemic inflammation in ruminants. In Brazil, Trypanosoma vivax is the most clinically relevant species in goats and sheep, associated with high morbidity, mortality, and production losses, though infections may also occur asymptomatically. While T. evansi and T. theileri can infect small ruminants, their pathogenicity in these hosts is considered low. In Maranhão state, the complete absence of studies on trypanosomiasis in small ruminants represents a critical epidemiological gap, leaving herds vulnerable to undiagnosed outbreaks. This study aimed to detect Trypanosoma spp. in small ruminants across Maranhão using direct and indirect diagnostic methods. Blood samples were collected from 84 goats and 210 sheep across 14 farms in São Bento, Pinheiro, and Itapecuru-Mirim municipalities during both dry and rainy seasons. A structured questionnaire documented risk factors, and individual animal records included demographic and health data. Diagnostic approaches included direct microscopy of blood smears, indirect ELISA for anti-T. vivax antibodies, species-specific PCR targeting T. vivax, T. evansi, and T. theileri genomic fragments, sequencing and phylogenetic analysis of PCR-positive samples (BLAST against GenBank), and statistical analysis using Chi-square and Fisher's exact tests for risk factors. No trypanosomes were detected by microscopy. Seroprevalence reached 14.28% (42/294), with 30 (10.20%) goats and 12 (4.08%) sheep testing positive. Two samples (0.68%) were PCR-positive for T. vivax, showing 100% identity with Brazilian isolates from buffaloes, cattle, small ruminants, and donkeys in GenBank. Phylogenetic analysis confirmed homology with these T. vivax strains. All samples tested negative for T. evansi and T. theileri. Significant risk factors (p&lt;0.05) included host species (higher in goats), recent animal acquisitions, veterinary care availability, farm infrastructure quality, and housing type. This study provides the first molecular evidence of T. vivax infection in Maranhão's small ruminants, highlighting the need for control measures. The findings expand understanding of animal trypanosomiasis in Brazil and underscore the vulnerability of small ruminant production systems to this neglected disease.</description>
    <dc:date>2025-06-09T00:00:00Z</dc:date>
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