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    <pubDate>Sat, 01 Aug 2026 01:21:34 GMT</pubDate>
    <dc:date>2026-08-01T01:21:34Z</dc:date>
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      <title>Crescimento Estocástico – um Grupo de Investigação nascido em Évora</title>
      <link>http://hdl.handle.net/10174/42215</link>
      <description>Title: Crescimento Estocástico – um Grupo de Investigação nascido em Évora
Authors: Braumann, Carlos A.
Editors: Fernando Rosado
Abstract: Artigo de divulgação</description>
      <pubDate>Sun, 01 Mar 2026 00:00:00 GMT</pubDate>
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      <dc:date>2026-03-01T00:00:00Z</dc:date>
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    <item>
      <title>Crescimento Estocástico – um Grupo de Investigação nascido em Évora</title>
      <link>http://hdl.handle.net/10174/42215</link>
      <description>Title: Crescimento Estocástico – um Grupo de Investigação nascido em Évora
Authors: Braumann, Carlos A.
Editors: Fernando Rosado
Abstract: Artigo de divulgação</description>
      <pubDate>Sun, 01 Mar 2026 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://hdl.handle.net/10174/42215</guid>
      <dc:date>2026-03-01T00:00:00Z</dc:date>
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      <title>Infectious disease epidemiology under meteorological factors: A review of mathematical models and an extended SEIR framework</title>
      <link>http://hdl.handle.net/10174/41644</link>
      <description>Title: Infectious disease epidemiology under meteorological factors: A review of mathematical models and an extended SEIR framework
Authors: Hari Subedi, Shiva; Alpizar-Jara, Russell; Bahadur Thapa, Gyan
Abstract: Mathematical  modeling  can  perform  a  deci-sive  task  in  understanding,  controlling,  and  preventingthe   transmission   of   infectious   diseases   by   forecastingtheir  spread,  estimating  the  effectiveness  of  interventionmeasures,  and  updating  public  health  policies.  A  math-ematical  epidemic  model  is  a  vital  tool  that  can  mockup   the   spread   of   infections   under   different   scenariosand environments, allowing researchers to test and refinetheir understanding of the fundamental mechanisms. Thispaper   attempts   to   review   some   existing   mathematicalcompartmental epidemic models and explore the impact ofmeteorological factors such as air temperature, humidity,and  wind  speed  on  epidemiology.  The  goal  is  to  identifyand  categorize  key  components,  research  trends,  majorfindings,  and  gaps  within  the  models.  Additionally,  thepaper  discusses  some  strategies  to  address  these  gapsand proposes a compartmental augmentation of the SEIRmodel  incorporating  meteorological  factors  for  furtherwork</description>
      <pubDate>Mon, 23 Jun 2025 23:00:00 GMT</pubDate>
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      <dc:date>2025-06-23T23:00:00Z</dc:date>
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    <item>
      <title>Report Fellowship POSDOC2/CIMA2023 May to October 2024</title>
      <link>http://hdl.handle.net/10174/41367</link>
      <description>Title: Report Fellowship POSDOC2/CIMA2023 May to October 2024
Authors: Antonino, Ficarra
Abstract: In this report, I briefly describe my activities since the start of the postdoc.</description>
      <pubDate>Mon, 01 Jan 2024 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">http://hdl.handle.net/10174/41367</guid>
      <dc:date>2024-01-01T00:00:00Z</dc:date>
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