T.I. Laakso

Corrigendum to “Long-term neutralization capacity of vaccine and breakthrough infection induced SARS-CoV-2 specific antibodies against omicron subvariants BA.2, XBB.1.5, and JN.1” [Vaccine 68 (2025) 127894]

Arttu Reinholm, Hira Khan, T.I. Laakso, Sari Maljanen et autres

fi (code pays fourni par la source)

Long-term neutralization capacity of vaccine and breakthrough infection induced SARS-CoV-2 specific antibodies against omicron subvariants BA.2, XBB.1.5, and JN.1

Arttu Reinholm, Hira Khan, T.I. Laakso, Sari Maljanen et autres

The long-term effectiveness of COVID-19 vaccines remains a critical area of study, especially with the emergence of new SARS-CoV-2 subvariants. In this study, we investigated the long-term neutralization capacity of SARS-CoV-2 -specific antibodies induced by the third, fourth and fifth vaccine doses, …

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Wastewater network assets' lifespan prediction: can we modify the machine learning algorithms to fit the purpose?

Sina Masoumzadeh Sayyar, Matti Kummu, Ilkka Mellin, Franz Tscheikner-Gratl et autres

fi, no (code pays fourni par la source)

Comparative Study of AI-Based Methods—Application of Analyzing Inflow and Infiltration in Sanitary Sewer Subcatchments

Zhe Zhang, T.I. Laakso, Zeyu Wang, Seppo Pulkkinen et autres

Inflow and infiltration (I/I) is a common problem in sanitary sewer systems. The I/I rate is also considered to be an important indicator of the operational and structural condition of the sewer system. Situation awareness in sanitary sewer systems requires accurate wastewater-flow …

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Data-driven network asset management - Focus on sewer systems

T.I. Laakso

The public defense on 5th June 2020 at 12:00 will be available via remote technology. Link: https://aalto.zoom.us/j/64138791244 Zoom Quick Guide: https://www.aalto.fi/en/services/zoom-quick-guide

Sewer Life Span Prediction: Comparison of Methods and Assessment of the Sample Impact on the Results

T.I. Laakso, Teemu Kokkonen, Ilkka Mellin, Riku Vahala

Survival models can support the estimation of the resources needed for future renovations of sewer systems. They are particularly useful, when a large share of network will need renovation. This paper studies modelling sewer deterioration in a context, where data are available …

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A Data Driven Approach to Identify Rainfall Derived Inflow and Infiltration in Sanitary Sewer Sub-catchments

Z. Zhang, T.I. Laakso, Seppo Pulkkinen, Suvi Ahopelto et autres

Suomen vesihuoltoverkoston kunto ja saneeraustarpeen kehittyminen tulevaisuudessa

T.I. Laakso, Suvi Virta

Sewer Condition Prediction and Analysis of Explanatory Factors

T.I. Laakso, Teemu Kokkonen, Ilkka Mellin, Riku Vahala

Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors affect it. We apply the random forest …

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Tulevaisuuden kestävä vesihuolto – ennakointi, ohjaus ja järjestäminen

Kati Berninger, T.I. Laakso, Henri Paatela, Suvi Virta et autres

Vesihuollon tulee tarjota luotettavasti riittävä määrä puhdasta talousvettä ja puhdistaa se käytön jälkeen ympäristöhaitat minimoiden. Suomessa on noin 1500 vesihuoltolaitosta. Erityisesti pienten laitosten toiminnan laadun, resurssien ja osaamispohjan varmistamiseksi tarvitaan uusia ratkaisuja. Verkostosaneerauksesta on tulossa kaikkien vesihuoltolaitosten arkipäivää. Resurssien järkeväksi kohdentamiseksi laitokset …

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Estimating water and wastewater pipe failure consequences and the most detrimental failure modes

T.I. Laakso, Suvi Ahopelto, Tiia Lampola, Teemu Kokkonen et autres

Abstract Failures of water and wastewater networks can lead to severe consequences for the human, natural and built environments. This paper presents how data on networks and their immediate environment together with graph analysis can be used to estimate the severity of …

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Data utilization at finnish water and wastewater utilities: Current practices vs. state of the art

Jukka Sirkiä, T.I. Laakso, Suvi Ahopelto, Ossi Ylijoki et autres

fi (code pays fourni par la source)