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628 results on '"Koivisto, M"'

11. Postoperative pain management with transdermal fentanyl after forefoot surgery: a randomized, placebo-controlled study

Author

Merivirta R, Pitkänen M, Alanen J, Haapoja E, Koivisto M, and Kuusniemi K

Subjects
Medicine (General), R5-920
Abstract

Riika Merivirta,1 Mikko Pitkänen,2 Jouko Alanen,3 Elina Haapoja,1 Mari Koivisto,4 Kristiina Kuusniemi11Department of Anaesthesiology, Intensive Care, Emergency Care and Pain Medicine of Turku University Hospital and University of Turku, Turku, 2Department of Anaesthesia, Hospital Orton, Invalid Foundation, Helsinki, 3Terveystalo Clinic Hospital, Helsinki, 4Department of Biostatistics, University of Turku, Turku, FinlandBackground: Quality of life is decreased in patients with hallux valgus deformity, mainly because of pain. Significant improvement is usually achieved by surgery. However, postoperative pain can be moderate to severe for 2–3 days. The aim of the present study was to evaluate the use of transdermal fentanyl for postoperative pain management after forefoot surgery.Methods: Sixty patients undergoing hallux valgus or hallux rigidus surgery were allocated to receive a patch delivering either fentanyl 12 µg/hour or placebo for postoperative pain. The consumption of rescue opioid oxycodone, the primary outcome measure, was evaluated daily until the fourth postoperative day. Total consumption of oxycodone during the study period was also assessed. Pain scores and possible adverse effects were evaluated every 6 hours during the first 24 hours and on the fourth postoperative day.Results: The use of rescue opioid was low in both groups, the median (range) consumption of oxycodone being 10 (0–50) mg on the day of surgery (no difference between the groups, P=0.31) and 0 (0–35) mg thereafter. The total combined consumption was 10 (0–105) mg in the fentanyl group and 20 (0–70) mg in the placebo group (P=0.23). There were no statistically significant differences in pain scores or adverse effects between the groups.Conclusion: As a part of multimodal analgesia with ibuprofen and acetaminophen, a patch delivering fentanyl 12 µg/hour did not significantly decrease the consumption of rescue opioid or pain scores after forefoot surgery.Keywords: pain management, transdermal fentanyl, multimodal analgesia, forefoot surgery, hallux valgus, hallux rigidus

Published
2015

14. Functional human cell-based vascularised cardiac tissue model for biomedical research and testing

Author

Koivisto, M. (Maria), Tolvanen, T. A. (Tuomas A.), Toimela, T. (Tarja), Miinalainen, I. (Ilkka), Kiviaho, A. (Antti), Kesseli, J. (Juha), Nykter, M. (Matti), Eklund, L. (Lauri), Heinonen, T. (Tuula), Koivisto, M. (Maria), Tolvanen, T. A. (Tuomas A.), Toimela, T. (Tarja), Miinalainen, I. (Ilkka), Kiviaho, A. (Antti), Kesseli, J. (Juha), Nykter, M. (Matti), Eklund, L. (Lauri), and Heinonen, T. (Tuula)

Abstract

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC) are widely used in in vitro biomedical research and testing. However, fully matured, adult cardiomyocyte characteristics have not been achieved. To improve the maturity and physiological relevance of hiPSC-derived cardiomyocytes, we co-cultured them with preconstructed vascular-like networks to form a functional, human cell-based cardiac tissue model. The morphology and gene expression profiles indicated advanced maturation in the cardiac tissue model compared to those of a cardiomyocyte monoculture. The cardiac tissue model’s functionality was confirmed by measuring the effects of 32 compounds with multielectrode array and comparing results to human data. Our model predicted the cardiac effects with a predictive accuracy of 91%, sensitivity of 90% and specificity of 100%. The correlation between the effective concentration (EC50) and the reported clinical plasma concentrations was 0.952 (R2 = 0.905). The developed advanced human cell-based cardiac tissue model showed characteristics and functionality of human cardiac tissue enabling accurate transferability of gained in vitro data to human settings. The model is standardized and thus, it would be highly useful in biomedical research and cardiotoxicity testing.

Published
2022

15. Alcohol use in adolescence as a risk factor for overdose in the 1986 Northern Finland Birth Cohort Study

Author

Koivisto, M. K. (Maarit K.), Miettunen, J. (Jouko), Levola, J. (Jonna), Mustonen, A. (Antti), Alakokkare, A.-E. (Anni-Emilia), Salom, C. L. (Caroline L.), Niemelä, S. (Solja), Koivisto, M. K. (Maarit K.), Miettunen, J. (Jouko), Levola, J. (Jonna), Mustonen, A. (Antti), Alakokkare, A.-E. (Anni-Emilia), Salom, C. L. (Caroline L.), and Niemelä, S. (Solja)

Abstract

Background: Overdoses and poisonings are among the most common causes of death in young adults. Adolescent problem drinking has been associated with psychiatric morbidity in young adulthood as well as with elevated risk for suicide attempts. There is limited knowledge on adolescent alcohol use as a risk factor for alcohol and/or drug overdoses in later life. Methods: Here, data from The Northern Finland Birth Cohort 1986 study with a follow-up from adolescence to early adulthood were used to assess the associations between adolescent alcohol use and subsequent alcohol or drug overdose. Three predictors were used: age of first intoxication, self-reported alcohol tolerance and frequency of alcohol intoxication in adolescence. ICD-10-coded overdose diagnoses were obtained from nationwide registers. Use of illicit drugs or misuse of medication, Youth Self Report total score, family structure and mother’s education in adolescence were used as covariates. Results: In multivariate analyses, early age of first alcohol intoxication [hazard ratios (HR) 4.5, 95% confidence intervals (CI) 2.2–9.2, P < 0.001], high alcohol tolerance (HR 3.1, 95% CI 1.6–6.0, P = 0.001) and frequent alcohol intoxication (HR 1.9, 95% CI 1.0–3.4, P = 0.035) all associated with the risk of overdoses. Early age of first intoxication (HR 5.2, 95% CI 1.9–14.7, P = 0.002) and high alcohol tolerance (HR 4.4, 95% CI 1.7–11.5, P = 0.002) also associated with intentional overdoses. Conclusions: Alcohol use in adolescence associated prospectively with increased risk of overdose in later life. Early age of first intoxication, high alcohol tolerance and frequent alcohol intoxication are all predictors of overdoses.

Published
2022

16. Addressing Market Issues in Electrical Power Systems with Large Shares of Variable Renewable Energy

Author

Korpas, M., Holttinen, H., Helisto, N., Girard, R., Koivisto, M., Frew, B., Dobschinski, J., Smith, J. C., Vrana, T. K., Flynn, D., Orths, A., Söder, Lennart, Korpas, M., Holttinen, H., Helisto, N., Girard, R., Koivisto, M., Frew, B., Dobschinski, J., Smith, J. C., Vrana, T. K., Flynn, D., Orths, A., and Söder, Lennart

Abstract

This paper reports recent findings from IEA Wind TCP Task 25, which compiles international experiences and research related to large-scale integration of wind and other renewable energy. In the paper, we address the main challenges for market integration of variable renewable energy, relating to price formation, cost recovery, balancing and other grid services. The paper gives an overview of recent scenario studies on electricity price impacts of (1) various generation, energy storage and demand types in different markets, and (2) different market designs and energy/climate policies. Studying markets with very high shares of variable renewable energy requires an improved set of analysis tools for forecasting market outcomes, estimating flexibility needs and sources, and assessing resource adequacy. Key market features need to be investigated within these improved analytical capabilities for systems transitioning to high shares of variable renewable energy, storage and flexible demand. System services that can be supported by markets will likely need to be revisited. Finally, this paper identifies open questions and suggested future market design work for supporting systems with very high shares of variable renewable energy, which are to be addressed in follow-up work of Task 25 collaborative research., QC 20230613

Published
2022
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17. Flexibility from the Electrification of Energy : How Heating, Transport, and Industries Can Support a 100% Sustainable Energy System

Author

Kiviluoma, J., Helisto, N., Putkonen, N., Smith, C., Koivisto, M., Korpas, M., Flynn, D., Söder, Lennart, Taibi, E., Guminski, A., Kiviluoma, J., Helisto, N., Putkonen, N., Smith, C., Koivisto, M., Korpas, M., Flynn, D., Söder, Lennart, Taibi, E., and Guminski, A.

Abstract

Energy production and consumption activities account for 76% of global greenhouse gas (GHG) emissions, according to the Historical GHG Emissions database by Climate Watch. Power and heat plants cause 42% of energy-related GHG emissions, while other sectors, like transport, other energy use in buildings, and industry, cause 22%, 8%, and 24% of energy-related GHG emissions, respectively., QC 20230412

Published
2022
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18. Very high spatial resolution soil moisture observation of heterogeneous subarctic catchment using nonlocal averaging and multitemporal SAR data

Author

Manninen, T. (Terhikki), Jääskeläinen, E. (Emmihenna), Lohila, A. (Annalea), Korkiakoski, M. (Mika), Räsänen, A. (Aleksi), Virtanen, T. (Tarmo), Muhić, F. (Filip), Marttila, H. (Hannu), Ala-Aho, P. (Pertti), Markovaara-Koivisto, M. (Mira), Liwata-Kenttälä, P. (Pauliina), Sutinen, R. (Raimo), Hänninen, P. (Pekka), Manninen, T. (Terhikki), Jääskeläinen, E. (Emmihenna), Lohila, A. (Annalea), Korkiakoski, M. (Mika), Räsänen, A. (Aleksi), Virtanen, T. (Tarmo), Muhić, F. (Filip), Marttila, H. (Hannu), Ala-Aho, P. (Pertti), Markovaara-Koivisto, M. (Mira), Liwata-Kenttälä, P. (Pauliina), Sutinen, R. (Raimo), and Hänninen, P. (Pekka)

Abstract

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination ( R2 ) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.

Published
2022

19. Evaluating the mesoscale spatio-temporal variability in simulated wind speed time series over Northern Europe

Author

Luzia, G., Hahmann, A. N., Koivisto, M. J., Luzia, G., Hahmann, A. N., and Koivisto, M. J.

Abstract

As wind energy increases its share of total electricity generation and its integration into the power system becomes more challenging, accurately representing the spatio-temporal variability in wind data becomes crucial. Wind fluctuations impact power and energy systems, e.g., energy system planning, vulnerability to storm shutdowns, and available voltage stability support. To analyze such fluctuations and their spatio-temporal dependencies, time series of wind speeds at hourly time-frequency or higher are needed. We provide a comprehensive evaluation of the global and mesoscale-model derived wind time series against observations by using a set of metrics that we present as requirements for wind energy integration studies. We also perform a sensitivity analysis to find the best model setup of the Weather Research and Forecasting (WRF) model, focusing on evaluating the wind speed fluctuation metrics. The results show that using higher spatial resolution in the WRF model simulations improves the representation of temporal fluctuations; however, higher-resolution simulations often lower the correlations of wind time series with measurements. We also show that the nesting strategy is an important consideration, and a smoother transition from the forcing data to the nested domains improves the correlations with measurements. All mesoscale model simulations overestimate the value of the spatial correlations in wind speed with respect to their observed values. Still, the spatial correlations and the wind speed distributions are insensitive to the model configuration tested in this study.

Published
2022

22. Unconsciously Guided Behavior With a Bias-Free Measurement of Consciousness

Author

Kangassalo and Koivisto M

Subjects
Cognitive science, media_common.quotation_subject, Consciousness, Psychology, media_common
Abstract

Many researchers believe that unconscious, invisible stimuli can guide behaviour, but convincing evidence for this phenomenon is lacking. The controversy results from the difficulty of defining and measuring consciousness in an unbiased way. We utilized a bias-free 2-interval forced-choice (2IFC) paradigm to study whether orientation or color of unconscious, masked stimuli can guide behaviour. Each trial consisted of two intervals, only one of which contained the target stimulus. The observers were forced to discriminate the orientation or color of the stimulus in each interval, and in the end of the trial they made a 2IFC decision on which one of the intervals the stimulus was present. We focused on the trials in which the 2IFC decision was made incorrectly, suggesting unconsciousness of the presence of the target. In masked trials both orientation and color was discriminated with higher accuracy than expected by chance. Control trials showed that the participants followed the instructions and performed at ceiling in the 2IFC task, indicating that the specific non-perceptual cognitive requirements of the 2IFC task cannot explain the incorrect 2IFC decisions. The present results provide strong evidence for unconsciously guided behaviour. Further studies should examine the constraints of this phenomenon with the unbiased procedure.

Published
2021
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32. Effect of hydrolyzed infant formula vs conventional formula on risk of type 1 diabetes the TRIGR randomized clinical trial

Author

Knip M., Akerblom H. K., Altaji E., Becker D., Bruining J., Castano L., Danne T., De Beaufort C., Dosch H. -M., Dupre J., Fraser W. D., Howard N., Ilonen J., Konrad D., Kordonouri O., Krischer J. P., Lawson M. L., Ludvigsson J., Madacsy L., Mahon J. L., Ormisson A., Palmer J. P., Pozzilli P., Savilahti E., Serrano-Rios M., Songini M., Taback S., Vaarala O., White N. H., Virtanen S. M., Wasikowa R., Mandrup-Poulsen T., Arjas E., Lernmark A., Laara E., Schmidt B., Hyytinen M., Koski K., Koski M., Merentie K., Pajakkala E., Reunanen A., Salonen M., Terhonen T., Virkkunen S., Cuthbertson D., Gainer B., Hadley D., Malloy J., Nallamshetty L., Shanker L., Bradley B., Lough G., Fraser W., Sermer M., Taback S. P., Franciscus M., Nucci A., Palmer J., Alahuhta K., Barlund S., Korhonen T., Kovanen L., Lehtonen E., Niinisto S., Pekkala M., Sorkio S., Toivanen L., Vahatalo L., Uusitalo U., Ohman T., Bongiorno R., Catteau J., Fraser G., Lloyd M., Crock P., Giles M., Siech K., See D. W., Brown C., Craig M., Johnston A., Bere L. J., Clarson C. L., Jenner M., McManus R., Renato N., Lovell M., Higo D., Kent N., Kwan J., Marshall C., Metzger D., Chanoine J. -P., Stewart L., Thompson D., Edwards A., Lange I., Mercer J., Pacaud D., Josephine H., Schwarz W., Stephure D. K., Boer J., Chatur T., Chick C., Couch B., Demianczuk N., Girgis R., Marks S., Ryan E., Thompson M., Dean H. J., Grant L., Hamelin K., LaForte J., Murphy L., Catte D., Schneider C., Sellers E. A. C., Woo V., Boland A., Clark H. D., Cooper T., Gruslin A., Karovitch A., Keely E., Malcolm J. C., Sauro V., Tawagi G. F., Andrighetti S., Arnold G., Barrett J., Blumer I., Daneman D., Donat D., Ehrlich R., Feig D., Gottesman I., Gysler M., Karkanis S., Kenshole A., Knight B., Lackie E., Lewis V., Martin M. J., Maxwell C., Oliver G., Panchum P., Shilletto N., Simone A., Skidmore M., Turrini T., Wong S., Allen C., Belanger L., Bouchard I., Ferland S., Frenette L., Garrido-Russo M., Leblanc M., Imbeault J., Morin V., Olivier G., Weisnagell J., Costain G., Dornan J., Heath K., MacSween M. -C., McGibbon A., Ramsay C., Sanderson F., Sanderson S., Benabdesselam L., Gonthier M., Huot C., Thibeault M., Laforte D., Legault L., Perron P., Armson A., Canning P., Cummings E. A., Ivanko V., McLeod L., Mokashi A., Scott K., Bridger T., Crane J., Crummell C., Curtis J. C., Dawson C., Joyce C., Newhook L. A., Newman S., Druken E., Begum-Hasan J., Breen A., Houlden R., Woods M., Carrson G., Kelly S., Martel M. J., Penner M., Sankaran K., Hardy-Brown K., King N., White R. A., Park M., Popkin J., Robson L., Coles K., Al Taji E., Cerna M., Cerny M., Francova H., Hainerova I., Kothankova H., Koukalova R., Krakorova V., Mendlova P., Sitova R., Stechova K., Vavrinec J., Vosahlo J., Zlatohlavkova B., Brazdova L., Faksova P., Gregorova D., Kantor L., Malkova K., Venhacova J., Venhacova P., Cipra A., Skvor J., Budejovice C., Tomsikova Z., Botkova-Krauseova H., Mockova A., Paterova P., Gogelova P., Kandrnalova J., Einberg U., Jakovlev U., Posiadlo S., Rannaste E., Raukas R., Riikjarv M. -A., Valla K., Astover V., Kirss A., Retpap J., Taht E., Tillmann V., Vahtra S., Heikkila M., Hirvasniemi M., Luopajarvi K., Johansson S., Kleemola P., Laukkanen E., Parkkola A., Pigg H. -M., Puttonen H., Renlund M., Salonen K., Suomalainen H., Tenkula T., Teramo K., Jarvenpaa A. -L., Hamalainen A. -M., Jussila R., Kiiveri S., Haavisto H., Holopainen S., Kupiainen H., Leeve T., Lumme K., Nironen T., Tenhola S., Tiilikainen T., Keinonen H., Lautala P., Salonen P., Vesanto M., Aspholm A. S., Asunta P., Ikavalko H., Jason E., Jaminki S., Kekki P., Koskinen M., Lehtimaki S., Lahde J., Makela M., Peltoniemi S., Poutiainen L., Ranta K., Salonsaari T., Sarviharju-Tujula S. -L., Selvenius J., Siljander H., Haanpaa P. -L., Holm C., Juutilainen A., Jarvelainen V., Kangaskolkka-Keskilohko A. -M., Laino E., Marjamaki L., Suominen E., Ylitalo S., Hokkanen M., Lounamaa R., Matikainen M., Nuuja A., Paalanen I., Puupponen A. R., Salo-Edwards H., Alanne S., Kultti T., Linjama H., Muhonen K., Vaaraniemi M., Talvitie T., Backman M., Hanhijarvi R., Koivula P., Lindstrom K., Martikainen A., Nurmi P., Bjork A., Huopio H., Komulainen J., Lehtomaki S., Muikku E., Pesola J., Sankilampi U., Arkkola T., Hekkala A., Jurvakainen S., Koivikko M. -L., Kahonen M., Leinonen E., Mykkanen T., Pohjola H., Riikonen K., Niittyvuopio A., Stenius A., Tapanainen P., Veijola R., Alar A., Jovio S., Korpela P., Makinen E., Hietanen L., Kivisto J., Kaar M. -L., Lehtimaki P., Mustila T., Popov E., Saatela S., Taittonen L., Ahtiainen K., Laaksonen N., Luoto M., Viitala J., Virransalo R., Nykanen P., Paajanen S., Parkkinen S., Pyrhonen H., Sarkka T., Aschemeier B., Bektas S., Biester T., Datz N., Deiss D., Fath M., Lupke K., Muller B., Nestoris C., Rothes S., Sadeghian E., Semler K., Arato A., Krikovszky D., Nobilis A., Szenasi J., Benevento D., Anguissola G. B., Biagioni M., Bizzarri C., Cherubini V., Ferrito L., Giordano C., Giorgetti C., Khazrai Y. M., Kyanvash S., Maddaloni E., Napoli A., Piergiovanni F., Pitocco D., Suraci T., Tabacco G., Valente L., Visalli N., Carboni M. B., Cavallo R., Cau V., Isola C., Ledda A., Loddo M., Mannu C., Pettinau M., Pisano S., Porceddu M., Putzu C., Rita A., Peters D., Schierloh U., Bisschoff M., Blonk L., Lappenschaar T., Manai B., Seesink M., Sperling-Conrad M., Verhagen M., Zoethout J. A., Basiak A., Chalas M., Chesiak M., Gramza A., Iwankiewicz J., Sieradzan E., Wikiera B., Ciechanowska M., Dziatkowiak H., Futona B., Gorska A., Glowacka-Wony M., Kaim I., Klich B., Starzyk J., Wolanin M., Tokarska L., Chucherco D., Deja G., Firek-Pedras M., Jarosz-Chobot P., Kalina M., Kutrowska-Adamusiak K., Minkina-Pedras M., Muchaka-Bianga M., Bodalski J., Mlynarski W., Szadkowska A., Cieslak A., Cypryk K., Dziatosz K., Jastzebowska J., Krysiak A., Szymanska U., Wilcznski J., Zawodniak-Szalapska M., Aguay A., Bilbao J. R., Chueca M., Cortazar A., Echarte G., Frutos T. G., Jimenez P., Martul P., Moreno A., Oyarzabal M., Rica I., Salgado Y., Martinez-Larrad M. T., Hawkins F. G., Hernandez R., Herranz L., Pallardo L. F., Deibarra L. S., Fernandez B. H., Luis J. L., Ortiz-Quintana L., Recarte P. P., Arnau D. R., Aronsson L., Boden S., Fredriksson J., Isacsson E., Johansson I., Karlsson E., Lock C., Sandstrom A. -M., Konefal M. S., Andreasson C., Dahlstrom U., Hanas R., Lundqvist K., Windell L., Jansson I., Karlsson A. -K., Lindbladh B., Odenman I., Pettersson C., Sundberg F., Sundqvist M., Aronsson S., Bellman I., Bengtsson A. -B., Lyden G. -B., Nilsson N. -O., Soderblom M., Unt C., Augustsson M., Bengtsson M., Fors H., Helmrich A., Johansson T. O., Andersson A. -C., Boiard-Stomlid A., Hellgren G., Kallsholm H., Lindqvist J., Nilsson M., Nordwall M., Stromstedt C., Ahsberg C., Lindh A., Lindhe C., Samuelsson C., Wiik A., Edenwall H., Ljumgcrantz M., Persson I. -B., Strigard E., Svensson B. -L., Aman J., Breivik G. -E., Detlofsson I. -L., Kroon M., Sarnblad S., Johansson C., Ilvered R., Lundberg A., Akesson K., Beccarelli A., Gadient M., Rappold-Amrein C., Schoenle E., Daftary A., Damagro-Elias M. E., Gilmour C., Klein M. B., Lain C., Salerno D., Smith M. E., Vats K., Pfaff D. J., Malone P., Mansfield P., Munns M., Nickel K., Pompilio K., Siemion W., Taculad R., Van Horn K., Zdanadewic M., Chambliss C., Jones J., Sadler M., Tanner-Blasiar M., Bell C., Camper N., Devaskar S., Devaskar U., Horowitz H., Rogers L., Shannahan R., Silk K., Bermudez Z., Colon R., Frazer T., Martinez-Nieves B., Torres J., Vega J., Chan M., Cook S., Goland R., Greenberg E., Jules N., Montes J., Nelson M., Parra-Valencia Z., Schachner H., Softness B., Kiviniemi M., Suomenin R., Alexander A., Hyrckowian E., Nichol L., Trucco M., Karjalainen E., Louhio T., Sarnesto A., Valtonen E., Davydova B., Helander S., Hamalainen J., Harkonen T., Joutsjoki L., Kararic M., Latva-Koivisto M., Lonn E., Nurmi T., Ollila I., Rinkinen J., Ronkainen M., Tukiainen H., Cederlof A., Kiikeri M., Tsupari S., Cheng R., Bryant K., Chan Y., Maezawa Y., Paltser G., Rasavi R., Tsui H., Winer S., Wu P., Yantha J., Pediatrics, Knip M., Akerblom H.K., Altaji E., Becker D., Bruining J., Castano L., Danne T., De Beaufort C., Dosch H.-M., Dupre J., Fraser W.D., Howard N., Ilonen J., Konrad D., Kordonouri O., Krischer J.P., Lawson M.L., Ludvigsson J., Madacsy L., Mahon J.L., Ormisson A., Palmer J.P., Pozzilli P., Savilahti E., Serrano-Rios M., Songini M., Taback S., Vaarala O., White N.H., Virtanen S.M., Wasikowa R., Mandrup-Poulsen T., Arjas E., Lernmark A., Laara E., Schmidt B., Hyytinen M., Koski K., Koski M., Merentie K., Pajakkala E., Reunanen A., Salonen M., Terhonen T., Virkkunen S., Cuthbertson D., Gainer B., Hadley D., Malloy J., Nallamshetty L., Shanker L., Bradley B., Lough G., Fraser W., Sermer M., Taback S.P., Franciscus M., Nucci A., Palmer J., Alahuhta K., Barlund S., Korhonen T., Kovanen L., Lehtonen E., Niinisto S., Pekkala M., Sorkio S., Toivanen L., Vahatalo L., Uusitalo U., Ohman T., Bongiorno R., Catteau J., Fraser G., Lloyd M., Crock P., Giles M., Siech K., See D.W., Brown C., Craig M., Johnston A., Bere L.J., Clarson C.L., Jenner M., McManus R., Renato N., Lovell M., Higo D., Kent N., Kwan J., Marshall C., Metzger D., Chanoine J.-P., Stewart L., Thompson D., Edwards A., Lange I., Mercer J., Pacaud D., Josephine H., Schwarz W., Stephure D.K., Boer J., Chatur T., Chick C., Couch B., Demianczuk N., Girgis R., Marks S., Ryan E., Thompson M., Dean H.J., Grant L., Hamelin K., LaForte J., Murphy L., Catte D., Schneider C., Sellers E.A.C., Woo V., Boland A., Clark H.D., Cooper T., Gruslin A., Karovitch A., Keely E., Malcolm J.C., Sauro V., Tawagi G.F., Andrighetti S., Arnold G., Barrett J., Blumer I., Daneman D., Donat D., Ehrlich R., Feig D., Gottesman I., Gysler M., Karkanis S., Kenshole A., Knight B., Lackie E., Lewis V., Martin M.J., Maxwell C., Oliver G., Panchum P., Shilletto N., Simone A., Skidmore M., Turrini T., Wong S., Allen C., Belanger L., Bouchard I., Ferland S., Frenette L., Garrido-Russo M., Leblanc M., Imbeault J., Morin V., Olivier G., Weisnagell J., Costain G., Dornan J., Heath K., MacSween M.-C., McGibbon A., Ramsay C., Sanderson F., Sanderson S., Benabdesselam L., Gonthier M., Huot C., Thibeault M., Laforte D., Legault L., Perron P., Armson A., Canning P., Cummings E.A., Ivanko V., McLeod L., Mokashi A., Scott K., Bridger T., Crane J., Crummell C., Curtis J.C., Dawson C., Joyce C., Newhook L.A., Newman S., Druken E., Begum-Hasan J., Breen A., Houlden R., Woods M., Carrson G., Kelly S., Martel M.J., Penner M., Sankaran K., Hardy-Brown K., King N., White R.A., Park M., Popkin J., Robson L., Coles K., Al Taji E., Cerna M., Cerny M., Francova H., Hainerova I., Kothankova H., Koukalova R., Krakorova V., Mendlova P., Sitova R., Stechova K., Vavrinec J., Vosahlo J., Zlatohlavkova B., Brazdova L., Faksova P., Gregorova D., Kantor L., Malkova K., Venhacova J., Venhacova P., Cipra A., Skvor J., Budejovice C., Tomsikova Z., Botkova-Krauseova H., Mockova A., Paterova P., Gogelova P., Kandrnalova J., Einberg U., Jakovlev U., Posiadlo S., Rannaste E., Raukas R., Riikjarv M.-A., Valla K., Astover V., Kirss A., Retpap J., Taht E., Tillmann V., Vahtra S., Heikkila M., Hirvasniemi M., Luopajarvi K., Johansson S., Kleemola P., Laukkanen E., Parkkola A., Pigg H.-M., Puttonen H., Renlund M., Salonen K., Suomalainen H., Tenkula T., Teramo K., Jarvenpaa A.-L., Hamalainen A.-M., Jussila R., Kiiveri S., Haavisto H., Holopainen S., Kupiainen H., Leeve T., Lumme K., Nironen T., Tenhola S., Tiilikainen T., Keinonen H., Lautala P., Salonen P., Vesanto M., Aspholm A.S., Asunta P., Ikavalko H., Jason E., Jaminki S., Kekki P., Koskinen M., Lehtimaki S., Lahde J., Makela M., Peltoniemi S., Poutiainen L., Ranta K., Salonsaari T., Sarviharju-Tujula S.-L., Selvenius J., Siljander H., Haanpaa P.-L., Holm C., Juutilainen A., Jarvelainen V., Kangaskolkka-Keskilohko A.-M., Laino E., Marjamaki L., Suominen E., Ylitalo S., Hokkanen M., Lounamaa R., Matikainen M., Nuuja A., Paalanen I., Puupponen A.R., Salo-Edwards H., Alanne S., Kultti T., Linjama H., Muhonen K., Vaaraniemi M., Talvitie T., Backman M., Hanhijarvi R., Koivula P., Lindstrom K., Martikainen A., Nurmi P., Bjork A., Huopio H., Komulainen J., Lehtomaki S., Muikku E., Pesola J., Sankilampi U., Arkkola T., Hekkala A., Jurvakainen S., Koivikko M.-L., Kahonen M., Leinonen E., Mykkanen T., Pohjola H., Riikonen K., Niittyvuopio A., Stenius A., Tapanainen P., Veijola R., Alar A., Jovio S., Korpela P., Makinen E., Hietanen L., Kivisto J., Kaar M.-L., Lehtimaki P., Mustila T., Popov E., Saatela S., Taittonen L., Ahtiainen K., Laaksonen N., Luoto M., Viitala J., Virransalo R., Nykanen P., Paajanen S., Parkkinen S., Pyrhonen H., Sarkka T., Aschemeier B., Bektas S., Biester T., Datz N., Deiss D., Fath M., Lupke K., Muller B., Nestoris C., Rothes S., Sadeghian E., Semler K., Arato A., Krikovszky D., Nobilis A., Szenasi J., Benevento D., Anguissola G.B., Biagioni M., Bizzarri C., Cherubini V., Ferrito L., Giordano C., Giorgetti C., Khazrai Y.M., Kyanvash S., Maddaloni E., Napoli A., Piergiovanni F., Pitocco D., Suraci T., Tabacco G., Valente L., Visalli N., Carboni M.B., Cavallo R., Cau V., Isola C., Ledda A., Loddo M., Mannu C., Pettinau M., Pisano S., Porceddu M., Putzu C., Rita A., Peters D., Schierloh U., Bisschoff M., Blonk L., Lappenschaar T., Manai B., Seesink M., Sperling-Conrad M., Verhagen M., Zoethout J.A., Basiak A., Chalas M., Chesiak M., Gramza A., Iwankiewicz J., Sieradzan E., Wikiera B., Ciechanowska M., Dziatkowiak H., Futona B., Gorska A., Glowacka-Wony M., Kaim I., Klich B., Starzyk J., Wolanin M., Tokarska L., Chucherco D., Deja G., Firek-Pedras M., Jarosz-Chobot P., Kalina M., Kutrowska-Adamusiak K., Minkina-Pedras M., Muchaka-Bianga M., Bodalski J., Mlynarski W., Szadkowska A., Cieslak A., Cypryk K., Dziatosz K., Jastzebowska J., Krysiak A., Szymanska U., Wilcznski J., Zawodniak-Szalapska M., Aguay A., Bilbao J.R., Chueca M., Cortazar A., Echarte G., Frutos T.G., Jimenez P., Martul P., Moreno A., Oyarzabal M., Rica I., Salgado Y., Martinez-Larrad M.T., Hawkins F.G., Hernandez R., Herranz L., Pallardo L.F., Deibarra L.S., Fernandez B.H., Luis J.L., Ortiz-Quintana L., Recarte P.P., Arnau D.R., Aronsson L., Boden S., Fredriksson J., Isacsson E., Johansson I., Karlsson E., Lock C., Sandstrom A.-M., Konefal M.S., Andreasson C., Dahlstrom U., Hanas R., Lundqvist K., Windell L., Jansson I., Karlsson A.-K., Lindbladh B., Odenman I., Pettersson C., Sundberg F., Sundqvist M., Aronsson S., Bellman I., Bengtsson A.-B., Lyden G.-B., Nilsson N.-O., Soderblom M., Unt C., Augustsson M., Bengtsson M., Fors H., Helmrich A., Johansson T.O., Andersson A.-C., Boiard-Stomlid A., Hellgren G., Kallsholm H., Lindqvist J., Nilsson M., Nordwall M., Stromstedt C., Ahsberg C., Lindh A., Lindhe C., Samuelsson C., Wiik A., Edenwall H., Ljumgcrantz M., Persson I.-B., Strigard E., Svensson B.-L., Aman J., Breivik G.-E., Detlofsson I.-L., Kroon M., Sarnblad S., Johansson C., Ilvered R., Lundberg A., Akesson K., Beccarelli A., Gadient M., Rappold-Amrein C., Schoenle E., Daftary A., Damagro-Elias M.E., Gilmour C., Klein M.B., Lain C., Salerno D., Smith M.E., Vats K., Pfaff D.J., Malone P., Mansfield P., Munns M., Nickel K., Pompilio K., Siemion W., Taculad R., Van Horn K., Zdanadewic M., Chambliss C., Jones J., Sadler M., Tanner-Blasiar M., Bell C., Camper N., Devaskar S., Devaskar U., Horowitz H., Rogers L., Shannahan R., Silk K., Bermudez Z., Colon R., Frazer T., Martinez-Nieves B., Torres J., Vega J., Chan M., Cook S., Goland R., Greenberg E., Jules N., Montes J., Nelson M., Parra-Valencia Z., Schachner H., Softness B., Kiviniemi M., Suomenin R., Alexander A., Hyrckowian E., Nichol L., Trucco M., Karjalainen E., Louhio T., Sarnesto A., Valtonen E., Davydova B., Helander S., Hamalainen J., Harkonen T., Joutsjoki L., Kararic M., Latva-Koivisto M., Lonn E., Nurmi T., Ollila I., Rinkinen J., Ronkainen M., Tukiainen H., Cederlof A., Kiikeri M., Tsupari S., Cheng R., Bryant K., Chan Y., Maezawa Y., Paltser G., Rasavi R., Tsui H., Winer S., Wu P., Yantha J., University of Zurich, and Knip, Mikael

Subjects
Male, Risk, medicine.medical_specialty, Casein, Breastfeeding, 030209 endocrinology & metabolism, 610 Medicine & health, 2700 General Medicine, Endocrinology and Diabetes, Disease-Free Survival, law.invention, Follow-Up Studie, Nutrition Policy, 03 medical and health sciences, 0302 clinical medicine, Randomized controlled trial, Double-Blind Method, SDG 3 - Good Health and Well-being, law, Internal medicine, Diabetes mellitus, medicine, Humans, Cumulative incidence, 030212 general & internal medicine, Child, Infant Nutritional Physiological Phenomena, Original Investigation, 2. Zero hunger, Type 1 diabetes, business.industry, Hazard ratio, Absolute risk reduction, Infant, Newborn, Caseins, General Medicine, ta3121, medicine.disease, Infant Formula, 3. Good health, Diabetes Mellitus, Type 1, Infant formula, 10036 Medical Clinic, Endokrinologi och diabetes, Female, business, Human, Follow-Up Studies
Abstract

IMPORTANCE Early exposure to complex dietary proteins may increase the risk of type 1 diabetes in children with genetic disease susceptibility. There are no intact proteins in extensively hydrolyzed formulas. OBJECTIVE To test the hypothesis that weaning to an extensively hydrolyzed formula decreases the cumulative incidence of type 1 diabetes in young children. DESIGN, SETTING, AND PARTICIPANTS An international double-blind randomized clinical trial of 2159 infants with human leukocyte antigen-conferred disease susceptibility and a first-degree relative with type 1 diabetes recruited from May 2002 to January 2007 in 78 study centers in 15 countries; 1081 were randomized to be weaned to the extensively hydrolyzed casein formula and 1078 to a conventional formula. The follow-up of the participants ended on February 28, 2017. INTERVENTIONS The participants received either a casein hydrolysate or a conventional adapted cows milk formula supplemented with 20% of the casein hydrolysate. The minimum duration of study formula exposure was 60 days by 6 to 8 months of age. MAIN OUTCOMES AND MEASURES Primary outcome was type 1 diabetes diagnosed according to World Health Organization criteria. Secondary outcomes included age at diabetes diagnosis and safety (adverse events). RESULTS Among 2159 newborn infants (1021 female [47.3%]) who were randomized, 1744 (80.8%) completed the trial. The participants were observed for a median of 11.5 years (quartile [Q] 1-Q3, 10.2-12.8). The absolute risk of type 1 diabetes was 8.4% among those randomized to the casein hydrolysate (n = 91) vs 7.6% among those randomized to the conventional formula (n = 82) (difference, 0.8%[95% CI, -1.6% to 3.2%]). The hazard ratio for type 1 diabetes adjusted for human leukocyte antigen risk group, duration of breastfeeding, duration of study formula consumption, sex, and region while treating study center as a random effect was 1.1 (95% CI, 0.8 to 1.5; P = .46). The median age at diagnosis of type 1 diabetes was similar in the 2 groups (6.0 years [Q1-Q3, 3.1-8.9] vs 5.8 years [Q1-Q3, 2.6-9.1]; difference, 0.2 years [95% CI, -0.9 to 1.2]). Upper respiratory infections were the most common adverse event reported (frequency, 0.48 events/year in the hydrolysate group and 0.50 events/year in the control group). CONCLUSIONS AND RELEVANCE Among infants at risk for type 1 diabetes, weaning to a hydrolyzed formula compared with a conventional formula did not reduce the cumulative incidence of type 1 diabetes after median follow-up for 11.5 years. These findings do not support a need to revise the dietary recommendations for infants at risk for type 1 diabetes. Funding Agencies|Eunice Kennedy Shriver National Institute of Child Health and Development (NICHD); National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health [HD040364, HD042444, HD051997]; Canadian Institutes of Health Research; Commission of the European Communities [QLK1-2002-00372]; European Foundation for the Study of Diabates/JDRF/Novo Nordisk; Academy of Finland (Centre of Excellence in Molecular Systems Immunology and Physiology Research) [250114]; Dutch Diabetes Research Foundation; Finnish Diabetes Research Foundation; JDRF

Published
2018

38. B2B-asiantuntijapalveluiden myynnin muutos digitalisaation myötä

Author

Koivisto, M. (Mikko)

Subjects
Marketing
Abstract

Tämän tutkimuksen tarkoituksena on lisätä ymmärrystä digitaalisten sovellusten käyttämisestä ja niiden aiheuttamista muutoksista yrityksen myyntiprosessiin, asiakkaiden hankintaan, lisämyyntiin sekä organisaatioon. Tavoitteena tutkimuksessa on selvittää, mitä ja kuinka voimakkaita nämä vaikutukset ovat olleet sekä miten aikaisempi tutkimus on kartoittanut digitaalisten sovellusten käytöstä johtuvaa muutosta. Tutkimuksen kohteena ovat B2B-asiantuntijapalveluita tarjoavat yritykset, jotka ovat toiminnassaan hyödyntäneet digitaalisia sovelluksia, kuten esimerkiksi CRM-järjestelmiä ja hakukonemarkkinointia. Tutkittavaa aihetta lähestytään myyvänä osapuolena olevan yrityksen näkökulmasta. Tutkimuksen ensimmäisessä osassa tarkastellaan yleistä myynnin muutosta digitalisaation myötä, käytettyjen digitaalisten sovellusten lisääntymistä ja niiden luomia mahdollisuuksia, sekä B2B-asiantuntijapalveluiden luonnetta ja erityispiirteitä. Lisäksi teoriaosuudessa tarkastellaan, mitä ongelmia ja mahdollisuuksia digitaalisiin sovelluksiin sekä niiden käytöstä aiheutuviin muutoksiin mahdollisesti liittyy. Aikaisemman tutkimuksen tarkastelussa on pyritty saavuttamaan mahdollisimman laaja-alainen näkemys tutkittavaan aiheeseen käyttämällä lähteinä viimeisimpiä tieteellisiä julkaisuja. Tämä tutkimus toteutettiin käyttämällä kvalitatiivista menetelmää. Tutkimusmateriaali kerättiin haastattelemalla useita eri aloilla toimivia ja eri kokoisia yrityksiä, jotka myyvät B2B-asiantuntijapalveluita. Haastateltavat yritykset valikoituivat tutkijan oman kartoituksen kautta ja haastattelut toteutettiin käyttämällä puolistrukturoituja teemahaastatteluja. Kriteereinä yritysten valinnalle olivat B2B-asiantuntijapalveluiden myyminen sekä digitaalisten sovellusten hyödyntäminen yrityksen myyntitoiminnoissa. Tutkimuksen tulokset ovat varsin mielenkiintoisia ja osoittavat, ettei digitaalisten sovellusten käyttäminen ole kohdeyritysten osalta aiheuttanut toistaiseksi merkittäviä muutoksia yritysten myyntiprosessiin. Digitaaliset sovellukset olivat kyllä selventäneet prosessia sekä helpottaneet myynnin ja markkinoinnin välistä kommunikointia, mutta itse myyntityön tekemiseen sovelluksilla ei näissä yrityksissä näytä olevan vaikutusta. Yhden yrityksen kohdalla ei myöskään myyntiprosessin läpimeno ollut nopeutunut. Tutkimukseen osallistuneiden yritysten organisaatiotkaan eivät olleet muuttuneet digitaalisten sovellusten ansiosta. Vaikka tutkimuksen tuloksissa ei ollut havaittavissa selkeitä mullistuksia, on muutos kuitenkin tulevaisuutta. Useimmissa tutkimukseen osallistuneissa yrityksissä digitaalisten sovellusten täysimittainen hyödyntäminen on vielä kesken ja olisikin mielenkiintoista nähdä vastaavat tulokset esimerkiksi viiden vuoden kuluttua.

Published
2018

42. High-accuracy joint position and orientation estimation in sparse 5G mmWave channel

Author

Talvitie, J. (Jukka), Koivisto, M. (Mike), Levanen, T. (Toni), Valkama, M. (Mikko), Destino, G. (Giuseppe), Wymeersch, H. (Henk), Talvitie, J. (Jukka), Koivisto, M. (Mike), Levanen, T. (Toni), Valkama, M. (Mikko), Destino, G. (Giuseppe), and Wymeersch, H. (Henk)

Abstract

With the emergence of new 5G radio networks, high-accuracy positioning solutions are becoming extensively more important for numerous 5G-enabled applications and radio resource management tasks. In this paper, we focus on 5G mm-wave systems, and propose a method for high-accuracy estimation of the User Equipment (UE) position and antenna orientation. Based on the sparsity of the mm-wave channel, we utilize a compressive sensing approach for estimating the departure and arrival angles as well as the time-of-arrival for each observed radio propagation path. After this, in order to obtain statistical descriptions of the unknown parameters, we analytically derive a set of sampling distributions, which enable utilization of an iterative Gibbs sampling method. As shown by the obtained simulation results, the proposed method is able to achieve centimeter-level positioning accuracy with degree-level orientation accuracy, even in the absence of a line-of-sight path.

Published
2019

43. Linking calculation of wakes from offshore wind farm cluster to the Danish power integration system

Author

Larsén, X. G., Volker, P., Imberger, M., Fischereit, J., Koivisto, M., Das, K., Kanellas, P., Sørensen, P., Langor, E., Duin, M., Hawkins, S., Maule, P., Ahsbahs, T., Du, J., Hahmann, A., Davis, N., Ott, S., Badger, J., Larsén, X. G., Volker, P., Imberger, M., Fischereit, J., Koivisto, M., Das, K., Kanellas, P., Sørensen, P., Langor, E., Duin, M., Hawkins, S., Maule, P., Ahsbahs, T., Du, J., Hahmann, A., Davis, N., Ott, S., and Badger, J.

Abstract

For the first time, the Danish power integration system takes into account of wake effects from large offshore wind farm clusters. The wake effect was calculated through an innovative, mesoscale wind-wave-wake coupled modeling system developed at DTU Wind Energy Department.

Published
2019

44. Derivation of Uncertainty Distributions for Channel Flow Rate and Fuel Critical Heat Flux Predictions for Best-Estimate Plus Uncertainty Analysis of Slow Loss-of–Reactor Power Regulation Accidents in CANDU Stations.

Author

Parlatan, Y., Rogers, J., and Koivisto, M.

Abstract

AbstractUncertainty in the figure of merit (FOM) parameters is a central feature of the best-estimate plus uncertainty (BEPU) method, which provides insight into the analysis margins not available from other analysis methods. The FOM uncertainty distributions are formed from propagation of the variations and uncertainty distributions in the operational and modeling parameters used in simulations of a design-basis accident (DBA) scenario for a nuclear power plant.To compute an accurate FOM uncertainty distribution, it is critical to accurately quantify and account for the input parameter prediction uncertainties. The coolant flow rate through fuel channels, or more precisely, the hydraulic resistance, including the impact of two-phase flow and its distribution in the primary heat transport system and the critical heat flux (CHF) of the fuel, are two key parameters for the limiting postulated accident scenarios in a CANDU reactor for various DBAs.Prediction uncertainty distributions for these parameters can be derived by directly validating code predictions against in-reactor measurements of flow rate and experimental measurements of CHF, respectively. Such code validation circumvents the convoluted and complex approach of decomposing computer models of physical phenomena into microscopic parameters, such as interfacial mass, momentum, and heat transfer correlations, and propagation of their uncertainty distributions to obtain an overall parameter uncertainty distribution of interest. Uncertainties associated with predictions of the coolant flow rate and CHF arise due to temporal and spatial variations and uncertainties in reactor conditions, limitations of physical models and their implementation in the codes, and in the case of CHF, measurement uncertainties associated with full-scale experiments.Careful assessment of key uncertainties, specifically their magnitudes, is important for ensuring uncertainty magnitudes are not unnecessarily over- or underestimated. These uncertainties also need to be characterized properly, e.g., whether uncertainties are common to a group of reactor fuel channels or vary independently for each fuel channel. Inadequate identification or incorrect classification or characterization of uncertainties would result in an inaccurate FOM uncertainty distribution.One important focus area for this study is the distinction between apparent prediction uncertainty (the difference between code prediction and measurement) and actual prediction uncertainty (the difference between code prediction and the true value). The actual code prediction uncertainty can be calculated from the apparent code uncertainty, provided there is adequate knowledge about the measurement uncertainty. The uncertainty models developed using this approach will be used as part of the BEPU analysis for slow loss-of–reactor power regulation accidents. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Benefits of positioning-aided communication technology in high-frequency industrial IoT

Author

Lohan, E. S. (Elena Simona), Koivisto, M. (Mike), Galinina, O. (Olga), Andreev, S. (Sergey), Tölli, A. (Antti), Destino, G. (Giuseppe), Costa, M. (Mário), Leppänen, K. (Kari), Koucheryavy, Y. (Yevgeni), Valkama, M. (Mikko), Lohan, E. S. (Elena Simona), Koivisto, M. (Mike), Galinina, O. (Olga), Andreev, S. (Sergey), Tölli, A. (Antti), Destino, G. (Giuseppe), Costa, M. (Mário), Leppänen, K. (Kari), Koucheryavy, Y. (Yevgeni), and Valkama, M. (Mikko)

Abstract

The future of industrial applications is shaped by intelligent moving IoT devices, such as flying drones, advanced factory robots, and connected vehicles, which may operate (semi-)autonomously. In these challenging scenarios, dynamic radio connectivity at high frequencies, augmented with timely positioning-related information, becomes instrumental to improve communication performance and facilitate efficient computation offloading. Our work reviews the main research challenges and reveals open implementation gaps in IIoT applications that rely on location awareness and multi-connectivity in super high and extremely high frequency bands. It further conducts a rigorous numerical investigation to confirm the potential of precise device localization in the emerging IIoT systems. We focus on positioning- aided benefits made available to multi-connectivity IIoT device operation at 28 GHz, which notably improve data transfer rates, communication latency, and the extent of control overhead.

Published
2018

47. Koululiikunnan vaikutukset koulumenestykseen

Author

Koivisto, M. (Markku)

Subjects
Education
Abstract

Huoli suomalaisten ja muidenkin länsimaisten lasten ja nuorten liikkumattomuudesta ja fyysisesti passiivisesta elämäntavasta, ja seurauksena syntyvistä mahdollisista terveysongelmista nostaa aika ajoin päätään. Samaan aikaan suomalaisten lasten sijoitukset vuoden 2013 PISA-tutkimuksessa olivat pudonneet reilusti aiemmista sijoituksista. Koululiikunta nähdään yhtenä tekijänä lasten ja nuorten liikunnan lisäämisessä. Tämän työn tarkoituksena on käsitellä lisäksi sitä, miten koululiikunta voi vaikuttaa myös koulumenestykseen. Työ on kirjallisuuskatsaus. Tutkimusten mukaan koululiikunnalla on neutraali, tai lievästi positiivinen yhteys koulumenestykseen. Tärkein havainto on se, että koululiikunnan seurauksena oppiminen tehostuu. Tällä tarkoitetaan sitä, että vaikka lisätylle koululiikunnalle otetaan aikaa muista oppiaineista, muiden aineiden koulumenestys ei huonone. Yleisen fyysisen aktiivisuuden ja hyvän kunnon vaikutuksiin verrattuna koululiikunnan vaikutukset koulumenestykseen ovat kuitenkin vaatimattomia. Syyksi epäillään sitä, että koulun liikuntatunneilla liikuntaan käytetty aika, sekä liikunnan intensiteetti jäävät liian vähäiseksi saadakseen aikaan positiivisia vaikutuksia koulumenestykseen. Tutkimustulokset kuitenkin osoittavat selkeästi, että koululiikuntaa voidaan koulussa lisätä merkittävästi ilman pelkoa muiden oppiaineiden kärsimisestä. Runsaampi koululiikunta voidaan nähdä merkittävänä tekijänä lasten ja nuorten liikuntamäärien lisäämisessä, sekä yhtenä tekijänä parempaa koulumenestystä tavoiteltaessa.

Published
2016

48. Dense subset sum may be the hardest

Author

Austrin, P., Kaski, P., Koivisto, M., Nederlof, J., Ollinger, N., Vollmer, H., Discrete Mathematics, Combinatorial Optimization 1, Ollinger, Nicolas, Vollmer, Heribert, KTH Royal Institute of Technology, Department of Computer Science, Helsinki Institute for Information Technology HIIT, Eindhoven University of Technology, Aalto-yliopisto, and Aalto University

Subjects
FOS: Computer and information sciences, Additive combinatorics, Discrete Mathematics (cs.DM), Computer Science - Information Theory, cs.DM, 0102 computer and information sciences, 02 engineering and technology, Computational Complexity (cs.CC), 01 natural sciences, cs.IT, Computer Science - Data Structures and Algorithms, 0202 electrical engineering, electronic engineering, information engineering, Data Structures and Algorithms (cs.DS), math.IT, ta113, 000 Computer science, knowledge, general works, cs.CC, Information Theory (cs.IT), Subset Sum, Computer Science - Computational Complexity, cs.DS, Exponential-time algorithm, 010201 computation theory & mathematics, Computer Science, Littlewood-offord problem, 020201 artificial intelligence & image processing, Homomorphic hashing, Computer Science - Discrete Mathematics
Abstract

The Subset Sum problem asks whether a given set of $n$ positive integers contains a subset of elements that sum up to a given target $t$. It is an outstanding open question whether the $O^*(2^{n/2})$-time algorithm for Subset Sum by Horowitz and Sahni [J. ACM 1974] can be beaten in the worst-case setting by a "truly faster", $O^*(2^{(0.5-\delta)n})$-time algorithm, with some constant $\delta > 0$. Continuing an earlier work [STACS 2015], we study Subset Sum parameterized by the maximum bin size $\beta$, defined as the largest number of subsets of the $n$ input integers that yield the same sum. For every $\epsilon > 0$ we give a truly faster algorithm for instances with $\beta \leq 2^{(0.5-\epsilon)n}$, as well as instances with $\beta \geq 2^{0.661n}$. Consequently, we also obtain a characterization in terms of the popular density parameter $n/\log_2 t$: if all instances of density at least $1.003$ admit a truly faster algorithm, then so does every instance. This goes against the current intuition that instances of density 1 are the hardest, and therefore is a step toward answering the open question in the affirmative. Our results stem from novel combinations of earlier algorithms for Subset Sum and a study of an extremal question in additive combinatorics connected to the problem of Uniquely Decodable Code Pairs in information theory., Comment: 14 pages

Published
2016
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