Maanpuolustuksen tieteellisen neuvottelukunnan osuus suomalaisessa puolustustutkimuksessa

Kirjoittaja toteaa alkusanoissaan, että sillä hetkellä nelivuotiskausiksi asetettu Maanpuolustuksen tieteellinen neuvottelukunta (MATINE) oli järjestyksessään neljäs. Lisäksi hän kuvaa lyhyesti aikaisempien neuvottelukuntien aikaansaannoksia. Ensimmäisessä luvussa tarkastellaan MATINEn asettamisen taustoja ja erityisesti valtioneuvoston vuonna 1959 asettaman professori Jaakko Raholan komitean toimintaa ja mietintöä. Toisessa luvussa esitellään MATINEn tehtäviä ja organisaatiota.Seuraavassa luvussa," MATINE 1960-luvulla", esitellään tutkimustoimintaa, taloutta ja henkilöstöä. Neljännessä luvussa, " Mahdollisuudet paranevat 1970-luvulla" todetaan, että tutkimustoiminta kehittyi ja kuvataan siihen vaikuttaneita tekijöitä. Taloustilannetta paransi valtion tulo- ja menoarvioon otettu selkeä määräraha tutkimustyön kehittämistä varten. Lisäksi esitellään henkilöstö ja virkamiehet sekä tarkastellaan MATINEn yhteistoimintaa puolustusministeriö ja pääesikunnan kanssa sekä yhteistoimintaa hallinnonalan ulkopuolella. Tulevaisuuden näkymiä käsitellessään kirjoittaja muun muassa toteaa, että "MATINEn toiminnan kehittämiseksi tarvittaisiin ennen kaikkealisää päätoimista suunnitteluhenkilöstöä. Sama koskee myös kokopuolustusministeriön hallinnonalan tutkimus- ja kehittämistyön suunnittelua.

Effects of beta-blockers on ventricular repolarization documented by 24-hour electrocardiography in long QT syndrome type 2

Peer reviewe

Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design

The global demand for renewable energy is on the rise. Expansion of onshore wind energy is in many parts of the world limited by societal acceptance, and also ecological impacts are a concern. Here, pragmatic methods are developed for the integration of high-dimensional spatial data in offshore wind energy planning. Over 150 spatial data layers are created, which either oppose or support offshore wind energy development, and represent ecological, societal, and economic factors. The method is tested in Finland, where interest in developing offshore wind energy is growing. Analyses were done using a spatial prioritization approach, originally developed for the prioritization of high dimensional ecological data, and rarely used in planning offshore wind energy. When all criteria are integrated, it is possible to find a balanced solution where offshore wind farms cause little disturbance to biodiversity and society, while at the same time yielding high profitability for wind energy production. Earlier proposed areas for offshore wind farms were also evaluated. They were generally well suited for wind power, with the exception of a couple of areas with comparatively high environmental impacts. As an outcome, new areas well suited for large scale wind power deployment were recognized, where construction costs would be moderate and disturbance to biodiversity, marine industries and people limited. A novel tradeoff visualization method was also developed for the conflicts and synergies of offshore energy deployment, which could ease the dialogue between different stakeholders in a spatial planning context. Overall, this study provides a generic and transparent approach for well-informed analysis of offshore wind energy development potential when conflict resolution between biodiversity, societal factors and economic profits is needed. The proposed approach is replicable elsewhere in the world. It is also structurally suitable for the planning of impact avoidance and conflict resolution in the context of other forms of construction or resource extraction.Peer reviewe

AN EXPERT SYSTEM FOR TALENT SEARCHING IN LONG DISTANCE RUNNING

NI

Waveform prototype-based feature learning for automatic detection of the early repolarization pattern in ECG signals

Objective: Our aim was to develop an automated detection method, for prescreening purposes, of early repolarization (ER) pattern with slur/notch configuration in electrocardiogram (ECG) signals using a waveform prototype-based feature vector for supervised classification. Approach: The feature vectors consist of fragments of the ECG signal where the ER pattern is located, instead of abstract descriptive variables of ECG waveforms. The tested classifiers included linear discriminant analysis, k-nearest neighbor algorithm, and support vector machine (SVM). Main results: SVM showed the best performance in Friedman tests in our test data including 5676 subjects representing 45408 leads. Accuracies of the different classifiers showed results well over 90%, indicating that the waveform prototype-based feature vector is an effective representation of the differences between ECG signals with and without the ER pattern. The accuracy of inferior ER was 92.74% and 92.21% for lateral ER. The sensitivity achieved was 91.80% and specificity was 92.73%. Significance: The algorithm presented here showed good performance results, indicating that it could be used as a prescreening tool of ER, and it provides an additional identification of critical cases based on the distances to the classifier decision boundary, which are close to the 0.1 mV threshold and are difficult to label.Peer reviewe

Waveform prototype-based feature learning for automatic detection of the early repolarization pattern in ECG signals

Objective: Our aim was to develop an automated detection method, for prescreening purposes, of early repolarization (ER) pattern with slur/notch configuration in electrocardiogram (ECG) signals using a waveform prototype-based feature vector for supervised classification. Approach: The feature vectors consist of fragments of the ECG signal where the ER pattern is located, instead of abstract descriptive variables of ECG waveforms. The tested classifiers included linear discriminant analysis, k-nearest neighbor algorithm, and support vector machine (SVM). Main results: SVM showed the best performance in Friedman tests in our test data including 5676 subjects representing 45408 leads. Accuracies of the different classifiers showed results well over 90%, indicating that the waveform prototype-based feature vector is an effective representation of the differences between ECG signals with and without the ER pattern. The accuracy of inferior ER was 92.74% and 92.21% for lateral ER. The sensitivity achieved was 91.80% and specificity was 92.73%. Significance: The algorithm presented here showed good performance results, indicating that it could be used as a prescreening tool of ER, and it provides an additional identification of critical cases based on the distances to the classifier decision boundary, which are close to the 0.1 mV threshold and are difficult to label.Peer reviewe