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In our study, 10 specimens of commercially available coaxial cable were exposed to gamma irradiation using two different dose rates and five different total doses. Then, the sound velocities were measured using traditional pulse-echo approach with an integrated transducermicrometer setup developed by VTT. EaB values were measured via tensile testing by ÚJV. The results clearly show, that sound velocity increases and EaB decreases when polyethylene is exposed to gamma irradiation. The correlation between sound velocity and EaB is linear. Based on the results, it is possible to use sound velocity as a low-cost nondestructive indicator for gamma-irradiation induced degradation of polyethylene.

In this study, 157 irradiated and non-irradiated Charpy specimens[1] manufactured from six different steel alloys used in the reactorpressure vessels (18MND5-W, 22NiMoCr37, A508-B, 15Kh2NMFA,HSST03 and A508-Cl2) were measured. The measurements includeddetermining several non-destructively measurable electric, magneticand elastic parameters. The applied non-destructive methods wereDirect Current-Reversal Potential Drop (resistivity) [2], 3MA (eddycurrent impedance loop shape) [3], TEP (Seebeck Coefficient) [4], MIRBE (Barkhausen noise) [5], MAT (magnetic hysteresis loop shape) [5] and sound velocity. After the non-destructive measurements, the ductile-brittle transition temperature (DBTT) was determined destructively using the ISO-standard method [1]. Several different regression algorithms, including neural network regression and support vector regression, were applied to the data. The algorithms were implemented with TensorFlow and scikit-learn using Python 3.7. With these algorithms, it was possible to estimate the DBTT with the mean absolute error smaller than 20 °C. Based on the results, the method can be seen as a potential candidate for estimating neutron-induced embrittlement non-destructively.

Machine learning (ML) has made marked advancements in the recent years. In many industries, machine learning algorithms have reached near-human or superhuman performance in tasks that were long considered impossible to automate. For NDT, the opportunity to improve automation in the tedious and error-prone task of complex data analysis offers several potential advantages. Automated systems would provide significant cost and time savings while at the same time improving the reliability and consistency of inspections.In this study, the current state of the art of machine learning systems for NDT and, in particular, inspection systems that could be applied to in-service inspections in nuclear power plants, is reviewed based on open literature. The current views and expectations of various stakeholders, including the utilities, NDT vendors, qualification bodies and authorities are surveyed. The necessary steps for bringing a machine learning system to industry use including needs for qualification and vendor participation are evaluated.The recent literature (including recent work from the authors) show that the ML systems have recently reached a state, where they can show super-human performance in flaw detection tasks, that have traditionally been considered impossible to automate. The key enabling technology is sophisticated data augmentation, that enables generation of sufficient training data-sets from limited flawed data-sets and training mock-ups. The current view among qualification bodies met is, that the ENIQ framework is flexible enough to accommodate qualification of an inspection system including a ML component. The NDT vendors are, in general, interested to make use of ML tools, albeit their development is considered outside their area of expertise.In summary, the current ML systems are powerful enough to be applied in the demanding environment of nuclear in-service inspection. To enable their use, further work is needed in training these models to wider range of data-sets including realistic plant data. Furthermore development and qualification of ML-powered inspection procedures need to be done in collaboration with NDT vendors.

Tämän tutkimuksen aiheena olivat opiskelijan stressinhallintastrategiat. Tutkimuskysymyksenä oli selvittää millaisilla strategioilla tutkittavat opiskelijat pyrkivät vaikuttamaan stressiin. Tavoitteena oli tuottaa jäsennyksiä, joilla ymmärtää stressiä ilmiönä sekä saada työväline stressin hallintaan. Tutkimus oli kvalitatiivinen ja aineistolähtöinen. Aineisto kerättiin kolmenkymmenen ammattikorkeakouluopiskelijan kirjoitelmista, joissa he kuvasivat stressiään ja sen hallintaa. Stressinhallintastrategioita etsittiin analysoimalla tutkittavien stressille ja stressinhallinnalle antamia merkityksiä. Aineiston analysoinnissa käytettiin grounded-teoriaa, eri koodausten kautta saatiin neljä stressinhallinnan pääkategoriaa, joiden väliltä löydettiin eroavaisuuksia ja samanlaisuuksia asenteen ja toiminnan suhteen. Näiden kahden dimension perusteella kategoriat sijoitettiin nelikenttään. Morenon rooliteoriaa hyödyntäen pääkategoriat nimettiin rooleiksi ja alakategoriat alarooleiksi. Tuloksena saadut roolit ovat hallitsija, työstäjä, ennaltaehkäisijä ja alle jäänyt.Saatua jäsennystä verrattiin morenolaisen rooliteorian sekä aikaisempien stressiteorioiden jäsennyksiin. Tavoitteena oli testata, rikastaa ja syventää luotuja rooleja. Stressin yläpuolella oleva hallitsija on voimakas. Hän voi hyödyntää stressiä tai olla välittämättä siitä. Voimaantunut työstäjä pyrkii omalla toiminnallaan kohtaamaan ja muokkaamaan stressaavaa tilannetta. Ennaltaehkäisijä pyrkii pääsemään karkuun tai pitämään stressin loitolla aktiivisella monipuolisella toiminnallaan. Alle jäänyt on passiivinen ja voimakkaasti stressin kourissa. Kohtalo, perimä tai oma heikkous edesauttavat hänen stressaamistaan. Stressiroolikartta on työväline, jonka avulla voi tarkastella stressinhallintastrategioita ja tunnistaa niiden vahvuuksia ja heikkouksia. Uusien roolien opettelun ja vanhoista, itselle haitallisista rooleista luopumisen kautta joustavoitetaan roolivarastoa ja stressin hallinta paranee. Asiasanat: stressi, stressinhallinta, grounded-teoria, morenolainen rooliteoria, roolikartta

The European Union (EU) is fundamentally an economic union. It was formed in 1957 by the Treaty of Rome. The initial target of the six founding members was to begin a process towards a European single market. This project focused mainly on the removal of barriers to free trade between the parties of the treaty. However, from the very beginning there was also an aim towards deeper political co-operation and a broader European project. Today, this has resulted in a union of 28 states with different industrial and public finance structures, political governing systems, social policy targets, national health systems, agricultural production structures and changing domestic political settings. During the last decades, the EU has been focusing (more or less) on closer integration in terms of a common constitution, a monetary union, and taking steps towards common foreign and security policy. Simultaneously, voices critical of closer integration and increased power of the union over its member states have become louder, ultimately leading to the decision of the UK to exit the EU as a result of a referendum in 2016. One of the key arguments of those advocating the Brexit decision was the claim that the UK would be economically stronger and better off outside the union, and would have the power to decide its own financial and economic policies.