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Joonas; Illana Sison, Andres et al. JYFL-ACCLAB-JM27 Collectivity and shape phenomena in extremely neutron deficient A ≈ 120 Lanthanides, Addendum JM03. V. 11.10.2021. University of Jyväskylä.","cumulative_state":0,"data_catalog":"urn:nbn:fi:att:data-catalog-ida","description":{"en":"JM27:\r\n\r\nCollectivity and shape phenomena in extremely neutron deficient A ≈ 120 Lanthanides (Addendum JM03)\r\n\r\nSpokesperson: C. Petrache, B. Cederwall\r\n\r\nLiaisons: A. Briscoe\r\n\r\nCollaboration: JYFL - Orsay - KTH\r\n\r\nDate: 04.10.2021-11.10.2021\r\nNucleus: 120La, 119La, 118Cs, 117Cs\r\nBeam (charge state): 64Zn11+\r\nTarget (thickness): 58 Ni(1.0 mg/cm2, 0.95 mg/cm2)\r\nDegrader (thickness): 48 ug/cm²\r\nBeam energy: 275, 255 MeV\r\nBeam intensity: 1.5-4 pnA\r\nBeam hours delivered roughly: 168 hours\r\nInstrumentation: JUROGAM 3, MARA, DSSD (BB20 300-um thick), MWPC, focal plane Ge-array: 3 BEGes + 1 clover, 2x punch through Si 1 mm.\r\nMARA Electric Dipole setting: 129-148 kV\r\n\r\nObjectives(less than 250 words):\r\n\r\nStudy collectivity and shape phenomena in extremely neutron deficient A ≈ 120 Lanthanides by aiming to discover excited states in 120La. Additionally, obtain a high-statistic data set on ancillary residual nuclei to reach terminating states of the (h11/2)x(h11/2) valence space.\r\n\r\nAchievements (less than 250 words): We performed very successful experiment. Regarding the 120La part, evidence of two cascades first observed in the previous JM03 experiment were observed seperately in at 275 MeV and 255 MeV respectively, leading to the tentative assignment of the latter to 120La; a superior m/q spectrum with respect the first run and La X-rays will aid with firm assignment during offline analysis. The other band of interest, after being observed only at a higher energy, is thought to belong to 117/118Cs.\r\n\r\nComments: MARA performed very well and spectra were notably clean. 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This paper explores the pivotal role of key competencies – self-efficacy, job crafting, and proactivity – in navigating the technological transformation brought about by AI in work settings. The three key competencies were previously identified as the main building blocks of the entrepreneurial spirit that is necessary for the successful use of AI in knowledge work and organizations (Humala & Lahtinen 2023, Ruohonen & Humala 2022). The research paper draws insights from a quantitative nation-wide survey that was carried out among knowledge workers in Finland. The survey was conducted during February-March 2024, with the insights gained from 474 informants. The research was funded by The Finnish Work Environment Fund and conducted by Haaga-Helia University of Applied Sciences based in Helsinki. 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The research reveals that AI can significantly foster proactivity in the workplace, particularly when employees are equipped with sufficient information and resources. Ultimately, this exploration aims to provide insights into developing AI-supported business models that not only leverage the human entrepreneurial spirit but also foster an inclusive environment that enhances organizational competitiveness and innovation.","fi":"The rapid advancements in Artificial Intelligence (AI) have significantly shifted the landscape of work, altering the roles of experts and knowledge workers across various domains (Humala et al. 2023). This paper explores the pivotal role of key competencies – self-efficacy, job crafting, and proactivity – in navigating the technological transformation brought about by AI in work settings. The three key competencies were previously identified as the main building blocks of the entrepreneurial spirit that is necessary for the successful use of AI in knowledge work and organizations (Humala & Lahtinen 2023, Ruohonen & Humala 2022). The research paper draws insights from a quantitative nation-wide survey that was carried out among knowledge workers in Finland. The survey was conducted during February-March 2024, with the insights gained from 474 informants. The research was funded by The Finnish Work Environment Fund and conducted by Haaga-Helia University of Applied Sciences based in Helsinki. Results show that while the integration of AI in knowledge work environments generally supports self-efficacy, job crafting, and proactivity, the effects are varied, showing both enhancement and, in some cases, diminishment. The findings indicate that AI’s positive influence is most pronounced among those engaged in high levels of knowledge-based tasks and who maintain a positive outlook toward new technologies. This research also highlights some disparity in experiences between different groups, particularly between younger and older knowledge workers with early- and late-careers as well as those in different work positions, pointing to the need for tailored organizational support to ensure equitable benefits from AI technologies. The research reveals that AI can significantly foster proactivity in the workplace, particularly when employees are equipped with sufficient information and resources. Ultimately, this exploration aims to provide insights into developing AI-supported business models that not only leverage the human entrepreneurial spirit but also foster an inclusive environment that enhances organizational competitiveness and innovation.","und":"The rapid advancements in Artificial Intelligence (AI) have significantly shifted the landscape of work, altering the roles of experts and knowledge workers across various domains (Humala et al. 2023). This paper explores the pivotal role of key competencies – self-efficacy, job crafting, and proactivity – in navigating the technological transformation brought about by AI in work settings. The three key competencies were previously identified as the main building blocks of the entrepreneurial spirit that is necessary for the successful use of AI in knowledge work and organizations (Humala & Lahtinen 2023, Ruohonen & Humala 2022). The research paper draws insights from a quantitative nation-wide survey that was carried out among knowledge workers in Finland. The survey was conducted during February-March 2024, with the insights gained from 474 informants. The research was funded by The Finnish Work Environment Fund and conducted by Haaga-Helia University of Applied Sciences based in Helsinki. Results show that while the integration of AI in knowledge work environments generally supports self-efficacy, job crafting, and proactivity, the effects are varied, showing both enhancement and, in some cases, diminishment. The findings indicate that AI’s positive influence is most pronounced among those engaged in high levels of knowledge-based tasks and who maintain a positive outlook toward new technologies. This research also highlights some disparity in experiences between different groups, particularly between younger and older knowledge workers with early- and late-careers as well as those in different work positions, pointing to the need for tailored organizational support to ensure equitable benefits from AI technologies. 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While 5G enables unprecedented capabilities such as ultra-reliable low latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC), it also introduces new vulnerabilities due to its cloud-native, virtualized, and software-driven architecture. \n\nThis dataset focuses on the GPRS Tunneling Protocol for the User Plane (GTP-U), which operates on the N3 interface between the gNodeB and the User Plane Function (UPF). As a protocol designed under the assumption of a trusted backhaul, GTP-U lacks authentication and integrity protections, making it a high-value target for adversaries. Attacks exploiting GTP-U can result in denial-of-service, session hijacking, traffic redirection, and privacy violations. \n\nThe dataset provides both benign traffic (representing real-world applications such as streaming, browsing, and file downloads) and malicious traffic (five classes of simulated attacks). Data is available in multiple formats (PCAP and CSV with over 80 extracted features), ensuring adaptability for diverse research purposes. \n\nTestbed Design \n\nThe dataset was generated using a realistic cloud-native 5G testbed: \n\nInfrastructure: Kubernetes cluster with two Lenovo ThinkStation P3 servers (Ubuntu 22.04). \n\nCore Network: Open-source Open5GS for AMF, SMF, UPF, and other core functions. Radio \n\nAccess Network (RAN): UERANSIM to emulate 30 UEs and 3 gNodeBs. \n\nTraffic Capture: Sidecar container inside the UPF pod for real-time packet sniffing. \n\nMonitoring: Prometheus and Grafana for system observability. Analysis: Independent ML-based anomaly detection to validate realism. \n\nThis setup ensured scalability, reproducibility, and real-time traffic monitoring. \n\nBenign Traffic Generation \n\nTo approximate real-world 5G usage, three benign traffic categories were simulated: \n\nVideo Streaming – 20 UEs using iperf3, with randomized throughput (5–50 Mbps) and session durations. \n\nWeb Browsing – 10 UEs using curl to fetch content from 12 randomized websites. \n\nFile Downloading – Additional browsing users download files of varying sizes. \n\nThis diversity captures the variability of session lengths, packet sizes, protocols, and throughput, which is essential for distinguishing attacks from legitimate usage. \n\nAttack Scenarios \n\nThe dataset includes five attack categories, each carefully simulated and labeled: \n\nGTP Encapsulation Attack – Nested GTP-U packets injected to exploit UPF vulnerabilities. \n\nMalformed GTP Attack – Variants with invalid headers, corrupted checksums, oversized fields, and unsupported message types. \n\nDDoS Attack (ICMP/UDP Floods) – Attacks from compromised UEs targeting the UPF. \n\nIntra-UPF UE DoS Attack – Malicious UE floods another UE within the same UPF, using SYN floods, UDP floods, ICMP floods, HTTP floods, and fragmentation-based amplification. \n\nGTP-U TEID Brute-Force Attack – Adversary guesses Tunnel Endpoint Identifiers (TEIDs) to discover active sessions or disrupt connectivity. \n\nThese attacks were repeated multiple times over several days to ensure diversity and statistical richness. \n\nData Processing Pipeline \n\nCaptured data underwent a structured pipeline: \n\nPacket Capture (PCAP) – Full traffic including GTP-U headers. \n\nGTP-U Header Removal (via STRIPE tool) – Preserving only relevant fields when appropriate. \n\nFlow Generation (via CICFlowMeter) – Conversion into flow-based CSV with 84 statistical features. \n\nLabeling – Mapping to one of six classes: BENIGN, GTP-ENCAPSULATION, GTP-MALFORMED, DDOS, 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In 2014, the ministries of Agriculture and Forestry and Environment identified areas where new fish farms can be established (link to the national aquaculture location management plan?). Metsähallitus manages over 2 million hectares of public waters in the sea. A large part of the areas suitable for fish farming are located in ranges under the control of Metsähallitus. The aim of the Aquaculture Innovation Program is to carry out research and development activities which can increase domestic fish farming and its value in a sustainable way. The Aquaculture Innovation Program has developed a fish farm allocation tool, FINFARMGIS. The tool is based on an ecosystem approach which considers the impact of fish farming through different ecological, social and economic criteria. The tool can be used to evaluate production sites by the set criteria or more comprehensively by the environmental permit process. FINFARMGIS is updated as spatial data and research data increases. At the moment location optimization takes the following criteria into account (in brackets criterias still under work): 1. Exclusive criteria: fairways, military areas, national parks, wrecks, cables, private and state nature reserves 2. Ecological guiding criteria: Natura SPA areas,Natura SCA areas, sea area openness, ocean current, ecological status classification, depth, underwater nature peak areas (a-chlorophyll, other fish farms load) 3. Social guiding criteria: recreational facilities (recreational areas and fishing areas) 4. Criteria that guide the economy: distance from the shore (high-efficiency production areas, estimate of sustainable production, fish disease risk) The Government's key project “Pilots of the Environmental Statement” recommends that public institutions make the necessary inquiries and obtain environmental permits for entrepreneurs,. As a result, Metsähallitus, the Natural Resources Center and the Finnish Environment Institute are launching a “Kalavaltio” project, where existing research data will be used to identify new production sites in waters owned and controlled by Metsähallitus. The FINFARMGIS tool is used to define sustainable production areas. Also more detailed studies that are required for the environmental permit will be implemented primarily in areas of interest to entrepreneurs. Entrepreneurs are therefore asked to contact the Natural Resources Center or Metsähallitus if the company is interested in expanding its production in the areas owned and controlled by Metsähallitus. The target areas for the project for more detailed explanations are selected 15.4.2019 based on incoming contacts.\n\n#### Additional Information\n\nMarkus Kankainen ja Lauri Niskanen Luonnonvarakeskus\n\nJani Viisanen Metsähallitus","fi":"Kansallisen vesiviljelystrategian 2022 tavoitteena on lisätä kalankasvatusta Suomessa. Kansallinen vesiviljelyn sijainninohjaussuunnitelma valmistui 2014 Maa- ja metsätalousministeriön ja Ympäristöministeriön yhteistyönä osoittamaan alueita, jonne voi perustaa uusia kalankasvatuslaitoksia. \n\nMetsähallituksen hallinnassa on yli 2 miljoonaa hehtaaria yleisiä vesialueita meressä. Vesiviljelyn sijainninohjaussuunnitelmassa kalankasvatukseen soveltuvat alueet sijaitsevat suurelta osin Metsähallituksen hallinnassa olevilla alueilla. Metsähallitus ja Luonnonvarakeskus arvioivat innovaatio-ohjelmassa yhteistyössä muiden partnereiden kanssa merialueita, jonne tuotantoa olisi mahdollista sijoittaa. \n\nVesiviljelyn innovaatio-ohjelman tavoitteena on toteuttaa tutkimus- ja kehittämistoimia, joilla voidaan lisätä kestävästi kotimaista kalankasvatusta ja sen arvoa. Vesiviljelyn innovaatio-ohjelmassa on kehitetty FINFARMGIS ekosysteemilähestymistapaan perustuvaa kalankasvatuksen sijainninohjaustyökalua, joka ottaa kansallista sijainninohjaussuunnitelmaa tarkemmin huomioon kalankasvatuksen vaikutukset ekologisiin, sosiaalisiin ja taloudellisiin kriteereihin. Jolloin työkalulla voidaan arvioida tuotantopaikkoja kriteerikohtaisesti ja kokonaisvaltaisesti esimerkiksi ympäristölupaprosessissa. \n\nFINFARMGIS tarkentuu paikkatiedon ja tutkimustiedon lisääntyessä ja tarkentuessa. Tällä hetkellä sijainninoptimoinnissa on huomioitu; (suluissa työn alla olevat kriteerit) 1. Poissulkevina kriteereinä; väyläalueet, sotilasalueet, kansallispuistot, hylyt, kaapelit, yksityiset ja valtion luonnonsuojelualueet 2. Ekologisina ohjaavia kriteereinä; Natura SPA alueet; Natura SCA alueet, alueen avoimuus, virtaus, ekologinen tilaluokittelu, syvyys, vedenalaisen luonnon huippualueet (a-klorofylli, muiden kalankasvatuslaitosten kuormitus) 3. Sosiaalisina ohjaavina kriteereinä; vapaa-ajan asutus, (virkistysalueet ja kalastukselle tärkeät alueet) 4. Taloudellisina ohjaavia kriteereitä; etäisyys rannasta ja merialueen avoimuus (lämpötilaltaan tehokkaat tuotantoalueet, arvio kestävästä tuotantomäärä, kalatautiriski) \n\nValtioneuvoston kärkihankkeessa ”Ympäristöluvituksen pilotit” suositus oli, että julkiset instituutiot voisivat tehdä tarvittavat selvitykset ja hakea ympäristöluvat valmiiksi yrittäjille, koska lupaprosessit ovat kalliita ja epävarmoja. Näin ollen Metsähallitus, Luonnonvarakeskus ja Suomen ympäristökeskus ovat käynnistämässä ”Kalavaltio” – hanketta, jossa olemassa oleva tutkimustietoa hyödynnetään uusien tuotantopaikkojen määrittämiseen Metsähallituksen vesialueille. Kestävien tuotantoalueiden määrittämiseen hyödynnetään FINFARMGIS työkalua mutta tarkempia ympäristöluvassa tarvittavia selvityksiä lähdetään toteuttamaan ensisijaisesti alueilla jotka kiinnostavat yrittäjiä. \n\nYrittäjiä pyydetäänkin ottamaan yhteyttä Luonnonvarakeskukseen tai Metsähallitukseen, jos yritystä kiinnostaa tuotantonsa laajentaminen karttalinkkien osoittamilla Metsähallituksen alueilla. 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The goal of the project is to produce a dataset that contains data about land use, land cover, soil types and special use and restricted areas. In the classification there are almost 50 land use classes. \r\n\r\nCoordinate Reference System: YKJ / Finland Uniform Coordinate System (EPSG:2393)","fi":"SLICES on paikkatietojen yhteiskäyttöhanke ja sen tavoitteena on tuottaa alueiden käyttöä, peitteisyyttä, maaperää sekä erityiskäyttö- ja käyttörajoitusalueita kuvaava paikkatietoaineisto koko valtakunnan alueelta. Maankäyttöluokkia on hierarkkisen luokkajärjestelmän tarkimmalla tasolla lähes 50. SLICES aineistoa ovat mukana tuottamassa Maa- ja metsätalousministeriö, Ympäristöministeriö, Maanmittauslaitos, Metsäntutkimuslaitos, Suomen ympäristökeskus ja Väestörekisterikeskus. \r\n\r\nKoordinaattijärjestelmä: YKJ (EPSG:2393)"},"field_of_science":[{"id":"7334f2e9-1aae-4b87-9088-8226211b06c9","url":"http://www.yso.fi/onto/okm-tieteenala/ta1171","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Geosciences","fi":"Geotieteet","sv":"Geovetenskaper"}},{"id":"27ad92ae-71a5-4cf1-896c-fbbdecf2289d","url":"http://www.yso.fi/onto/okm-tieteenala/ta519","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Social and economic geography","fi":"Yhteiskuntamaantiede, talousmaantiede","sv":"Socialgeografi och ekonomisk 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vektori"},"use_category":{"id":"ed5844fc-6309-4a8f-98bb-e18c1aec9fdc","url":"http://uri.suomi.fi/codelist/fairdata/use_category/code/outcome","in_scheme":"http://uri.suomi.fi/codelist/fairdata/use_category","pref_label":{"en":"Outcome material","fi":"Tulosaineisto"}},"access_url":"https://wiki.eduuni.fi/display/cscjemma/Paituli+limited+access+datasets"}],"spatial":[{"geographic_name":"Suomi","reference":{"id":"684b98e8-d5c1-4a59-9247-4793cbbd38e4","url":"http://www.yso.fi/onto/yso/p94426","in_scheme":"http://www.yso.fi/onto/yso/places","pref_label":{"en":"Finland","fi":"Suomi","sv":"Finland"},"as_wkt":"POINT (27 65)"},"custom_wkt":["POLYGON ((19.044079934135574 60.35245665473816, 19.044454069184297 60.35275908364717, 19.651571297995343 60.74811340370924, 19.65171533318069 60.74820374931038, 20.305217001936242 61.14278792238181, 20.305278223018185 61.142824295281144, 20.969115242035308 61.53085645475963, 20.970774850137 61.53207157313135, 20.972150739167848 61.533600540457115, 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