Seafood traceability systems: Case Tracey - your traceability and trade data companion

Traceability of perishables such as food products is important for end-consumer food safety and operational efficiency of supply chains. Regulatory and market requirements have been driving the development and adoption of seafood traceability information systems. These Information systems are designed and built to support different stakeholders throughout the supply and value chain to collect, store and disseminate data about traceable products or resource units to form end-to-end traceability solutions. Implementation and use of traceability information systems typically require resources and know-how which may not always be available for the stakeholders in the beginning of the supply chain e.g. small scale fishers. There aren’t many information system solutions or tools that are targeted towards small scale fishers and fisheries. To answer to this need an information systems project, Tracey, was established to design and develop tooling for small scale fishers. Tracey is a blockchain based novel IT artifact, an information systems concept, that attempts to incentivize small scale fishers to provide first mile trade and traceability data of fish product from e.g. fish catch and fish landing. In this thesis the concepts of traceability, its drivers and benefits as well as traceability information systems are explored. In the case study, Tracey - a concept to incentivize small scale fishers to produce verifiable traceability and trade data, is presented and examined with information science research methods. The objectives for this study are to create a general understanding of benefits and challenges relate to seafood traceability, reflect Tracey with IS research methods, and suggest how to improve Tracey concept on basis of previous literature and research. Recommendations to improve Tracey IT artifact are provided on basis of analysis of Tracey with DSRM framework and further research is recommended on using blockchains in traceability information systems.Pilaantuvien tuotteiden kuten elintarvikkeiden jäljitettävyys on tärkeä osa elintarviketurvallisuutta ja toimitusketjujen toiminnallista tehokkuutta. Sääntely ja markkinoiden vaatimukset ovat olleet ajureina merenelävien jäljitettävyyden tietojärjestelmien kehittämiselle ja käyttöönotolle. Nämä informaatiojärjestelmät ovat suunniteltu ja toteutettu tukemaan eri sidosryhmiä datan keräämiselle, tallentamiselle ja jakamiselle jäljitettäville tuotteille arvo- ja toimitusketjuissa. Jäljitettävyyden informaatiojärjestelmien toteutus ja käyttö vaativat tyypillisesti resursseja ja tietopääomaa joka ei välttämättä ole aina saatavissa toimitusketjun alussa esimerkiksi pienimuotoisien kalastajien tapauksessa. Pienimuotoisille kalastajille suunnitellut informaatiojärjestelmät ja työkalut ovat harvassa. Vastatakseen tähän tarpeeseen uusi informaatiojärjestelmä projekti nimeltään Tracey on aloitettu. Tracey projektin tavoitteena on suunnitella ja kehittää työkaluja pienimuotoisille kalastajille. Tracey on lohkoketjuja hyödyntävä IT artifakti, informaatiojärjestelmä konsepti jonka tavoiteena on kannustaa pienimuotoisia kalastajia tuottamaan ensimmäisen mailin kauppa ja jäljitettävyys dataa merenelävien tuotteista esimerkiksi kalasaaliista. Tässä lopputyössä käydään lävitse jäljitettävyyden käsitteet, jäljitettävyyden ajurit ja hyödyt sekä jäljitettävyyden informaatiojärjestelmien konseptit. Case-tutkimusosuudessa esitetään Tracey informaatiojärjestelmä konsepti pienimuotoisten kalastajien kannustamiseksi tuottamaan todennettua jäljitettävyys ja kaupankäynti dataa, jota tutkitaan DSRM tutkimusmenetelmällä. Lopputyön tavoitteena on luoda yleiskuva ja näkemys merenelävien jäljitettävyyden hyödyistä ja haasteista, reflektoida Tracey konseptia tietojärjestelmien tutkimusmenetelmien avulla ja tuottaa ehdotuksia Tracey konseptin parantamiseksi kirjallisuuskatsauksen ja case-tutkimuksen myötä

Data Driven Social Network Analysis: Case Finnish Children’s Parliament

Tieteellinen kiinnostus sosiaalisten verkostojen analysointiin on kehittynyt ja kasvanut tietokoneiden ja webin myötä. Verkkopalveluiden suuret käyttäjämäärät ja niiden sisällä tapahtuvat käyttäjien muodostamat sosiaaliset vuorovaikutussuhteet tekevät mielenkiintoiseksi, niin markkinataloudellisesti kuin tieteellisestikin, käyttäjien muodostamien sosiaalisten verkostojen analysoimisen. Sosiaalisia verkostoja voidaan tulkita matemaattisin menetelmin ja visualisoida graafeilla. Sosiaalisten verkostojen matemaattiset analysointimenetelmät nojaavat graafiteoriaan ja matriisilaskentaan. Tietotekniikan avulla voidaan yhdistää ja automatisoida sosiaalisten verkostojen analyysin kannalta olennaiset vaiheet tiedon keräys, matemaattiset ja laskennalliset menetelmät sekä tulosten visuaalinen esittämisen graafeina. Graafien informatiivisuutta voidaan lisätä muokkaamalla visuaalisia elementtejä, kuten solmujen väriä, kokoa ja paikkaa, verkostoista laskettavien tunnuslukujen avulla. Tässä diplomityössä esitetään Suomen Lasten Parlamentti tapauksessa web-poh-jaisiin keskustelualueisiin sovellettuja sosiaalisten verkostojen laskennallisia menetelmiä sekä tulosten visualisointeja. Lisäksi esitetään uudenlainen työväline, joka yhdistää tiedonlouhinnan, matemaattisen analyysin ja visualisoinnin yhdeksi kontekstiherkäksi sovellukseksi. Laskennallisilla menetelmillä on löydetty SLP-tapauksen keskustelualueista muodostetuista verkostoista erilaisia merkittävimpiä tekijöitä. Keskusteluiden mallintamistavalla tiedosta verkostoiksi huomattiin olevan merkittävä vaikutus laskennallisiin lopputuloksiin. Tutkimuksen tuloksena havaittiin, että keskustelualueista voidaan mallintaa verkostoja, joista voidaan löytää merkittäviä tekijöitä matemaattisilla menetelmillä. Näitä tekijöitä ja matemaattisia menetelmiä sovellettaessa joudutaan soveltamaan sisällöllistä analyysiä, jotta voidaan selvittää mitä laskennalliset menetelmät kertovat mallinnetusta verkostosta. Lisäksi havaittiin, että epäsopivalla mallintamisella voidaan päätyä tilanteeseen, jossa edistyneet laskennalliset menetelmät eivät tuota lisäinformaatiota verkostosta. /Kir10The scientific interest towards SNA (Social Network Analysis) has been developing and growing along the computers and the Web. The massive amount of users on Web services and the social interactions between the users on them make analysis of user generated social networks intriguing economically as well as scientifically. Social networks can be modeled with mathematical methods and represented with graph visualizations. The mathematical methods of SNA focuses on graph theory and matrix algebra. By using computer science, the necessary phases of SNA data collection, data modeling, mathematical and computational methods, and visual representation of results with graphs can be combined and automated. Informativeness of graphs can be increased by modifying visual elements, such as size and position of nodes with respect to calculated metrics. This Master of Science Thesis describes those SNA and visualization methods that are used on case Finnish Children's Parliament web based discussion forums. In addition, a new tool is represented which combines datamining, and mathematical analysis and visualization into a single context sensitive tool. By using metrics, one can determine the most important actors in a modeled network. The method of modeling discussion forums into a network has been found to affect the results of computational metrics. Also, discussion forums have been modeled into networks, from which most important actors have been determined by using mathematical methods. When applying these metrics onto networks, qualitative research must be used to understand the context. Moreover, improper modeling of network may lead to situations where advanced metrics give no additional information about the network

Kiintoaineen eroosio ja sedimentaatio virtavesissä - luonnollisesta prosessista virtavesien ongelmaksi

Valuma-alueiden eroosio ja vedessä kulkeutuvan kiintoaineen sedimentaatio ovat luonnollisia prosesseja virtavesissä. Ne ylläpitävät virtavesien elinympäristöjen monimuotoisuutta. Ihmistoiminta, erityisesti maankäyttö, on kuitenkin merkittävästi lisännyt eroosiota ja hienon kiintoaineksen määrää virtavesissä, millä on lukuisia haitallisia vaikutuksia virtavesien ekosysteemeihin. Tässä kirjallisuuskatsauksessa kuvataan virtavesien luontaisen sekä ihmistoiminnan muuttaman kiintoaineen eroosion ja sedimentaation merkitystä virtavesissä. Katsauksessa käsitellään liiallisen kiintoainekuormituksen ja sedimentaation vaikutuksia virtavesien perustuotantoon ja vesikasvillisuuteen, pohjaeläimiin, kaloihin sekä mikrobeihin ja hajotusprosesseihin. Lisäksi käsitellään kiintoainekuormituksen ja sedimentaation arvioinnin ja vesienhoidon kannalta keskeisiä seuranta-, vesiensuojelu- ja kunnostusmenetelmiä sekä tutkimustarpeita

Fungal assemblages in predictive stream bioassessment: A cross-taxon comparison along multiple stressor gradients

Highlights • We compared fungi, invertebrates and diatoms in model-based stream bioassessment. • Fungal models virtually equaled the overall best model in precision and accuracy. • Fungi were superior in identifying streams degraded by multiple stressors. • Results strongly support the use of microbial communities in stream bioassessment.Degradation of freshwater ecosystems requires efficient tools for assessing the ecological status of freshwater biota and identifying potential cause(s) for their biological degradation. While diatoms and macroinvertebrates are widely used in stream bioassessment, the potential utility of microbial communities has not been fully harnessed. Using data from 113 Finnish streams, we assessed the performance of aquatic leaf-associated fungal decomposers, relative to benthic macroinvertebrates and diatoms, in modelling-based bioassessment. We built multi-taxon niche -type predictive models for fungal assemblages by using genus-based and sequence-based identification levels. We then compared the models’ precision and accuracy in the prediction of reference conditions (number of native taxa) to corresponding models for macroinvertebrates and diatoms. Genus-based fungal model nearly equalled the accuracy and precision of our best model (macroinvertebrates), whereas the sequence-based model was less accurate and tended to overestimate the number of taxa. However, when the models were applied to streams disturbed by anthropogenic stressors (nutrient enrichment, sedimentation and acidification), alone or in combination, the sequence-based fungal assemblages were more sensitive than other taxonomic groups, especially when multiple stressors were present. Microbial leaf decomposition rates were elevated in sediment-stressed streams whereas decomposition attributable to leaf-shredding macroinvertebrates was accelerated by nutrients and decelerated by sedimentation. Comparison of leaf decomposition results to model output suggested that leaf decomposition rates do not detect effectively the presence of multiple simultaneous disturbances. The rapid development of global microbial database may soon enable species-level identification of leaf-associated fungi, facilitating a more precise and accurate modelling of reference conditions in streams using fungal communities. This development, combined with the sensitivity of aquatic fungi in detecting the presence of multiple human disturbances, makes leaf-associated fungal assemblages an indispensable addition in a stream ecologist’s toolbox

Fungal assemblages in predictive stream bioassessment : A cross-taxon comparison along multiple stressor gradients

Highlights • We compared fungi, invertebrates and diatoms in model-based stream bioassessment. • Fungal models virtually equaled the overall best model in precision and accuracy. • Fungi were superior in identifying streams degraded by multiple stressors. • Results strongly support the use of microbial communities in stream bioassessment.Degradation of freshwater ecosystems requires efficient tools for assessing the ecological status of freshwater biota and identifying potential cause(s) for their biological degradation. While diatoms and macroinvertebrates are widely used in stream bioassessment, the potential utility of microbial communities has not been fully harnessed. Using data from 113 Finnish streams, we assessed the performance of aquatic leaf-associated fungal decomposers, relative to benthic macroinvertebrates and diatoms, in modelling-based bioassessment. We built multi-taxon niche -type predictive models for fungal assemblages by using genus-based and sequence-based identification levels. We then compared the models’ precision and accuracy in the prediction of reference conditions (number of native taxa) to corresponding models for macroinvertebrates and diatoms. Genus-based fungal model nearly equalled the accuracy and precision of our best model (macroinvertebrates), whereas the sequence-based model was less accurate and tended to overestimate the number of taxa. However, when the models were applied to streams disturbed by anthropogenic stressors (nutrient enrichment, sedimentation and acidification), alone or in combination, the sequence-based fungal assemblages were more sensitive than other taxonomic groups, especially when multiple stressors were present. Microbial leaf decomposition rates were elevated in sediment-stressed streams whereas decomposition attributable to leaf-shredding macroinvertebrates was accelerated by nutrients and decelerated by sedimentation. Comparison of leaf decomposition results to model output suggested that leaf decomposition rates do not detect effectively the presence of multiple simultaneous disturbances. The rapid development of global microbial database may soon enable species-level identification of leaf-associated fungi, facilitating a more precise and accurate modelling of reference conditions in streams using fungal communities. This development, combined with the sensitivity of aquatic fungi in detecting the presence of multiple human disturbances, makes leaf-associated fungal assemblages an indispensable addition in a stream ecologist’s toolbox

Randomised double-blind phase 3 clinical study testing impact of atorvastatin on prostate cancer progression after initiation of androgen deprivation therapy : study protocol

Introduction Blood cholesterol is likely a risk factor for prostate cancer prognosis and use of statins is associated with lowered risk of prostate cancer recurrence and progression. Furthermore, use of statins has been associated with prolonged time before development of castration resistance (CR) during androgen deprivation therapy (ADT) for prostate cancer. However, the efficacy of statins on delaying castration-resistance has not been tested in a randomised placebo-controlled setting. This study aims to test statins' efficacy compared to placebo in delaying development of CR during ADT treatment for primary metastatic or recurrent prostate cancer. Secondary aim is to explore effect of statin intervention on prostate cancer mortality and lipid metabolism during ADT. Methods and analysis In this randomised placebo-controlled trial, a total of 400 men with de novo metastatic prostate cancer or recurrent disease after primary treatment and starting ADT will be recruited and randomised 1:1 to use daily 80 mg of atorvastatin or placebo. All researchers, study nurses and patients will be blinded throughout the trial. Patients are followed until disease recurrence or death. Primary outcome is time to formation of CR after initiation of ADT. Serum lipid levels (total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL) and trigyserides) are analysed to test whether changes in serum cholesterol parameters during ADT predict length of treatment response. Furthermore, the trial will compare quality of life, cardiovascular morbidity, changes in blood glucose and circulating cell-free DNA, and urine lipidome during trial. Ethics and dissemination This study is approved by the Regional ethics committees of the Pirkanrnaa Hospital District, Science centre, Tampere, Finland (R18065M) and Tarto University Hospital, Tarto, Estonia (319/T-6). All participants read and sign informed consent form before study entry. After publication of results for the primary endpoints, anonymised summary metadata and statistical code will be made openly available. The data will not include any information that could make it possible to identify a given participant.Peer reviewe

Randomised double-blind phase 3 clinical study testing impact of atorvastatin on prostate cancer progression after initiation of androgen deprivation therapy: study protocol

Introduction Blood cholesterol is likely a risk factor for prostate cancer prognosis and use of statins is associated with lowered risk of prostate cancer recurrence and progression. Furthermore, use of statins has been associated with prolonged time before development of castration resistance (CR) during androgen deprivation therapy (ADT) for prostate cancer. However, the efficacy of statins on delaying castration-resistance has not been tested in a randomised placebo-controlled setting.This study aims to test statins’ efficacy compared to placebo in delaying development of CR during ADT treatment for primary metastatic or recurrent prostate cancer. Secondary aim is to explore effect of statin intervention on prostate cancer mortality and lipid metabolism during ADT.Methods and analysis In this randomised placebo-controlled trial, a total of 400 men with de novo metastatic prostate cancer or recurrent disease after primary treatment and starting ADT will be recruited and randomised 1:1 to use daily 80 mg of atorvastatin or placebo. All researchers, study nurses and patients will be blinded throughout the trial. Patients are followed until disease recurrence or death. Primary outcome is time to formation of CR after initiation of ADT. Serum lipid levels (total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL) and trigyserides) are analysed to test whether changes in serum cholesterol parameters during ADT predict length of treatment response. Furthermore, the trial will compare quality of life, cardiovascular morbidity, changes in blood glucose and circulating cell-free DNA, and urine lipidome during trial.Ethics and dissemination This study is approved by the Regional ethics committees of the Pirkanmaa Hospital District, Science centre, Tampere, Finland (R18065M) and Tarto University Hospital, Tarto, Estonia (319/T-6). All participants read and sign informed consent form before study entry. After publication of results for the primary endpoints, anonymised summary metadata and statistical code will be made openly available. The data will not include any information that could make it possible to identify a given participant.</p

Data Driven Social Network Analysis: Case Finnish Children’s Parliament

Tieteellinen kiinnostus sosiaalisten verkostojen analysointiin on kehittynyt ja kasvanut tietokoneiden ja webin myötä. Verkkopalveluiden suuret käyttäjämäärät ja niiden sisällä tapahtuvat käyttäjien muodostamat sosiaaliset vuorovaikutussuhteet tekevät mielenkiintoiseksi, niin markkinataloudellisesti kuin tieteellisestikin, käyttäjien muodostamien sosiaalisten verkostojen analysoimisen. Sosiaalisia verkostoja voidaan tulkita matemaattisin menetelmin ja visualisoida graafeilla. Sosiaalisten verkostojen matemaattiset analysointimenetelmät nojaavat graafiteoriaan ja matriisilaskentaan. Tietotekniikan avulla voidaan yhdistää ja automatisoida sosiaalisten verkostojen analyysin kannalta olennaiset vaiheet tiedon keräys, matemaattiset ja laskennalliset menetelmät sekä tulosten visuaalinen esittämisen graafeina. Graafien informatiivisuutta voidaan lisätä muokkaamalla visuaalisia elementtejä, kuten solmujen väriä, kokoa ja paikkaa, verkostoista laskettavien tunnuslukujen avulla. Tässä diplomityössä esitetään Suomen Lasten Parlamentti tapauksessa web-poh-jaisiin keskustelualueisiin sovellettuja sosiaalisten verkostojen laskennallisia menetelmiä sekä tulosten visualisointeja. Lisäksi esitetään uudenlainen työväline, joka yhdistää tiedonlouhinnan, matemaattisen analyysin ja visualisoinnin yhdeksi kontekstiherkäksi sovellukseksi. Laskennallisilla menetelmillä on löydetty SLP-tapauksen keskustelualueista muodostetuista verkostoista erilaisia merkittävimpiä tekijöitä. Keskusteluiden mallintamistavalla tiedosta verkostoiksi huomattiin olevan merkittävä vaikutus laskennallisiin lopputuloksiin. Tutkimuksen tuloksena havaittiin, että keskustelualueista voidaan mallintaa verkostoja, joista voidaan löytää merkittäviä tekijöitä matemaattisilla menetelmillä. Näitä tekijöitä ja matemaattisia menetelmiä sovellettaessa joudutaan soveltamaan sisällöllistä analyysiä, jotta voidaan selvittää mitä laskennalliset menetelmät kertovat mallinnetusta verkostosta. Lisäksi havaittiin, että epäsopivalla mallintamisella voidaan päätyä tilanteeseen, jossa edistyneet laskennalliset menetelmät eivät tuota lisäinformaatiota verkostosta. /Kir10The scientific interest towards SNA (Social Network Analysis) has been developing and growing along the computers and the Web. The massive amount of users on Web services and the social interactions between the users on them make analysis of user generated social networks intriguing economically as well as scientifically. Social networks can be modeled with mathematical methods and represented with graph visualizations. The mathematical methods of SNA focuses on graph theory and matrix algebra. By using computer science, the necessary phases of SNA data collection, data modeling, mathematical and computational methods, and visual representation of results with graphs can be combined and automated. Informativeness of graphs can be increased by modifying visual elements, such as size and position of nodes with respect to calculated metrics. This Master of Science Thesis describes those SNA and visualization methods that are used on case Finnish Children's Parliament web based discussion forums. In addition, a new tool is represented which combines datamining, and mathematical analysis and visualization into a single context sensitive tool. By using metrics, one can determine the most important actors in a modeled network. The method of modeling discussion forums into a network has been found to affect the results of computational metrics. Also, discussion forums have been modeled into networks, from which most important actors have been determined by using mathematical methods. When applying these metrics onto networks, qualitative research must be used to understand the context. Moreover, improper modeling of network may lead to situations where advanced metrics give no additional information about the network

Blockchain-enabled traceability system for the sustainable seafood industry

This study highlights both theoretical as well as practical contributions to improving traceability in supply chain management with special focus on the fish supply chain. As a theoretical contribution, it highlights how blockchain technology can contribute to improving supply chain transparency in various sectors by providing real-time visibility. Concerning practical contributions, this study adopted a case study approach in the Philippines to track and trace the fish supply chain from bait to plate. It deploys a project titled ‘Tracy’, a blockchain-based information technology artifact to support fishermen in the Philippines, who are facing numerous challenges in the seafood industry such as different standards and regulations, requirements for international exports, catch certification, etc. This study specifically contributes to developing a smartphone app through the Tracy project that allows fishermen to view the history of their fish captures and exchanges (such as the name of the fish species, weight, and length of the fish, date of capture, and vessel name). The update of transaction histories, the identity of the fishermen, and the amount of fish that has to be sold are additional updates also made possible by this app. This research study concludes with overall study outcomes and limitations with future research.© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.fi=vertaisarvioitu|en=peerReviewed

Elements of Antirival Accounting with sNFT

| openaire: EC/H2020/964678/EU//ATARCAAccounting with antirival tokens, i.e., accounting based on shareable units that gain value with increased use, enables efficient and effective collective action. However, most currencies are rival tokens which can naturally represent — and be exchanged to — rival goods, such as a cup of coffee. Antirival systems of account would be a natural fit for the economy of antirival goods because the logic of value creation and accounting would be compatible. It is challenging to find an allocatively efficient price for antirival goods, such as data, measured in rival units of account. We present an antirival accounting system based on Distributed Ledger Technology (DLT), where the fundamental operation is sharing instead of exchanging and study it with system dynamics models and simulations. We illustrate our arguments by presenting a system known as Streamr Awards that defines three tokens of a fundamentally novel type, shareable non-fungible token (sNFT). We present the functioning of one of these in the work allocation of a self-directed online community.Peer reviewe