Modeling intrinsic factors of inclusive engagement in Citizen Science:Insights from the participants’ survey analysis of CSI-COP

Inclusive citizen science is an emerging research topic that has been extensively studied in recent years. However, most of the previous studies focused on the development of theoretical models and practical strategies that scholars can employ to attract diverse populations to their citizen science (CS) projects. Their findings were mostly based on either conceptual ethical frameworks or empirical observations of the scholars after the completion of the specific CS project. Few studies collected socio-demographic and behavioral data of the active citizens involved in a CS project to analyze the diversity of citizens in CS. However, to the best of our knowledge, having reviewed numerous peer-reviewed papers, none of the previous studies attempted to use prospective citizen scientists’ traits to investigate intrinsic factors that may help increase the active engagement and diversity in CS. This paper presents a new inclusive citizen science engagement model based on quantitative analysis of surveys administered to 540 participants of the dedicated free informal education course ‘Your Right to Privacy Online’ (MOOC - a massive online open course) from eight countries in the EU funded project, CSI-COP (Citizen Scientists Investigating Cookies and App GDPR compliance). The surveys were filled out just after completing the training stage and before joining the project as active citizen scientists. Out of the 540 participants who completed the surveys analyzed in this study, only 170 (32%) individuals actively participated as citizen scientists in the project. Therefore, the study attempted to understand what characterizes these participants compared to those who decided to refrain from joining the project after the training stage. This study’s findings revealed several important relationships and predictors for becoming a citizen scientist based on the surveys analysis, such as age, gender, culture, education, Internet accessibility and apps usage, as well as the satisfaction with the MOOC, the mode of training and initial intentions for becoming a citizen scientist. These findings lead to the development of the empirical model for inclusive engagement in CS and enhance the understanding of the internal factors that influence individuals' intention and actual participation as citizen scientists. The devised model offers valuable insights for designing inclusive recruitment strategies, fostering positive learning experiences, addressing technological barriers, bridging the intention-engagement gap, and tailoring engagement strategies to accommodate ethnic and cultural diversity

Modelling the quality of the steel products under challenging measurement conditions

Abstract Industry is increasingly moving towards data-driven business. With an abundance of data, the problem is no longer how to get access to data but how to extract the most value from it. The extracted knowledge helps to control industrial processes efficiently and automatically. The quality of products can be improved by finding root causes behind poor quality, hence improving the yield and competitiveness of the whole plant. This thesis helps to understand the benefit of using statistical machine learning methods to improve manufacturing processes. With the methods presented, the future can be predicted based on historical data and data-driven decision support for the processes can be offered. The application area of this work is the steel industry. This work gives step-by-step advice for successfully implementing AI applications in the industry. In addition, methods for finding root causes behind poor quality are presented to improve the process and the quality of the products. Since data collected under challenging measurement conditions in the industry is never flawless, a quality model which can utilize incomplete data is also developed. Machine learning methods are used in this work to process data and to develop data-driven quality models, which help to predict the desired quality characteristics and hence support the decision-making of the workers managing the process. Both supervised and semi-supervised machine learning methods are used. In addition, explainable machine learning is used to increase the transparency of the models. With the help of these methods, predictive quality models are developed for four different datasets consisting of measurements from steel manufacturing processes. The developed models have been implemented as a smart manufacturing tool, which enables real-time support for process workers during the manufacturing process. Using the tool, a worker can recognize the factors that can cause quality problems in the process in advance. Hence, the tool allows for fast reactions to improve the quality. This work shows significant advantages the industry can obtain with a data-driven business model. By using the developed quality models, the yields of manufacturing processes were improved by better planning of material sufficiency, minimizing the amount of waste, improving the product quality, and reducing the risk of rejections. All of these methods are also entirely applicable to processes in other fields of industry such as food production, biomass drying, and health applications.Tiivistelmä Teollisuus on siirtynyt yhä enenemissä määrin kohti dataohjautuvaa liiketoimintaa. Dataa on tarjolla niin paljon, että ongelmana ei ole sen saatavuus vaan se mitä siitä saadaan irti. Datasta louhittu tieto auttaa kontrolloimaan teollisuusprosesseja tehokkaammiksi ja automaattisemmiksi sekä parantamaan tuotteen laatua löytämällä syyt huonon laadun takaa ja näin parantamaan koko tehtaan tuottoa ja kilpailukykyä. Tämä väitöskirja auttaa ymmärtämään tilastollisten koneoppimismenetelmien tärkeyden teollisuusprosesseja kehitettäessä. Näiden menetelmien avulla pystymme ennakoimaan tulevaisuutta historian avulla ja kasvattamaan dataohjautuvan päätöksenteon tukea prosesseissa. Tämän väitöskirjan sovellusalueena on terästeollisuus. Työ antaa työkaluja siihen miten AI sovellus voidaan toteuttaa onnistuneesti teollisuudessa askel askeleelta. Lisäksi työssä esitellään menetelmiä joiden avulla voidaan löytää juurisyitä huonolle laadulle ja näin kehittää prosessia tuottamaan laadukkaampia tuotteita. Koska teollisuuden haastavista olosuhteista kerätty data ei koskaan ole täydellistä, on tässä työssä kehitetty myös laatumalli, joka pystyy hyödyntämään puuttuvia tietoja. Tässä työssä käytetään koneoppimismenetelmiä, joiden tarkoituksena on prosessoida dataa ja kehittää datamukautuvia laatumalleja, joiden avulla voidaan tehdä ennustuksia haluttavasta laatuominaisuudesta, ja näin tukea prosessissa työskentelevien ihmisten päätöksentekoa. Työssä on käytetty sekä ohjattuja-, että osittain-ohjattuja koneoppimismenetelmiä sekä mallien läpinäkyyvyyttä lisääviä selittäviä koneoppimismenetelmiä. Näiden menetelmien avulla on kehitetty ennustavia laatumalleja neljän erilaisen teräsprosessia kuvaavaan datasetin avulla. Kehitetyt laatumallit on implementoitu teollisuutta varten kehitettyyn älykkään päätöksenteon työkaluun, joka mahdollistaa reaaliaikaisen tuen teollisuusprosessissa työskenteleville henkilöille. Työkalun avulla työntekijä voi jo etukäteen tunnistaa tekijät, jotka voivat aiheuttaa laatuongelmia prosessissa ja mahdollistaa näin nopean reagoinnin laadun parantamiseen. Tämä työ osoittaa kuinka merkityksellisiä hyötyjä teollisuus voi saavuttaa dataohjautuvalla liiketoiminnalla. Kehitettyjen laatumallien avulla teollisuusprosessien tuotosta on voitu parantaa materiaalin riitävyyden suunnittelulla, jätteiden minimoimisella, laadun parantamisella sekä hylkäysriskien pienentämisellä. Työssä esiteltävät menetelmät ovat täysin sovellettavissa myös muihin teollisuuden alojen prosesseihin, kuten esimerkiksi elintarvikkeiden valmistusprosessin, biomassan kuivausprosessin ja terveyssovelluksien kehittämiseen

Intelligent methods for root cause analysis behind the center line deviation of the steel strip

This article presents a statistical prediction model-based intelligent decision support tool for center line deviation monitoring. Data mining methods enable the data driven manufacturing. They also help to understand the manufacturing process and to test different hypotheses. In this study, the original assumption was that the shape of the strip during the hot rolling has a strong effect on the behaviour of the steel strip in Rolling, Annealing and Pickling line (RAP). Our goal is to provide information that enables to react well in advance to strips with challenging shape. In this article, we show that the most critical shape errors arising in hot rolling process will be transferred to critical errors in RAP-line process as well. In addition, our results reveal that the most critical feature characterizes the deviation better than the currently used criterion for rework. The developed model enables the user to understand better the quality of the products, how the process works, and how the quality model predicts and performs

Intelligent methods for root cause analysis behind the center line deviation of the steel strip

Abstract This article presents a statistical prediction model-based intelligent decision support tool for center line deviation monitoring. Data mining methods enable the data driven manufacturing. They also help to understand the manufacturing process and to test different hypotheses. In this study, the original assumption was that the shape of the strip during the hot rolling has a strong effect on the behaviour of the steel strip in Rolling, Annealing and Pickling line (RAP). Our goal is to provide information that enables to react well in advance to strips with challenging shape. In this article, we show that the most critical shape errors arising in hot rolling process will be transferred to critical errors in RAP-line process as well. In addition, our results reveal that the most critical feature characterizes the deviation better than the currently used criterion for rework. The developed model enables the user to understand better the quality of the products, how the process works, and how the quality model predicts and performs

Evidence-based and explainable smart decision support for quality improvement in stainless steel manufacturing

Abstract This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement

An online quality monitoring tool for information acquisition and sharing in manufacturing:Requirements and solutions for the steel industry

The purpose of this study was to develop an innovative online supervisor system to assist the operators of an industrial manufacturing process in discovering new solutions for improving both the products and the manufacturing process itself. In this paper, we discuss the requirements and practical aspects of building such a system and demonstrate its use and functioning with different types of statistical modelling methods applied for quality monitoring in industrial applications. The two case studies presenting the development work were selected from the steel industry. One case study predicting the profile of a stainless steel strip tested the usability of the tool offline, while the other study predicting the risk of roughness of a steel strip had an online test period. User experiences from a test use period were collected with a system usability scale questionnaire.</p

Data center for biomass drying

Arctiq-DC is a InterReg North funded project with a total budget of about €1´430´000 where 9 partners from Sweden and Finland are collaborating: Oulu University, Oulun Data Center, Aurora Data Center, SFTec, Xarepo, Hushållningssällskapet, Älvsbyns municipality, Hydro 66 and RISE Research Institutes of Sweden as coordinator. The project duration is almost three years and consist of six main activities where the fourth is about cooling and heat reuse from data center. This report describes the trails that were made for evaluating data center excess heat as heat source for biomass drying.ArctiqDC – Arctic data centers 2019 - 2021  </p

AI enhanced alarm presentation for quality monitoring

Abstract This paper presents an AI based method for improving poorly performing quality prediction models. The method improves automatically the usability of the low quality alarm predictions in the web based quality monitoring tool that provides decision support for users. The tool enables the utilization of the models that suffer from the lack of information because of a long time gap to the predicted future. The reliability of the presented alarms in a monitoring tool will be improved by reducing the amount of false alarms

Recognizing steel plate side edge shape automatically using classification and regression models

Abstract In the steel plate production process it is important to minimize the wastage piece produced when cutting a mother steel plate to the size ordered by a customer. In this study, we build classi?cation and regression models to recognize the steel plate side edge shape, if it is curved or not and the amount of curvature. This is done based on time series data collected at the manufacturing line. In addition, this information needs to be presented in a way that enables fast analysis and long-term statistical monitoring. It can then be used to tune the parameters of the manufacturing process so that optimal curvature can be found and the size of the wastage piece can be reduced. The results show that using the classi?cation and linear regression methods, the side edge shape can be recognized reliably and the amount of curvature can be estimated with high accuracy as well