MoodFoam:an atmospheric evaluation of multi-spaces

Abstract. Understanding spatial experience is a multi-faceted problem which requires using tools from various fields. Spaces are rarely evaluated after they are built, and the spaces might not be used the way they were intended. This can lead to a lacking knowledge on how the spaces work or how they could be improved. This thesis aims to tackle this challenge by developing a methodology to gather data from a multi-space. To achieve this, a MoodFoam web application was developed for user surveys, and environmental sensor data was gathered from Tellus, a multi-space in the University of Oulu. The theoretical background of this thesis utilizes understanding from the fields of architecture, organization theory, philosophy, and psychology. The subjective and contextual user data and the objective sensor data was gathered successfully during the study’s two-week data gathering period. The data was analyzed and visualized with statistical programming language R to highlight various aspects of the spatial experience in Tellus. As a result, the methodology was able to produce subjective data with a time-space information allowing for a broader understanding of the multi-space and its users. The results suggest that even close-by spaces can have different atmospheres. Furthermore, the results found differences in the subjective experiences in the researched spaces, in terms of smell, temperature, and sound. The presented methodology can be further used in various contexts to improve its explicative capabilities.MoodFoam : monitilojen ilmapiirievaluaatio. Tiivistelmä. Tilakokemuksen ymmärtäminen on monitahoinen ongelma, joka vaatii eri alojen menetelmien hyödyntämistä. Tiloja arvioidaan harvoin niiden rakentamisen jälkeen, eikä niitä välttämättä käytetä niille tarkoitetulla tavalla. Tämä voi johtaa puutteelliseen ymmärrykseen tilojen toiminnasta tai siitä, miten niitä voisi kehittää. Tämä diplomityö pyrkii ratkaisemaan ongelman kehittämällä metodologia monitilan datankeruuseen. Tätä varten kehitettiin MoodFoam-internetsovellus käyttäjäkyselyille ja sensoridataa kerättiin Telluksesta, monitilasta Oulun yliopistolla. Diplomityön teoreettinen tausta hyödyntää arkkitehtuurin, organisaatioteorian, filosofian ja psykologian tarjoamaa ymmärrystä. Subjektiivinen ja kontekstuaalinen käyttäjädata sekä objektiivinen sensoridata kerättiin onnistuneesti diplomityön kahden viikon datankeruun aikajaksolta. Data analysoitiin ja visualisoitiin tilastollisella R-ohjelmointikielellä Telluksen moninaisten tilakokemusten korostamiseksi. Diplomityön tuloksena metodologia onnistui tuottamaan subjektiivista aika–paikka-tietoa mahdollistaen laajemman ymmärryksen monitiloista ja sen käyttäjistä. Tulokset viittaavat siihen, että lähekkäisilläkin tiloilla voi olla oma ilmapiiri. Lisäksi tuloksista löytyi eroja tutkittujen tilojen subjektiivisissa kokemuksissa hajun, lämpötilan ja äänen suhteen. Esiteltyä metodologiaa voidaan edelleen hyödyntää lukuisissa konteksteissa tehokkaamman kuvaavuuden kehittämiseksi

Perceptions and Realities of Text-to-Image Generation

Generative artificial intelligence (AI) is a widely popular technology that will have a profound impact on society and individuals. Less than a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on many creative domains. In this paper, we present the findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their fears and concerns, and thoughts about risks and dangers of text-to-image generation to the individual and society. We find that while participants were aware of the risks and dangers associated with the technology, only few participants considered the technology to be a personal risk. The risks for others were more easy to recognize for participants. Artists were particularly seen at risk. Interestingly, participants who had tried the technology rated its future importance lower than those who had not tried it. This result shows that many people are still oblivious of the potential personal risks of generative artificial intelligence and the impending societal changes associated with this technology.Comment: ACM Academic Mindtrek 202

Eliciting Empathy towards Urban Accessibility Issues

Empathy is an integral part of what it means to be human. Empathy refers to the ability to sense other people's emotions, coupled with the ability to imagine what they might be thinking and feeling. Architectural and urban design have identified empathy as a crucial factor in the design process and especially in user-centered participatory methods. Although empathy has been recognized as important for relating to other people's issues, current research has not explored how urban accessibility issues elicit empathy. We conducted a between-subjects online study where 202 participants observed five scenarios on different accessibility issues. Our results show that empathic traits and previous experience are significant factors in empathizing with accessibility issues. Additionally, storytelling and photos can influence perceptions of accessibility issues. The study highlights the importance of empathic traits and personal experience in understanding and addressing accessibility issues, as well as the potential of storytelling and photos in shaping perceptions of accessibility issues and evoking empathy. Our contribution demonstrates the advantages of incorporating narrative multimedia into design processes for improved urban accessibility.</p

Text-to-Image Generation: Perceptions and Realities

Generative AI is an emerging technology that will have a profound impact on society and individuals. Only a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on creative domains. In this paper, we present the key findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their ideas for potential application areas, as well as concerns, risks, and dangers of text-to-image generation to society and the individual. The study found that participants were aware of the risks and dangers associated with the technology, but only few participants considered the technology to be a risk to themselves. Additionally, those who had tried the technology rated its future importance lower than those who had not.Comment: Accepted at Generative AI in HCI workshop, CHI '2

Kaksoisenergiatietokonetomografia - lisäarvoa päivystyskuvantamiseen?

Kaksoisenergiatietokonetomografia (KETT) on menetelmä, jossa tietokonetomografian (TT) tarkkuuteen havaita ja luokitella poikkeavia löydöksiä pystytään edelleen vaikuttamaan tehostamalla kudoserottelua. KETT:llä voidaan myös vähentää artefakteja tai kohinaa ja materiaalien erottelukyvyn perusteella esimerkiksi suoraan kuvantaa jodin kertymistä kudoksiin. KETT-sovelluksia on jo monipuolisesti, myös päivystyskuvantamiseen. Virtuaaliset natiivisarjat mahdollistavat sädeannoksen tutkimuskohtaisen vähentämisen. Samaan suuntaan vaikuttaa mahdollisten jatkotutkimusten väheneminen, kun diagnostiikka tarkentuu

“Notame”: Workflow for non-targeted LC-MS metabolic profiling

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

“Notame”: Workflow for non-targeted LC-MS metabolic profiling

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting

Diagnostic yield of genetic testing in a heterogeneous cohort of 1376 HCM patients

Background Genetic testing in hypertrophic cardiomyopathy (HCM) is a published guideline-based recommendation. The diagnostic yield of genetic testing and corresponding HCM-associated genes have been largely documented by single center studies and carefully selected patient cohorts. Our goal was to evaluate the diagnostic yield of genetic testing in a heterogeneous cohort of patients with a clinical suspicion of HCM, referred for genetic testing from multiple centers around the world. Methods A retrospective review of patients with a suspected clinical diagnosis of HCM referred for genetic testing at Blueprint Genetics was undertaken. The analysis included syndromic, myopathic and metabolic etiologies. Genetic test results and variant classifications were extracted from the database. Variants classified as pathogenic (P) or likely pathogenic (LP) were considered diagnostic. Results A total of 1376 samples were analyzed. Three hundred and sixty-nine tests were diagnostic (26.8%); 373 P or LP variants were identified. Only one copy number variant was identified. The majority of diagnostic variants involved genes encoding the sarcomere (85.0%) followed by 4.3% of diagnostic variants identified in the RASopathy genes. Two percent of diagnostic variants were in genes associated with a cardiomyopathy other than HCM or an inherited arrhythmia. Clinical variables that increased the likelihood of identifying a diagnostic variant included: an earlier age at diagnosis (p <0.0001), a higher maximum wall thickness (MWT) (p <0.0001), a positive family history (p <0.0001), the absence of hypertension (p = 0.0002), and the presence of an implantable cardioverter-defibrillator (ICD) (p = 0.0004). Conclusion The diagnostic yield of genetic testing in this heterogeneous cohort of patients with a clinical suspicion of HCM is lower than what has been reported in well-characterized patient cohorts. We report the highest yield of diagnostic variants in the RASopathy genes identified in a laboratory cohort of HCM patients to date. The spectrum of genes implicated in this unselected cohort highlights the importance of pre-and post-test counseling when offering genetic testing to the broad HCM population.Peer reviewe

"Notame": Workflow for Non-Targeted LC-MS Metabolic Profiling

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting