Health Monitoring of Ground Support System Through Point-Cloud Processing: Rockbolts Extraction Phase

Safety in underground mining operations relies on understanding the geological and geotechnical properties of the site. The creation of an underground void for mining induces instability in the rock structure, resulting in deformation. The compressive strength of the rocks is maintained by the tensioning of ground support, such as rockbolts. Monitoring and predicting the condition of the mining ground support system is crucial for ensuring the safety of operations. Inspecting the mining tunnels poses challenges due to their large span and ongoing production activities. Light Detection and Ranging (LiDAR) technology can scan physical structures and generate point cloud data, which is valuable for creating applications like topographic mapping and spatial models. Extracting rockbolt information from point cloud data from underground mines can offer comprehensive mine coverage. This information can be utilised to monitor the condition of the rockbolts over time. Extracted rockbolt data can assist in the health monitoring of ground support, indicating deformation due to geostatic pressure. This paper proposes a method for extracting rockbolt spatial information from point cloud datasets collected via LiDAR technology to facilitate Prognostics and Health Management for ground support in underground mining.Funder: Mining Innovation for Ground Support (MIGS);Fulltext license: CC BY;This article has previously appeared as a manuscript in a thesis. </p

Handling Novel and Out-Of-Distribution Data in Deep Learning : OOD Detection and Shortcut Mitigation

Advancements in machine learning, and particularly deep learning, have revolutionized the real-world applications of artificial intelligence in recent years. A main property of deep neural models is their ability to learn a task based on a set of examples, that is, the training data. Although the state-of-the-art performance of such models is promising in many tasks, this of-ten holds only as long as the inputs to the model are “sufficiently similar” to the training data. Mathematically, a ubiquitous assumption in machine learning studies is that the test data used for evaluating a model are sampled from the same probability distribution as the training data. It is challenging to approach any problem where this assumption is violated, as it requires handling Out-Of-Distribution (OOD) data, i.e., data points that are systematically different from the training (in-distribution) data. In particular, one might be interested in detecting OOD inputs at test time given an unlabeled training set, which is the main problem explored in this thesis. This type of OOD detection (a.k.a. novelty/anomaly detection) has various applications in discovering unusual events and phenomena as well as improving safety in AI systems. Another challenging problem in deep learning is that a model might rely on certain trivial relations (spurious correlations) existing in training data to solve a task. Such “shortcuts” can bring a high performance on in-distribution data, but they may collapse on more realistic OOD data. It is therefore vital to mitigate the shortcut learning effects in deep models, which is the second topic studied in this thesis. A part of the present thesis is concerned with leveraging pretrained deep models for OOD detection on images, without modifying their standard training algorithms. A method is proposed to use invertible (flow-based) generative models based on null hypothesis testing ideas, leading to an OOD detection method that is fast and more reliable than the traditional likelihood-based method. Diffusion (score-based) models are another type of modern generative models used for OOD detection in this thesis, in combination with pretrained deep encoders. Another contribution of the thesis is in leveraging the power of large self-supervised models in fully unsupervised fine-grained OOD detection. It is shown that the simple k-nearest neighbor distance in the representation space of such models results in a reasonable performance but can be boosted substantially through the proposed adjustments, without any model fine-tuning. The local geometry of representations and background (irrelevant) features are considered to this end. OOD detection with time series data is another problem studied in this thesis. Specifically, a method is proposed based on Contrastive Predictive Coding (CPC) self-supervised learning, and applied to detect novel categories in human activity data. It is demonstrated, both empirically and through theoretical motivation, that modifying the CPC to use a radial basis function instead of the conventional log-bilinear function is a requirement for reliable and efficient OOD detection. This extension is combined with quantization of representation vectors to achieve better performance. This thesis also addresses the problem of learning deep representations (transfer learning) in a situation where a shortcut exists in data. In this problem, a deep model is trained on a shortcut-biased image dataset to solve a self-supervised or supervised classification task. The representations learned by this model are used to train a smaller model on a related but different downstream task, and the adverse effect of the shortcut is verified empirically there. Moreover, a method is proposed to enhance the representation learning in this scenario, based on an auxiliary model trained in an adversarial manner along with the upstream classifier

Död i krigets skugga : mortalitet och dödsorsaker i Umeå landsförsamling och på Röbäcks sjukhus under finska kriget 1808–1809

Finska kriget 1808–1809 utkämpades mellan Sverige och Ryssland som en del av Napoleonkrigen. I skuggan av de storpolitiska händelserna utspelade sig en mortalitetskatastrof som drabbade inte bara soldaterna, utan även i hög grad den civila befolkningen. Genom bevarat källmaterial kan vi i dag få en unik inblick i hur både befolkningen och soldaterna i Umeå med omnejd påverkades.The Finnish War of 1808–1809 was fought between Sweden and Russia as part of the Napoleonic Wars. In the shadow of these geopolitical events, a mortality catastrophe unfolded, affecting not only the soldiers but also, to a great extent, the civilian population. Through preserved source material, we can today gain a unique insight into how both the population and the soldiers in Umeå and its surroundings were affected.Suomen sota vuosina 1808–1809 käytiin Ruotsin ja Venäjän välillä osana Napoleonin sotia. Näiden geopoliittisten tapahtumien varjossa kehkeytyi kuolleisuuskatastrofi, joka vaikutti paitsi sotilaisiin myös suuressa määrin siviiliväestöön. Säilyneen lähdeaineiston ansiosta voimme tänä päivänä saada ainutlaatuisen käsityksen siitä, miten sekä väestö että sotilaat Uumajassa ja sen ympäristössä kärsivät tilanteesta.Финская война 1808–1809 годов велась между Швецией и Россией как часть Наполеоновских войн. В тени этих геополитических событий разразилась катастрофа смертности, затронувшая не только солдат, но и в значительной степени гражданское население. Благодаря сохранившимся источникам мы сегодня можем получить уникальное представление о том, как эти события повлияли как на население, так и на солдат в Умео и его окрестностях

The Problems with Genetic Essentialism, Determinism, and Reductionism

In this article, we address misunderstandings about genetic essentialism, genetic determinism, and genetic reductionism. There are good reasons to carefully consider the meanings and relationships that characterize genetic essentialism, genetic determinism, and genetic reductionism; these are different (if related) concepts, despite their superficial resemblances. Although a recent Nature Reviews Genetics article addressed these issues, problems intrinsic to them remained unexplored, problems that we address here by adopting a developmental systems perspective. Discussions of these concepts should explicitly communicate that genetic essentialism fails because individuals are as they are in part due to the contexts in which they develop, that genetic determinism fails because all phenotypes depend on co-acting genomic and non-genomic factors for their development, and that genetic reductionism fails because emergent properties above the level of the genome can feed back and influence the subsequent functioning of that genome. Elucidating the meaning of these concepts without providing arguments for rejecting them is problematic. Developmental science offers the required arguments

"En man är inte en fågel" : Könsroller och hegemonisk maskulinitet i En handelsresandes död

This essay examines traditional gender roles in Arthur Miller’s Death of a Salesman (1949). Using gender theory, including hegemonic masculinity and masculinity as a homosocial enactment, the essay explores how the play critiques traditional gender roles and essentialist ideas on gender. This takes place in an American post-war setting in which women are subordinate to men, but also men being subordinate to and marginalized by other men. The analysis focuses on the characters of Willy Loman and Biff Loman, as well as their relationships with other characters, and the difficulties they face in relation to post-war expectations of masculinity. The analysis reveals that Willy exhibits traits traditionally associated with women,and how gender roles are reversed in his relationships with female characters. The analysis also shows that Willy tries to conform to the hegemonic ideals of masculinity, but fails, which makes him a complicit man. As for Biff, the analysis shows that he rejects the prevailing masculinity ideals. Moreover, he contradicts and challenges traditional gender roles by questioning the prevailing norms within the Loman household. In conclusion, Death of a Salesman critiques essentialism and undermines traditional gender roles

Läslistans bilderböcker: En tematisk och formmässig undersökning av berättande och illustrationer

I uppsatsen valdes tolv bilderböcker ut från läslistan för grundskolan som är framtagen avSkolverket och Kulturrådet. Dessa analyseras med hjälp av bilderboksanalys. Begreppen somanvänts är ikonotext, metafiktion, hemma- och bortasida, intrakoniska texter och pageturner.Syftet med analysen är att undersöka vilka dominerande teman som finns i det urval avbilderböcker som jag gjorde. Slutsatsen är att teman som barn i stor utsträckning kan relateratill som längtan efter gemenskap, sorg och bearbetning och läsningens magi är dominerande.Dessa teman gestaltas oftast genom användandet av de stilistiska begreppen ovan. Vidare serjag likt Kulturrådet och Skolverket ett behov av att hitta fler ingångar att inte bara läsa textenutan även förstå dess ikonotextuella betydelse

AI-driven identifiering av referensprojekt för arkitektoniska anbud : En datadriven metod

The identification of suitable reference projects is a critical yet time-consuming aspect of the architectural tendering process. This thesis investigates how arti- ficial intelligence (AI) can be leveraged to automate and optimize this task, fo- cusing on Cedervall Arkitekter as a case study. A data-driven retrieval system was developed to mine internal datasets—specifically the Milltime database— encompassing both structured project metadata and unstructured user notes. After evaluating multiple AI methods, an embedding-based retrieval approach integrated with keyword filtering was selected, striking a balance between computational efficiency and retrieval accuracy. Deployed on-premise as a web application, the final solution enables ar- chitects and procurement staff to query project records using natural language inputs. The system applies semantic similarity modeling and a customized ranking algorithm to provide rapid, relevant search results, cutting manual search time by more than 50% according to user testing. Structured interviews further demonstrated its capacity to enhance the reference project selection process and reduce reliance on personal memory. Taken together, these find- ings underscore the value of AI-driven retrieval systems in architectural prac- tices, while highlighting promising directions for expanded machine learning integration within tendering and other knowledge-intensive workflows in the architecture, engineering, and construction (AEC) sector.Identifieringen av lämpliga referensprojekt är en avgörande men tidskrävan- de del av anbudsprocessen inom arkitektur. I detta examensarbete undersöks hur artificiell intelligens (AI) kan användas för att automatisera och effekti- visera denna uppgift, med Cedervall Arkitekter som fallstudie. Ett datadrivet söksystem utvecklades för att analysera interna datakällor—särskilt Milltime- databasen—som innehåller både strukturerad projektinformation och ostruk- turerade användarnoteringar. Efter att flera AI-metoder utvärderats valdes en lösning baserad på inbäddade representationer i kombination med nyckelords- filtrering, vilket ger en god balans mellan beräkningskostnad och söknoggrann- het. Systemet, som körs lokalt som en webbapplikation, gör det möjligt för arki- tekter och upphandlingspersonal att söka projektposter med hjälp av naturliga språk. Med semantisk likhetsberäkning och en anpassad rankningsalgoritm le- vererar systemet snabba och relevanta träffar, vilket kortar ned manuell söktid med över 50%. Strukturerade intervjuer visar dessutom att verktyget förbätt- rar processen för att hitta referensprojekt och minskar beroendet av personliga minnesanteckningar. Sammantaget belyser resultaten hur AI-baserade söksy- stem kan gynna arkitektverksamheter och öppnar för ytterligare användning av maskininlärning inom anbudsarbete och andra kunskapsintensiva områden i arkitektur-, ingenjörs- och byggsektorn (AEC)

Prevalence Rates of Bullying: A Comparison Between a Definition-Based Scale and a Behavior-Based Scale

Self-reported measures of school bullying can be divided into two subtypes. Definition-based measures present a bullying definition followed by one question about being bullied and one question about bullying others, while behavior-based measures avoid using terms like "bully" and "bullying," do not provide an explicit bullying definition, include items describing specific bullying behaviors, and respondents are asked to rate how often they have engaged in or have been a target of each behavior. The current study aimed to compare bullying perpetration and victimization prevalence rates between a definition-based scale and a behavior-based scale. The current study was part of a 4-year longitudinal research project, where students in Sweden completed an annual web-based survey at five waves starting with the school year of 2015 to 2016 (Wave 1; approximately age = 10.5 years) and ending in the school year of 2019 to 2020 (Wave 5; approximately age = 14.5 years). Because they responded to both measurement conditions, the study controlled for their possible individual differences. In this study, data from 1,469 to 1,715 students were analyzed. Findings revealed that the behavior-based scale displayed higher bullying perpetration and victimization prevalence than the definition-based scale. The behavior-based scales used in this study offer researchers and practitioners a self-report bullying measurement that includes power imbalance, concrete, and specific negative behaviors, and the ability to estimate repetition, but without using bullying terminology. Still, pros and cons of both approaches can be further discussed, and both definition-based and behavior-based self-report measures are vulnerable to a number of biases while they provide estimates or approximations-not exact pictures-of bullying prevalence.Funding Agencies|Swedish Research Council [D0775301]</p

The Affordances of Metaphors in Meaning-Making of Nutrient Uptake in Upper Primary School

Previous research suggests that the use of metaphors in science education have both possibilities and challenges. In this study, we analyse the role of metaphors in meaning-making in the upper primary science classroom. We investigate the potential of metaphors about nutrient uptake occurring in classrooms in which an animation was used. To identify metaphors in the classroom interaction, we have applied an analysis according to systemic-functional grammar (SFG), rooted in social semiotic theory. The present study indicates that the use of metaphors can play an important role in scientific meaning-making, since, in that way, students and teachers can make meaning about scientific processes and functions before having access to the scientific terminology. However, if metaphors are to be functional tools for meaning-making in science education, the teacher has an important role to play in, among other things, explicitly connecting the metaphors and everyday language to scientific concepts. We argue that metaphors based on functional similarity have a high affordance for making meaning about complex processes, such as nutrient uptake.Funding Agencies|Stockholm University</p