DISA

You are welcome to attend Manoranjan Kumar’s final seminar (doctoral 90% control). The seminar will include a presentation and a follow-up discussion with Mirka Kans, Associate Professor at the Department of Mechanical Engineering.

When: Wednesday, December 17, 9:00–12:00
Where: Room D1167 and on Zoom

Title: Digital Twins for Construction Equipment

Abstract: The technology organization at Volvo Construction Equipment (VCE) aims to predict and verify the performance of machines like Wheel loaders (WL) and Articulated haulers (AH) to enhance product development, requirements engineering, and customer service etc. Hence, it is needed to virtualize machines and components using the existing sensors on the machines and infrastructure of the central server. Over the years, virtualization has been achieved through the use of digital twins (DTs) across different industries, but realizing it on dynamically complex machines has its own challenges. It is also an important step in this digital transformation journey.

What is the specific research question to be answered?

This PhD thesis describes and investigates how the digital twin (DT) needs to be developed for machines like AH, and more specifically for WL. Further, a variety of actions are needed to incorporate into the framework of the DT. The framework needs to support different machines and their predictive journey, which can be different based on their usage and where it is being used. 

What are the means and methods used by the authors to answer the stated question?

This DT is the virtual replica of the physical machines that feed the twins (simulation model) with data from sensors and edge-based algorithms. The algorithms are built using a machine learning (ML) model. The algorithms that are implemented into machines are often called machine logs or virtual sensors. Further, a high-fidelity simulation supports the different force-driven maneuvers of different machine operators. A new co-simulation framework has been developed that integrates the operators’ model of the wheel loader (WL) and its interaction with the power source model, i.e., the drive train, the hydraulics, and the material. By using the simulations and physical machine data, visualizations are built to illustrate the results, which support various departments in providing customers with predictive services.

What is the answer to the research question?

The edge-based virtual sensors align well with their accuracy in predicting different failures in the machines. Furthermore, the results show that the co-simulation model aligns well with measurement data, validating the model’s accuracy in different types of machine operator driving. The integration of virtual sensors, machine logs, simulation, and results visualization paves the way for a successful DT of the machines.

Why is the answer important and for whom specifically?

The results are useful for engineers in product development, sales, and the aftermarket to create services and develop the machines for future generations.

How does the answer inspire future research?

The successful validation of the framework also paves the way for future research to enhance the virtual simulation techniques for WL and AH performances with different types of machine operators. It also paves the way and inspires to improve ML algorithms on the edge and, therefore, create services under the shadow of DTs. 

When? Friday November 14, 14-16
Where? Onsite: D2273 and via zoom

Agenda
14.00-14.10 Welcome and practical information
14.10-14.55 Presentation and discussion: 
Artificial ‘ulama – Analyzing AI-Generated Islamic Theology – Jonas Svensson 
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion:  
Socio-Technical Considerations on Inter- and Intra-Organizational Sustainability Data Sharing – Anna Sell  
15.50 -16.00 Sum up and plan for our seminars in December

Abstracts
Artificial ‘ulama – Analyzing AI-Generated Islamic Theology – Jonas Svensson
The presentation provides information on, and preliminary findings from my research project Artificial ‘Ulama, examining how artificial intelligence systems produce Islamic theological content. The study focusses on how modern Large Language Models interpret and respond to prompts based on Islamic texts, concepts, and interpretational frameworks.

The presentation will focus preliminary results on having LLMs translate and interpret the Qur’an, produce views on  inter religious dialogue and producing synthetic data. 

Socio-Technical Considerations on Inter- and Intra-Organizational Sustainability Data Sharing – Anna Sell
In response to new sustainability reporting requirements, such as the EU Corporate Sustainability Reporting Directive (CSRD), companies are increasingly expected to collect and share sustainability data, not only within their own operations but across entire supply chains. Most manufacturing companies operate in multiple supply chains and must adapt to the varying data requirements of each. The cross-organizational scope, unclear data expectations and lack of standardization make sustainability data particularly challenging to work with. Internally, companies’ existing data infrastructures and reporting capabilities are tailored to traditional business data, making them ill-suited for the complex and heterogeneous nature of sustainability data. In this research we explore the paradoxes and barriers that companies must navigate in order to move from compliance-driven reporting to value-creating use of sustainability data.  

When? Thursday November 6, 10-12
Where? Onsite D1172 and via zoom
Registration: No registration needed – just come by

Abstract
Transcribing numbers and Receipts with Generative AI – Nemi Pelgrom
This dissertation investigates the usability of multi-modal language models (MMLMs) as transcription tools, with a focus on their reliability, limitations, and error mechanisms in document parsing tasks.

The work addresses four research questions across three studies. First, the potential of vision-capable generative models for extracting structured information from complex financial documents is evaluated using GPT-4. Tested on 1,000 digital invoices and 1,000 photographic receipts, the model achieved near-perfect accuracy, 99.8\% and 99.5\% respectively, with an additional API-based trial reaching 94.4\%. Second, the capacity of MMLMs to transcribe long numerical strings is explored, showing that GPT-4 and GPT-4o maintain 100\% accuracy up to 75 digits, after which performance drops sharply. Third, systematic error patterns are identified in transcription of random number sequences; mistakes consistently occur in the same positions across repeated runs, and hallucinated digits account for only 23\% of total errors, indicating biases and structured failure modes rather than noise. Lastly, a framework for categorisation of transcription errors is introduced, based on the analysis of 5,502 mistakes across GPT-4o and ARIA.

This reveals three mutually exclusive categories, and a detailed examination of ways to automatically distinguish between them, where the Ratcliff/Obershelp similarity was found to be highly useful. Together, these findings demonstrate that state-of-the-art MMLMs can already be deployed in production settings where accuracy and scalability are critical, while also providing systematic methods for diagnosing their weaknesses and guiding future model development.

When? Friday 24 October,14-16
Where? Onsite: D1172 and via zoom
Registration: Please sign up for the seminar via this link, https://forms.gle/m3nRqxaQmnv8zETb6 by 20 October.

Agenda
14.00-14.10 Welcome and practical information
14.10-14.55 Presentation and discussion: 
The Self-Healing Hypochondriac: Confessions of an AI Nerd: From Skeleton Avatar Technology to medical insights and future directions in AI for eHealth – Welf Löwe
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion:  The Journey and Lessons of Emerging Technologies in Education: Insights from the European Project Exten.(D.T.)² – Alisa Lincke
15.50 -16.00 Sum up and plan for our seminars in November

Abstract
The Self-Healing Hypochondriac: Confessions of an AI Nerd
From Skeleton Avatar Technology to medical insights and future directions in AI for eHealth – Welf Löwe

This talk introduces our Skeleton Avatar Technology (SAT), an AI-based approach to video motion analysis. We will present ongoing research, recent results, commercialization efforts, and future directions, highlighting how SAT can provide valuable medical insights and transform applications in healthcare and elderly care. In addition, we will briefly outline our other research activities in AI and eHealth.

The Journey and Lessons of Emerging Technologies in Education: Insights from the European Project Exten.(D.T.)² – Alisa Lincke

This seminar introduces the Exten.(D.T.)² project (Extending Design Thinking with Emerging Digital Technologies), a Horizon Europe / Innovate UK initiative (2022–2025). The project enhances Design Thinking in schools by integrating technologies such as AI, augmented reality, robotics, 3D printing, with a main focus on authorable learning analytics and dashboards. Implemented in six European countries, it explores both the opportunities and challenges of using these tools to foster creativity, collaboration, problem solving, and digital literacy. Personal experiences from European research projects will be shared, with reflections on lessons learned in cross-national collaboration.

Agenda
When? Friday 22 August,14-15
Where? Onsite: D1172 and via zoom
Registration: Please sign up for the seminar via this link: https://forms.gle/KsDUyvyffJdk8wZN9 by 20 august.

Abstract

Animating maths and physics – Alexander Gustafsson
In this presentation, I will share insights from running my YouTube channel, which focuses on animated content in physics and mathematics. With currently 150,000–200,000 views per month and around 35,000 subscribers, the channel has grown into a prominent platform for this type of content.

The talk will be accessible to a broad audience, in line with the channel’s overall philosophy: to make complex topics engaging and understandable. Alongside physics, mathematics and coding, the presentation also touches on education, music, and occasionally art.

When? Friday, 23 May, 14-15
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/SS8cDDJBfRHDQBWZ7 by May 21

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Distributional Reinforcement Learning – Björn Lindenberg
14.55 – 15.00 Sum up

Abstracts

Distributional Reinforcement Learning – Björn Lindenberg
Distributional Reinforcement Learning (DRL) represents a recent and successful paradigm shift in reinforcement learning, especially for algorithms based on deep learning. Instead of estimating only expected returns, DRL agents learn the full distribution of possible outcomes — offering a richer representation and greater flexibility for algorithm design. This approach has led to improved empirical performance in complex environments and enables new capabilities, such as risk-sensitive behavior. The talk will serve as an introduction to the subject, presenting the theory and possible applications in an accessible way.

When? Friday May 16th 14-16
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/DKAh2iCN5EGEth9F6 by May 14th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Secure On-Premises Deployment of Large Language Models for Enhanced Patent Drafting – Homam Mawaldi, AWA
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Enhancing E-commerce Personalization with a Hybrid Recommendation and Advanced Search System – Kailash Chowdary Bodduluri, Enode
15.50 -16.00 Sum up and plan for our seminars in June

Abstracts

Secure On-Premises Deployment of Large Language Models for Enhanced Patent Drafting – Homam Mawaldi, AWA

Patent drafting is a complex and high-stakes process for securing intellectual property rights. During the patent prosecution phase, maintaining confidentiality is crucial, which makes cloud-based third-party services inadequate. This study explores the feasibility of AWACopilot, a secure, on-premise solution comprising a web service that leverages open-source large language models (LLMs) to assist patent attorneys in the intricate patent application drafting process. AWACopilot generates key patent sections such as background, abstract, detailed description, etc., from human-crafted claims, addressing the data security risks posed by cloud-based AI services. Its modular architecture enables customization and adaptability to different patent tasks. Although challenges remain—including reliance on LLM capabilities and the need for rigorous content verification—this study demonstrates the potential for secure, AI-driven solutions to enhance patent drafting workflows.

Enhancing E-commerce Personalization with a Hybrid Recommendation and Advanced Search System – Kailash Chowdary Bodduluri, Enode

In the evolving landscape of e-commerce, personalizing user experience through recommendation systems has become a way to boost user satisfaction and engagement. However, small-scale e-commerce platforms struggle with significant challenges, including data sparsity and user anonymity. These issues make it hard to effectively implement recommendation systems, resulting in difficulty in recommending the right products to users. This study introduces an innovative Hybrid Recommendation System (HRS) to address challenges in e-commerce personalization caused by data sparsity and user anonymity. By blending multiple dimensions of the data into one unified system for producing recommendations, this system represents a notable advancement in achieving personalized user experiences in the context of limited data. In addition to the recommendation system, we have also developed an effective search feature with capability of leveraging fuzzy matching, TF-IDF vectorization, and a Swedish language synonym model for query expansion. Our current research focuses on integrating these two independent systems—recommendations and search—to address their individual limitations and create a unified discovery ecosystem. By combining explicit search behaviors with implicit user preferences and exploring technologies such as large language models and sequential recommendation frameworks, we aim to further improve and optimize product discovery in data-sparse environments.

When? Friday April 25th 14-16
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/BTAE5wY4XW9TDB2A9 by April 23 (especially important if you plan on attending onsite so we have fika for everyone)

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Enhancing Efficiency in Industry: Automation, Analytics, and Digital Twins – Arslan Musaddiq
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Machine-Learning Models for Two-Dimensional Antiferromagnetic Materials – Shahid Sattar
15.50 -16.00 Sum up and plan for our seminars in May

Abstracts

Enhancing Efficiency in Industry: Automation, Analytics, and Digital Twins –Arslan Musaddiq
This presentation will be about ongoing industry collaborations focused on data-driven optimization in manufacturing, energy, and logistics. These projects aim to improve operational efficiency, enhance decision-making, and drive digital transformation across various sectors. The presentation will cover the automation of waste input measurement and real-time thickness monitoring in construction board production, energy data analysis for baseload identification and prediction, digital twin development for sawmill process optimization, and scheduling optimization for mobile blood donation units. The seminar will explore the challenges, methodologies, and practical impact of integrating smart systems into traditional industries.

Machine-Learning Models for Two-Dimensional Antiferromagnetic Materials – Shahid Sattar
Two-dimensional antiferromagnets (2D AFMs) recently gained tremendous scientific interest owing to their use in next-generation spintronic devices. In this talk, I will discuss about our recent effort to develop machine-learning (ML) models for 2D AFMs. More specifically, how ML models together with molecular dynamics simulations can be effectively used to capture surface reconstructions in topological 2D AFMs [1]. Additionally, I will show how ML models can be employed to compute thermal properties and heat transport in 2D MnX and Janus XMnY (X,Y=S, Se, Te). Finally, I will talk about potential new avenues which can be explored combining first-principles calculations and ML models.

[1] S. Sattar, D. Hedman and C. M. Canali, Phys. Rev. Research (2025).

When? Friday April 4th 14-16
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/aht4pqfi4XWv76PK6 by April 2nd (especially important if you plan on attending onsite so we have fika for everyone)

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Data-driven Community-based Business Models for Forestry: Friends and Foes – Samin Ghalandarzadeha, Södra
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Generalisation of GenAI and Verification pipelines – Nemi Pelgrom Fortnox
15.50 -16.00 Sum up and plan for our seminars in May

Abstracts

Data-driven Community-based Business Models for Forestry: Friends and Foes – Samin Ghalandarzadeha, Södra

Building on our recent systematic literature review of the challenges and opportunities in data-driven and community-based business models for agriculture and forestry , this study will explore key findings and will seek to bridge the gap between theory and practice by engaging experts from a major Swedish community-based forestry association.

Through interviews with industry specialists, this study will uncover new opportunities and challenges for implementation of the abovementioned business models, as well as test these evidence-based findings. Ultimately, this research will assess the feasibility of the proposed business models, identify context-specific challenges and benefits, and strengthen the theoretical framework with real-world insights.

Generalisation of GenAI and Verification pipelines – Nemi Pelgrom Fortnox

Based on the publication of AlphaGeometry a little over a year ago, a new development in the strive towards trustworthy AI is gaining popularity; to combine generative models with automatic verification tools, as separate parts of frameworks or information pipelines. Many formats of information pipelines have been well researched before Generative AI joined the picture, but the difficulty in interpreting GenAI models into the languages (terminologies) used by those fields, makes it hard for researchers to interpret what previous results are relevant in these new contexts. In this presentation I will propose a formal terminology for describing this kind of pipeline, which may be used as guidance for how to interpret the validity, or trustworthiness, of any pipeline produced that fulfils the relevant criteria.

When? Friday March 21 14-16
Where? Onsite: D2272 and via zoom
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/XmL6bguq3T4Lax71A by March 19th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Immersive Analytics for Understanding Ecosystem Services Data – Benjamin Powley
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion Bridging Theory and Practice: AI-Driven Insights into Manufacturing Evolution and Industrial Maintenance Innovation – Muntaser Nuttah
15.50 -16.00 Sum up and plan for the April seminar

Abstracts

Immersive Analytics for Understanding Ecosystem Services Data – Benjamin Powley
When planning land use decisions, the input from experts in various domains is often required when making the decision. Ecosystem services analysis is often performed by expert analysts to estimate the effect of land use changes on the environment. For example, farming provides the benefit of agricultural productivity, but can negatively impact ecosystem services by reducing biodiversity, or increasing the amount of nitrogen in waterways.

In this talk, immersive VR visualization system, Immersive ESS Visualizer, is presented. The visualization system was designed for the comparison of multiple ecosystem services across different land use change scenarios. A user study was performed to evaluate the effectiveness of Immersive ESS Visualizer for ecosystem services analysis tasks compared to existing media (paper maps, and PDF’s on a 2D screen). The results of the user study will be discussed.

Bridging Theory and Practice: AI-Driven Insights into Manufacturing Evolution and Industrial Maintenance Innovation – Muntaser Nuttah
“In today’s industrial landscape, artificial intelligence (AI) is critical for transforming data into actionable knowledge. This talk highlights two innovative studies that leverage AI to decode complex unstructured datasets. The first study employs Natural Language Processing (NLP), Large Language Models, and Dynamic Topic Modeling to conduct a large-scale review of over 35,000 publications in manufacturing digitalization and automation from 1970 to 2023. This approach not only structures a fragmented body of knowledge but also tracks thematic evolutions—from early simulation and scheduling studies to emerging trends in energy efficiency, composite materials, cybersecurity, robotics, and AI—offering empirical support to creative destruction and technological paradigm theories. Similarly, the second study transitions to practical application, demonstrating how NLP-driven text mining could be used to deal with unstructured maintenance logs, claims, and work orders from Volvo CE. By converting raw text into structured insights, the framework enables proactive maintenance planning, system optimization, and knowledge transfer—showcasing AI’s capacity to bridge data volume and expert interpretation in industrial settings.”