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This master thesis investigates how a manufacturing company develops and implements packaging solutions for sustainable development, using ASSA ABLOY as a case study. Focusing on the sliding door operator produced at a manufacturing plant in Eastern Europe, the study explores the environmental and economic impacts of packaging throughout its life cycle and the internal packaging innovation process
The purpose of this research is to determine how social media influencers drive the pre-purchase phase of the consumer’s decision-making process and how these drivers differ in regard to hedonic and utilitarian products. Considering the aim to answer how the pre-purchase phase is driven through social media influencers’, and how the drivers differ for hedonic and utilitarian products, a qualitati
Today, the overwhelming majority of Internet of Things (IoT) and mobile edge devices have extreme resource limitations, e.g., in terms of computing, memory, and energy. As a result, training Deep Neural Networks (DNNs) using the complex Backpropagation (BP) algorithm on such edge devices presents a major challenge. Forward-only algorithms have emerged as more computation- and memory-efficient alte
Objective: The aim of this study is to describe how the use of diagnostic imagingfor prostate cancer (PCa) has evolved over time and to determine whether thereare any differences in access to diagnostic imaging, type of cancers detected, andmortality based on the education level of patients.Methods: 11,063 men were recruited between 1991 and 1996 and thenprospectively followed until 2020. All new
Machine Learning (ML) offers many opportunities, but its reliance on personal data raises privacy concerns. One such example is the Membership Inference Attack (MIA), which aims to determine whether a specific data point was part of a model’s training dataset. In this paper, we investigate this attack on Random Forests (RFs) and propose a method to quantify their vulnerability to MIA. We also demo
Vision impairment increases risks such as social isolation, mobility challenges, and falls. Wearable Augmented Reality (AR) devices with Artificial Intelligence (AI) can enhance sensory perception by enabling real-time recognition of obstacles, assisting visually impaired individuals during street navigation, aiming to reduce the risk of falls. In this paper, we propose a Federated Learning (FL) f
Robotic systems often face execution failures due to unexpected obstacles, sensor errors, or environmental changes. Traditional failure recovery methods rely on predefined strategies or human intervention, making them less adaptable. This paper presents a unified failure recovery framework that combines Vision-Language Models (VLMs), a reactive planner, and Behavior Trees (BTs) to enable real-time
The Australian Bogong moth (Agrotis infusa) is a small noctuid moth that undertakes annual, nocturnal migrations of up to 1000 km to escape the summer heat of its breeding grounds. The moths travel to cool alpine caves, where they enter a dormant state (aestivation) before returning to reproduce and die. During migration, their brains integrate magnetic and visual cues to guide their flight direct
Organic pollutants in aquatic environments pose a threat to ecosystems and human health, a challenge that requires urgent resolution. This urgent threat highlights the need for the development of efficient technologies for pollutant detection and degradation in environmental protection. This study presents the successful fabrication of a composite nanomaterial consisting of Ag nanoparticle-decorat
Photo-induced dynamics of electronic processes are driven by the coupling between electronic and nuclear degrees of freedom. Here, we construct one- and two-dimensional organic-inorganic tin halides to investigate how dimensionality controls exciton-phonon coupling and exciton self-trapping. The results show that a one-dimensional system has strong exciton-phonon coupling leading to excitation-ind
Action-detected two-dimensional electronic spectroscopy (A-2DES) provides valuable insights into ultrafast dynamics within functional materials and devices by measuring incoherent signals like photocurrent. This work details the implementation and optimization of a pulse-shaper-based A-2DES setup, focusing on methodological strategies crucial for acquiring high-fidelity data. We present a comprehe
BACKGROUND: Knee osteoarthritis (OA) is a major public health issue causing chronic pain, impaired physical function, and reduced quality of life. As there is no cure, self-management of symptoms via exercise is recommended by all current international clinical guidelines. This review updates one published in 2015.OBJECTIVES: We aimed to assess the effects of land-based exercise for people with kn
We present an Unmanned Aerial Vehicle (UAV) human-robot interaction simulation environment to evaluate operator situational awareness (SA) during Beyond Visual Line of Sight (BVLOS) operations. We have developed a modular simulator that functions as a testbed for the design of custom scenarios and the study of human-robot interaction. This simulator facilitates data collection and allows for the c
Objective: Individuals with psychiatric illness suffer from poorer physical health compared with the general population and have a higher risk of developing cardiovascular and metabolic diseases. This cross-sectional study aims to describe the prevalence of lifestyle and cardiovascular risk factors and the association with self-reported psychiatric symptoms in a population of 40-year-old individua
This work investigates lightweight personalisation of micro-electro-mechanical systems (MEMS)-based wearables using a public padel database as a case study. We compare a centralised CNN model, single-user models and two fine-tuning schemes (full and last-layer) on wrist-worn IMU data from 23 players and 13 stroke classes. Personalised models with data augmentation achieve weighted F1-scores above
The training of the state-of-the-art Deep Neural Networks (DNNs) consumes massive amounts of energy, while the human brain learns new tasks with remarkable efficiency. Currently, the training of DNNs relies almost exclusively on Backpropagation (BP). However, BP faces criticism due to its biologically implausible nature, underscoring the significant disparity in performance and energy efficiency b
Background Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition influenced by both genetic and environmental factors. Prenatal and perinatal exposures have been implicated in ASD etiology, but their influence may vary across clinical subgroups, including subgroups defined by co-occurring intellectual disability (ID). Methods We conducted a population-based case–control st
Objective The basis for protein synthesis is the genetic code. Many of these proteins will affect intermediary metabolites by acting as enzymes, hormones, or by other actions. The aim of the present study was to assess the relationships of a large number of proteins with endogenous metabolites. Methods Plasma protein levels were measured by the proximity extension assay (PEA) and metabolites by ma
Skew Laplace distributions, which naturally arise in connection with random summation and quantile regression settings, offer an attractive and flexible alternative to the normal (Gaussian) distribution in a variety of settings where the assumptions of symmetry and short tail are too restrictive. The growing popularity of the Laplacebased models in recent years is due to their fundamental properti
Neoadjuvant chemotherapy (NACT) is offered to an increasing number of breast cancer (BC) patients, and comprehensive monitoring of treatment response is of utmost importance. Several imaging modalities are available to follow tumor response, although likely to provide different clinical information. We aimed to examine the association between early radiological response by three conventional imagi
