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Academic skills such as reading, writing, critical thinking and information literacy are foundational to university-level learning. However, although students often have a certain level of academic literacy on arrival, their skills do not necessarily match the context-specific requirements of higher education and often need to be explicitly taught. In contrast to
Sparse tensor computations suffer from irregular memory access patterns that degrade cache performance. While software prefetching can mitigate this, existing compiler approaches lack the semantic insight needed for effective optimization. We present ASaP, an automatic software prefetching framework integrated within MLIR’s sparse tensor dialect. By leveraging semantic information-tensor formats a
The downstream process of monoclonal antibodies (mAbs) is expensive and significantly contributes to overall manufacturing costs. One primary reason is the extensive consumption of water and chemicals required for preparing large volumes of various buffers, essential for multiple chromatography and filtration steps. Reducing the water consumption in biopharmaceutical processes is critical to drive
Background and purpose: Neuroblastoma is the most common extracranial solid tumour in children. Radiotherapy is commonly part of the multimodal treatment for high-risk patients. The aim of this study is to analyse doses to organs at risk (OAR) in comparative proton and photon treatment plans for children treated for neuroblastoma and report side effects. Patient/material and methods: All children
Nature-based Solutions (NBS) are recognized as key actions for achieving sustainable and resilient development. They are being implemented in a variety of fields, such as urban planning, and offer a wide range of social, environmental, and economic benefits. While there has been a significant increase in the number of NBS practices in recent years, there remains a notable imbalance, with a bias to
Predicting live birth after the first IVF/ICSI treatment is challenging, as many factors may interact to affect IVF/ICSI outcomes. Adenomyosis is one factor that impacts live birth rates. Machine learning algorithms have been shown valuable for detecting complex dependencies and predicting outcomes in different clinical settings. We aimed to develop a prediction model for live birth after IVF/ICSI
Background: Surgery in select individuals with inflammatory bowel disease (IBD) may obviate the need for future IBD-related treatment. Aims: To characterise individuals who remain treatment-free during the first 5 years after initial IBD-related surgery. Methods: We performed a nationwide cohort study using the Swedish National Patient Register and the ESPRESSO histopathology to identify individua
Purpose: High-risk colon cancer may benefit from preoperative chemotherapy (preCHT), but evidence on its short-term safety and outcome is limited. Population-based evidence before its incorporation into national guidelines is lacking. Methods: Patients with final weighted stage II–III colon cancer undergoing elective resection between 2007 and 2017 were identified in the Swedish Colorectal Cancer
Environmental Building Certification (EBC) assessments, involving modelling, simulation, and grading calculation, are often time-consuming, prompting a need for streamlining. This investigation assesses existing Building Energy Modelling (BEM) workflows for Swedish context of EBC, focusing on extracting building geometry from Autodesk Revit with minimal information losses, and considering both low
Background: Surgical resection of post-chemotherapy residual masses for metastatic seminoma is discussed controversially with regard to oncological and functional outcome. Furthermore, the role of FDG-PET/CT to detect vital seminoma is still unclear. It is the aim of this study is to report the outcomes of patients with pure seminoma who underwent PC-RPLND. Methods: In this retrospective multi-ins
Dynamic task offloading is essential for real-time robotic applications, enabling them to adapt to fluctuating computational demands and maintain efficiency under changing conditions. This paper introduces a dynamic task offloading framework that incorporates monitoring, decision making, offloading triggering, and performance monitoring to optimize resource usage by offloading real-time tasks to e
The chemical industry, essential to economic growth and innovation, faces critical environmental sustainability challenges, notably pollution, emissions, and intensive resource use. Motivated by the need for practical tools to address these issues, this research develops a structured, comprehensive framework to assess and enhance environmental sustainability within the chemical sector. The framewo
Closed-form conditions for bound states in the continuum are derived for planar, cylindrical, and spherical cavities enclosed by metasurfaces with electric and magnetic response. While the combined response in lossless systems enables field confinement in arbitrarily small cavities, the gain-enabled bound states in the continuum are found to coalesce with a laser mode, leading to intriguing single
Given the increasing number of heterogeneous data stored in relational databases, file systems or cloud environment, it needs to be easily accessed and semantically connected for further data analytic. The potential of data federation is largely untapped, this paper presents an interactive data federation system (https://vimeo.com/319473546) by applying large-scale techniques including heterogeneo
The illicit trafficking of cultural heritage represents a significant global challenge, exacerbated by digital technologies that facilitate unauthorised trade while offering new protection opportunities. The ANCHISE project addresses these challenges through a suite of six innovative, complementary tools designed to enhance the detection, prevention, and investigation of cultural property traffick
Electric power is the foundation of modern society, yet Europe is currently facing an energy crisis, increasing interest in power generation, energy infrastructure, and grid resilience. However, power plant data are complex and multidimensional, making it difficult to gain an overview or understanding. Visualization methods can help to reduce cognitive load and facilitate exploration of such data.
This paper contributes to semantic representation of human demonstrated actions for robot execution of time constrained tasks. We propose a semantic action encoding method based on interactions between the subject and objects in the scene. Our semantic framework is enriched with a descriptive spatial reasoning method which leads to accurate segmentation and recognition of unique action primitives.
Background People with Parkinson’s disease (PD) need life-long rehabilitative interventions to slow disease progression and reduce impact of the disease on daily life activities. To be sustainable for the individual, activities should have a positive impact on physical, cognitive and mental health, and should be appealing and meaningful. Aim To quantitatively evaluate the health-related effects of
Rapidly evolving academic and professional environments require essential skills such as communication, collaboration, creativity and critical thinking, which are not typically learned and assessed explicitly in formal education. These skills encompass a significant part of the competencies and are increasingly valued in an AI-driven workforce because they cannot be easily replicated by machines.
