Sensing Archaeology in the North: The Use of Non-Destructive Geophysical and Remote Sensing Methods in Archaeology in Scandinavian and North Atlantic Territories

In August 2018, a group of experts working with terrestrial/marine geophysics and remote sensing methods to explore archaeological sites in Denmark, Finland, Norway, Scotland and Sweden gathered together for the first time at the Workshop ‘Sensing Archaeology in The North’. The goal was to exchange experiences, discuss challenges, and consider future directions for further developing these methods and strategies for their use in archaeology. After the event, this special journal issue was arranged to publish papers that are based on the workshop presentations, but also to incorporate work that is produced by other researchers in the field. This paper closes the special issue and further aims to provide current state-of-the-art for the methods represented by the workshop. Here, we introduce the aspects that inspired the organisation of the meeting, a summary of the 12 presentations and eight paper contributions, as well as a discussion about the main outcomes of the workshop roundtables, including the production of two searchable databases (online resources and equipment). We conclude with the position that the ‘North’, together with its unique cultural heritage and thriving research community, is at the forefront of good practice in the application and development of sensing methods in archaeological research and management. However, further method development is required, so we claim the support of funding bodies to back research efforts based on testing/experimental studies to: explore unknown survey environments and identify optimal survey conditions, as well as to monitor the preservation of archaeological remains, especially those that are at risk. It is demonstrated that remote sensing and geophysics not only have an important role in the safeguarding of archaeological sites from development and within prehistorical-historical research, but the methods can be especially useful in recording and monitoring the increased impact of climate change on sites in the North

Avancerad algoritmer för Ultra Höga Energetiska Kosmisk strålning detektion med EUSO-TA exprimentet

Cosmic rays at energies 10^18 eV and above are known as Ultra High Energy Cosmic Rays (UHECR). UHECR are charged particles that are accelerated by the biggest accelerators in our universe. Candidate accelerators generating these UHECR are super novas, black holes and neutron stars. But where and what these intergalactic accelerators is at large still unknown. One of the experiments in the forefront of research in this eld is JEM-EUSO, a planed space based telescope for detecting UHECR particles as they enter Earth's atmosphere. Made possible by the advances in photon detectors and light weighted Fresnel lenses. A ground based path nder experiment was carried out in 2015 called EUSO-TA to test the optics and photomultiplier technologies. When the UHECR enters the atmosphere it collides with the atoms generating a number of secondary particles which in turn interacts with other atoms in the atmosphere generating a cascade of secondary particles. These trails are known as Extensive Air Showers (EAS). Mostly electrons are generated and in turn they excites the nitrogen atoms in the atmosphere which generate a isotropic characteristic uorescence light. The JEM-EUSO telescope is designed to detect and measure the photon ux. From the photon ux it will be able to estimate the energy of the initial UHECR. JEM-EUSO will cover the largest area of EAS search and increase statistics of UHECR data. This thesis describes the method and development of algorithms made for EAS analysis and detection based on EUSO-TA data. A simulation of EUSO-TA focal surface was developed, simulating background, stars and EAS. The algorithms developed involves a background subtracting lter, line detection using Hough transform and a neural network for decision making. The Hough transform is used in computer vision and is a method used to detect lines in the pictures. It successfully identi ed both simulated and captured UHECR incoming direction with small errors. Neural network are a machine learning method used classi cation and regression problems. With the use of know example data simulated or real captured data a neural network can without explicit programing it, adjust its parameters to t the data. Based on method called supervised learning. The algorithms was programed in Python and using ROOT software to build the neural network. The resulting algorithm was able to successfully detect simulated data. Test on the EUSO-TA captured data shows a promising result but has to be developed and tested further

Varför misslyckas IT-projekt?

Tillgänglig statistik visar att IT-projekt misslyckas i större omfattning än projekt i andra branscher. Denna trend har länge existerat och man har dokumenterat misslyckanden iutvecklingsprojekt ända tillbaka till början av 90-talet. Varför fortsätter detta vara ett problem än idag, trots allt arbete som lagts ner på att utforma nya projektstyrningsmodeller,utvecklingsmetoder och certifierande utbildningar? Denna studie undersöker IT-projekt och tar fram de nyckelfaktorer som orsakar att IT-projektmisslyckas. I uppsatsen utförs en flerfallsstudie där fyra seniora IT-projektledare får dela medsig av sin erfarenhet och kompetens inom området. Materialet samlas in via intervjuer och analyseras kvalitativt där de mest förekommande faktorerna analyseras i detalj. Med utgångspunkt från projektledarnas perspektiv behandlas vad som avses med ett misslyckat IT-projekt samt även vilka faktorer som de anser är viktigast att jobba med i IT-projekt.Uppsatsens innehåll är relevant för de personer som arbetar med IT-projekt, de som utvecklarsamt de som beställer. Dessa grupper behöver ha uppsatsens resultat i åtanke för att möjliggöra en bättre leverans. Resultatet indikerar att kommunikation är en kritisk faktor i IT-projekt.Available statistics indicate that IT projects fail on a larger scale than projects in other industries. This trend has existed for an extended period and there is documentation dating back to the early 90s that indicate this problem. Why do this continue to be a problem today, despite all the work that has been done to develop new project management models, system development models and certification courses? This study examines IT-projects and highlights the key factors that causes IT-projects to fail. In this paper a multi case study is conducted in which four senior IT-project managers shares their experience and expertise in the field. The material is gathered through interview and is analyzed qualitatively where the most common factors are analyzed in detail. Based on the project managers’ perspective it is established what constitutes a failed IT-project and also what factors they consider most important to work with. The content of this paper relevant to people working with IT-projects, both the developers and those who order the project. These groups need to have the results of the thesis in mind to allow for better delivery in the future. The result of this paper indicates that communication is a critical factor in IT-projects

Varför misslyckas IT-projekt?

Tillgänglig statistik visar att IT-projekt misslyckas i större omfattning än projekt i andra branscher. Denna trend har länge existerat och man har dokumenterat misslyckanden iutvecklingsprojekt ända tillbaka till början av 90-talet. Varför fortsätter detta vara ett problem än idag, trots allt arbete som lagts ner på att utforma nya projektstyrningsmodeller,utvecklingsmetoder och certifierande utbildningar? Denna studie undersöker IT-projekt och tar fram de nyckelfaktorer som orsakar att IT-projektmisslyckas. I uppsatsen utförs en flerfallsstudie där fyra seniora IT-projektledare får dela medsig av sin erfarenhet och kompetens inom området. Materialet samlas in via intervjuer och analyseras kvalitativt där de mest förekommande faktorerna analyseras i detalj. Med utgångspunkt från projektledarnas perspektiv behandlas vad som avses med ett misslyckat IT-projekt samt även vilka faktorer som de anser är viktigast att jobba med i IT-projekt.Uppsatsens innehåll är relevant för de personer som arbetar med IT-projekt, de som utvecklarsamt de som beställer. Dessa grupper behöver ha uppsatsens resultat i åtanke för att möjliggöra en bättre leverans. Resultatet indikerar att kommunikation är en kritisk faktor i IT-projekt.Available statistics indicate that IT projects fail on a larger scale than projects in other industries. This trend has existed for an extended period and there is documentation dating back to the early 90s that indicate this problem. Why do this continue to be a problem today, despite all the work that has been done to develop new project management models, system development models and certification courses? This study examines IT-projects and highlights the key factors that causes IT-projects to fail. In this paper a multi case study is conducted in which four senior IT-project managers shares their experience and expertise in the field. The material is gathered through interview and is analyzed qualitatively where the most common factors are analyzed in detail. Based on the project managers’ perspective it is established what constitutes a failed IT-project and also what factors they consider most important to work with. The content of this paper relevant to people working with IT-projects, both the developers and those who order the project. These groups need to have the results of the thesis in mind to allow for better delivery in the future. The result of this paper indicates that communication is a critical factor in IT-projects

Self-Interference Suppression in Full-Duplex MIMO Relays

Full-duplex relays can provide cost-effective cover-age extension and throughput enhancement. However, the main limiting factor is the resulting self-interference signal which deteriorates the relay performance. In this paper, we propose a novel technique for self-interference suppression in full-duplex Multiple-Input Multiple-Output (MIMO) relays. The relay employs transmit and receive weight filters for suppressing the self-interference signal. Unlike existing techniques that are based on zero forcing of self-interference, we aim at maximizing the ratio between the power of the useful signal to the self-interference power at the relay reception and transmission. Our simulation results show that the proposed algorithm outperforms the existing schemes since it can suppress interference substantially with less impact on the useful signal

Women’s perceptions and attitudes towards the use of AI in mammography in Sweden: a qualitative interview study

Background Understanding women’s perspectives can help to create an effective and acceptable artificial intelligence (AI) implementation for triaging mammograms, ensuring a high proportion of screening-detected cancer. This study aimed to explore Swedish women’s perceptions and attitudes towards the use of AI in mammography.Method Semistructured interviews were conducted with 16 women recruited in the spring of 2023 at Capio S:t Görans Hospital, Sweden, during an ongoing clinical trial of AI in screening (ScreenTrustCAD, NCT 04778670) with Philips equipment. The interview transcripts were analysed using inductive thematic content analysis.Results In general, women viewed AI as an excellent complementary tool to help radiologists in their decision-making, rather than a complete replacement of their expertise. To trust the AI, the women requested a thorough evaluation, transparency about AI usage in healthcare, and the involvement of a radiologist in the assessment. They would rather be more worried because of being called in more often for scans than risk having overlooked a sign of cancer. They expressed substantial trust in the healthcare system if the implementation of AI was to become a standard practice.Conclusion The findings suggest that the interviewed women, in general, hold a positive attitude towards the implementation of AI in mammography; nonetheless, they expect and demand more from an AI than a radiologist. Effective communication regarding the role and limitations of AI is crucial to ensure that patients understand the purpose and potential outcomes of AI-assisted healthcare

An artificial intelligence-based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening.

AIMS: Screening for atrial fibrillation (AF) is recommended in the European Society of Cardiology guidelines. Yields of detection can be low due to the paroxysmal nature of the disease. Prolonged heart rhythm monitoring might be needed to increase yield but can be cumbersome and expensive. The aim of this study was to observe the accuracy of an artificial intelligence (AI)-based network to predict paroxysmal AF from a normal sinus rhythm single-lead ECG. METHODS AND RESULTS: A convolutional neural network model was trained and evaluated using data from three AF screening studies. A total of 478 963 single-lead ECGs from 14 831 patients aged ≥65 years were included in the analysis. The training set included ECGs from 80% of participants in SAFER and STROKESTOP II. The remaining ECGs from 20% of participants in SAFER and STROKESTOP II together with all participants in STROKESTOP I were included in the test set. The accuracy was estimated using the area under the receiver operating characteristic curve (AUC). From a single timepoint ECG, the artificial intelligence-based algorithm predicted paroxysmal AF in the SAFER study with an AUC of 0.80 [confidence interval (CI) 0.78-0.83], which had a wide age range of 65-90+ years. Performance was lower in the age-homogenous groups in STROKESTOP I and STROKESTOP II (age range: 75-76 years), with AUCs of 0.62 (CI 0.61-0.64) and 0.62 (CI 0.58-0.65), respectively. CONCLUSION: An artificial intelligence-enabled network has the ability to predict AF from a sinus rhythm single-lead ECG. Performance improves with a wider age distribution.The project was funded by Vinnova, Sweden’s innovation agency (grant to Zenicor Medical Systems AB). In addition, the project received funding by The Swedish Heart-Lung Foundation and CIMED. The study also received a research grant from The Swedish Research Council, Dnr 2022-01466. Emma Svennberg is supported by the Stockholm County Council (Clinical researcher appointment). The SAFER Study was funded by the National Institute for Health Research (NIHR), grant number RP-PG- 0217-20007 and by the NIHR School for Primary Care Research